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Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis of Web Behavior

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30 October 2023

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31 October 2023

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Abstract
During virus outbreaks in the recent past web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on October 4, 2023, with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on October 3, 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on October 4, 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies (in the context of this conspiracy theory) was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of this conspiracy theory) on Google on October 4, 2023. Finally, the correlation between zombie-related searches (in the context of this conspiracy theory) in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.
Keywords: 
Subject: Public Health and Healthcare  -   Public Health and Health Services

1. Introduction

The 2023 outbreak of the Marburg Virus Disease (MVD) was officially declared by the Ministry of Health and Social Welfare of Equatorial Guinea on February 13, 2023. This declaration followed the reporting of suspected fatalities caused by viral hemorrhagic fever from January 7, 2023, to February 7, 2023, and a positive RT-PCR case for Marburg virus on February 12, 2023, at the Institut Pasteur de Dakar in Senegal [1]. Between February 13, 2023, and June 7, 2023, 17 confirmed cases and 23 suspected cases were documented in the continental area of Equatorial Guinea. A total of 12 individuals among the confirmed cases succumbed to the illness, while all of the likely cases were reported as fatalities. It is worth noting that the case-fatality ratio among the confirmed cases of this MVD outbreak was 75% (omitting one confirmed case for which the outcome was not known). The most recently confirmed patient was released from a Marburg treatment center in the Bata area of Litoral province on April 26, 2023, after the administration of two successive negative PCR tests for MVD. The Ministry of Health of Equatorial Guinea officially declared the conclusion of the outbreak on June 8, 2023, after a period of 42 days including two successive incubation periods during which no new confirmed cases were recorded [2].
As a result of this outbreak and the high fatality rate of MVD [3], in the last few months people from all over the world have been spending a lot more time than ever before on social media platforms and the internet in general to seek, share, access, and disseminate information about MVD [4,5]. During virus outbreaks of the past such as COVID-19 [6,7], MPox [8,9], Ebola [10,11], H1N1 [12,13], and MERS [14,15], researchers from different disciplines such as Healthcare, Epidemiology, Big Data, Data Analysis, Data Science, and Computer Science have studied and analyzed the underlining web behavior as web behavior provides insights into the public health needs, interests, motives, concerns, perspectives, and opinions related to virus outbreaks. Furthermore, web behavior analysis related to a virus outbreak has also had several applications related to the real-time surveillance of outbreaks [16], prediction of cases [17], forecasting the behavior of different strains of a virus [18], timely preparation of public health policies [19], better preparedness of healthcare systems [20], and timely implementation of public health policies and guidelines [21]. In addition to this, during virus outbreaks of the recent past, for example, COVID-19, such paradigms of information-seeking and sharing behavior on the internet led to the development and dissemination of different conspiracy theories which led to a range of reactions, both positive and negative, in the general public [22]. An example of a conspiracy theory in the context of COVID-19 was related to the role of 5G towers in spreading of the COVID-19 [23]. In January 2020, this conspiracy theory started on social media and it soon gained unprecedented attention, leading to a surge in Google Searches related to 5G and COVID-19 around that time. Furthermore, the rapid dissemination of this conspiracy theory led to people burning 5G towers in different regions in the United Kingdom [24]. Researchers from different disciplines have also investigated such patterns of web behavior related to the conspiracy theories associated with virus outbreaks of the past [25,26,27]. The recent outbreak of MVD followed by a warning signal (for testing purposes) sent by the United States Federal Emergency Management Agency (FEMA) to every TV, radio, and cellphone in the U.S. on October 4, 2023, led to an unusual conspiracy theory involving the MVD and zombies. One post about this conspiracy theory on Twitter [28], viewed close to 11 million times at the time of writing of this paper, states – “Turn off your cell phones on October 4th. The EBS is going to "test" the system using 5G. This will activate the Marburg virus in people who have been vaccinated. And sadly turn some of them into zombies”. In the past, there have been examples where just one Tweet started a conspiracy theory [29]. Since the publication of this Tweet, there have been several other posts on Twitter associated with this conspiracy theory which reveal the views, opinions, reactions, responses, and concerns of the general public in this regard. This conspiracy theory created a buzz in the global population to the extent that “Marburg Virus” featured in the list of the top trending topics on Twitter on October 3, 2023 [30]. To add to this, “Emergency Alert System” and “Zombie” featured in the list of top trending topics on Twitter on October 4, 2023 [31]. As a result of the widespread nature of this conspiracy theory and the associated concern and public reactions, Jeremy Edwards (press secretary and deputy director of public affairs at FEMA) stated publicly – “I received it on my phone and saw it on the TV. And I can confirm to you that I am not a zombie”, soon after the broadcast of the FEMA emergency alert signal [32]. In view of this recent outbreak of MVD and the associated conspiracy theory that created a significant buzz on the internet, which included this conspiracy theory being amongst the trending topics on Twitter for 2 days – October 3, 2023, and October 4, 2023, modeling and analyzing the underlining patterns of web behavior of the general public in this context becomes highly crucial to investigate. This serves as the main motivation for this research work.

1.1. Marburg Virus: A Brief Overview

In August of 1967, thirty people in Marburg and Frankfurt, Germany, became mysteriously and dangerously ill. This was the first known outbreak of MVD [33]. The virus was traced to African green monkeys that had previously been imported from Uganda. Infection occurred when autopsies were performed on the monkeys for the purpose of collecting kidney cell samples [33]. When the Ebola virus (EBOV) emerged in Africa nearly ten years later, in 1976, the two viruses were classified together as Filoviridae [33,34]. MVD appeared sporadically between the years 1975 and 1985, but it did not result in deaths the way that EBOV did, leading people to believe that MVD was not as deadly [35]. Though MVD is not a prevailing threat in endemic locations, it poses a threat to tourists or travelers, especially as they might bring the virus to other countries; the risk of infection also exists in laboratory workers [36]. Because of its danger, transmissibility, and lack of vaccine, the World Health Organization (WHO) categorizes MARV as the Risk 4 Group (RG4). RG4 is the highest risk group and defines pathogens as a serious risk to individuals and communities [37]. The MVD infection manifests as a zoonotic disease, but the original or natural host of the virus is yet to be identified [38,39,40]. However, researchers speculate that bats could be vital to the transmission of the disease, or they may also be the original carriers of MVD [41]. In fact, MVD was isolated from Egyptian fruit bats after the initial outbreak [42,43,44,45,46]. Research works involving the Gabonese bat populations suggest that MVD is enzootic, and its transmissibility poses a risk of appearing in other countries [47,48]. Transmission between humans usually occurs through bodily fluids such as blood, saliva, and urine. Such interactions tend to happen when caring for a sick patient but can include the handling of an infected corpse [49].
The disease is observed over three phases: generalization, early organ, and late organ or convalescence [50]. During the generalization phase, the patient usually displays symptoms similar to the flu. During the second phase, which occurs between days five to thirteen of the illness, patients may display psychological symptoms. This may manifest as general confusion and irritability but could also be seen as worse symptoms, like swelling of the brain and delirium. The last stage of the disease is either late organ or convalescence depending on if the patient is able to recover. Should a patient enter the late organ stage, they may experience dementia, a coma, or convulsions. Death usually comes about by shock from multiorgan failure. The convalescence phase is marked by a slow recovery with symptoms like muscle pain, exhaustion, and peeling of the skin where the rash appeared [50]. Nearly 600 MVD cases have been reported since the first outbreak. These recent cases of Marburg disease have catalyzed the creation of MARVAC, a WHO-coordinated cooperative aimed at tackling the Marburg vaccine [51,52]. The vaccine has since been under development through the use of the MVD glycoprotein and animal testing [53]. Of approved vaccines, Ad26-MARV, developed using the Ad26 vector encoding of MVD, is set to be moved forward in development. It is currently available for emergency use alongside another Ad-based vaccine, ChAd3-MARV, which takes the Ad vector from a chimpanzee. Several vesicular stomatitis virus-based vaccines are scheduled to advance to clinical testing after manufacturing, namely VSV-N4CTI-MARV, VSV-MARV Musoke vector, and VSV-MARV Angola vector [53].

1.2 Concept of Conspiracy Theories

A conspiracy theory is an explanation for an event or occurrence that typically cites outgroups or authority powers as the perpetrators. Douglas et al. [54] proposed that people believe in conspiracy theories due to three key psychological motives: knowledge, existential, and social. Knowledge refers to certainty and the desire to create patterns or fill gaps in understanding. Existential motives include exerting control or safety in one’s own situation, and knowledge allows people to have the certainty to feel safe. Lastly, social motives may be a person’s desire to fit into a group, and following conspiracy theories may provide them with the agency to look good or feel desirable in social settings.
In addition to the core psychological motives, conspiracy theories can appeal to certain demographics [55,56]. People who are more likely to believe in conspiracy theories tend to include those with lower levels of education, lower levels of income, weak social networks, and low media literacy [57,58,59,60]. Males, unmarried people, and unemployed people are also seen to have a higher belief in conspiracy theories [59]. A final reason why people might believe in conspiracy theories could be attributed to politics. Politically motivated conspiracy theories give people of a particular party the reasoning needed to further a point, argument, or campaign, regardless of whether the content is true or not [55]. Conspiracy theories tend to have largely negative social and/or psychological impacts [61]. Research shows that people who participate in conspiracy theory dialogue are less likely to vote or participate in politics in general due to a lack of trust in the political system [62,63,64]. Conspiracy theories can also be associated with prejudiced views of certain groups of people. Research into conspiracy theories suggests that said conspiracy theories can portend anti-Semitic beliefs, discrimination against Jewish people, and sometimes even racism towards groups who are not a part of the conspiracy theory at all. Such sentiments contribute to and exacerbate division between groups of people [65,66,67].
One of the more significant impacts of conspiracy theories may be scientific skepticism. Climate change, for example, is commonly the target of many conspiracy theories, driving people away from caring about the core issue [68]. Those who might believe in climate change conspiracy theories may also believe in theories that surround scientific evidence, like GMOs or the forensics of the 9/11 attacks [69,70]. Scientific skepticism of this nature can extend to issues of human health as well. Belief in anti-science theories correlates to unsafe health choices, like being anti-vaccines (especially the COVID-19 vaccine), not using contraceptives, or alternative medicinal practices, or refusing professional help for physical or mental illnesses [71,72,73,74,75,76,77]. Conspiracy theories surrounding COVID-19 specifically contributed to an unwillingness to comply with COVID-19 regulations [78,79]. The insights into why people believe in conspiracy theories may play a role in how they are transmitted as well. People generally only believe in conspiracy theories after learning about them, and they may come across them in certain political spheres. Prior works in this field have found that political agendas could be furthered by conspiracy theories, making people who fall into particular political categories more inclined to share conspiracy theories [80,81,82,83]. Conspiracy theories may also be used to generate doubt in mainstream politics and media [84]. Research work in this field has shown that people commonly avoid sharing conspiracy theories out of fear of ostracization. However, the involvement in politics and feelings generated by it may be so strong that it negates this fear anyway. This may further indicate how conspiracy theorists find community among each other [85].
The remainder of the paper is presented as follows. A review of recent works in this field is presented in Section 2. Section 3 presents the detailed methodology that was followed for the investigation, interpretation, and analysis of the underlying web-behavior. The results are presented and discussed in Section 4, which is followed by the conclusion.

2. Literature Review

A review of recent works related to web behavior investigation, interpretation, and analysis during recent virus outbreaks is presented in this section. This section is divided into three parts. Section 2.1 presents a review of recent works related to time series forecasting in the context of recent virus outbreaks such as COVID-19 and MPox as time series forecasting approaches have been popular in the last few years for modeling web behavior. Section 2.2 presents a review of various conspiracy theories that were associated with virus outbreaks in the recent past. Section 2.3 presents an overview of healthcare research based on web behavior analysis from Google Trends as Google Trends is the most popular platform for web behavior analysis [86] and it was used for data collection in this research project.

2.1. Review of Recent Works related to Time Series Forecasting in the Context of Recent Virus Outbreaks Such as COVID-19 and MPox

To predict the spread of COVID-19, Shahid et al. [87] used Auto Regressive Integrated Moving Average model (ARIMA), support vector regression (SVR), long short-term memory (LSTM), and bidirectional long-short term memory (Bi-LSTM). They found that Bi-LSTM outperformed the rest when trying to predict cases of COVID-19. In a similar study, Chandra et al. [88] found that different types of LSTM models could be used to predict COVID-19 with high levels of accuracy. They used LSTM, Bi-LSTM, and encoder-decoder LSTM (ED-LSTM) to predict cases. While ED-LSTM tended to underperform compared to LSTM and Bi-LSTM models, it performed at the highest accuracy with static-split training. Alabduldrazzaq et al. [89] also used ARIMA in their study. Their work used cases in Kuwait and resulted in a correlation coefficient of 0.996, indicating that ARIMA was a strong contender for the best prediction model. In a study performed in India, the authors used ARMIA to predict where COVID-19 infections might occur [90]. Using data from Johns Hopkins University, they were able to accurately predict COVID-19 cases. Katoch et al. [91] used ARIMA modeling to devise numbers for the COVID-19 outbreak during the time of January 30, 2020, to September 16, 2020, in India. A study done in Brazil found that ARIMA models successfully predicted cases in Recife, contributing to the prevention effort [92]. In Slovakia, a spatiotemporal analysis was used to analyze the spread of COVID-19 [93]. Spatial autocorrelation was used to view cases across Slovakian districts, and data was synthesized with Moran’s global autocorrelation index and local index. A similar study was done in Lebanon. Spatial autocorrelation was used with certain parameters to analyze COVID-19 cases across Lebanese districts, and El Deeb [94] found that geographic bordering, resident population, density, distance between district centers, and poverty density correlated to disease clustering and spread.
The work of Iftikhar et al. [95] focused on forecasting new cases and death counts related to the MPV virus using a hybrid forecasting system that combined time series and stochastic models. Long et al. [96] worked on addressing the global health concern during the MPox outbreak, particularly in the United States, and utilized machine learning models for short-term forecasting. Among the models tested, NeuralProphet emerged as the most efficient, achieving a low RMSE and high accuracy in predicting future cases. The work of Wei et al. [97] addressed the increasing prevalence of MPox cases in non-endemic countries, particularly in North America and Europe since May 2022. The researchers employed various forecasting models, such as ARIMA, exponential smoothing, LSTM, and GM(1,1), to predict daily cumulative confirmed MPox cases in different regions.
Priyadarshini et al. [98] used linear regression, decision trees, random forests, elastic net regression, ANN, and CNN to assess the spread of the MPox virus across different countries. The results indicated that CNNs performed the best in modeling the virus's spread, while time-series analysis using ARIMA and SARIMA models provided valuable insights for risk assessment and preventive measures. Pathan et al. [99] used a deep learning-based LSTM model to analyze the gene mutation rate of the MPox virus. The work of Eid et al. [100] introduced a novel approach called BER-LSTM, which optimized LSTM deep networks using the Al-Biruni Earth Radius (BER) algorithm to predict MPox cases accurately. Patwary et al. [101] examined the global spread of MPox using concepts of GIS technology and spatial data analysis. Du et al. [102] examined online search activity related to the MPox outbreak in China. The findings showed that regions with higher economic levels, particularly Beijing and Shanghai, exhibited more interest in MPox.
To summarize, these works have used time series forecasting models such as ARIMA, Autocorrelation, and LSTM, to analyze web behavior, internet activity, and related information during virus outbreaks in the recent past. However, none of these works have focused on applying any such models related to the recent surge in web behavior related to the 2023 MVD outbreak.

2.2. Review of various Conspiracy Theories that were associated with Virus Outbreaks in the Recent Past

The COVID-19 pandemic was plagued by the proliferation of conspiracy theories and false information. These encompass claims suggesting that COVID-19 was a fabrication, insinuations that the virus was artificially engineered and released as a bioweapon, and accusations of governments capitalizing on the crisis for anti-democratic purposes [103]. In the early stages of the pandemic, social media stories even propagated the idea that 5G technology was responsible for the spread of the virus [104]. Some conspiracy theories contended that the pandemic served as a guise for the clandestine injection of microchip quantum-dot spy software into individuals for surveillance purposes, gaining substantial traction on social media platforms [105]. Furthermore, there were assertions that COVID-19 testing, especially the use of nasopharyngeal swabs, could harm the blood-brain barrier or even infect individuals with the virus [106]. The conspiracy theories related to face masks included claims that masks could facilitate viral transmission or lead to oxygen deprivation and carbon dioxide poisoning [107]. Furthermore, misinformation extended to unverified therapies and remedies, encompassing homeopathic arsenic-based products, colloidal silver solutions, the use of high-dose vitamins as preventive measures, and various herbal remedies [108,109].
In general, conspiracy theories have the potential to have a significant negative impact. For example, false claims connecting 5G technology to the pandemic triggered attacks on telecommunication masts and subjected engineers to verbal and physical abuse in multiple countries, including the UK [110]. The repercussions of misinformation during infectious disease crises draw historical parallels, such as the HIV/AIDS pandemic, where denial of the virus's existence and the promotion of untested alternative solutions led to substantial public health concerns and loss of lives [111,112,113]. The findings from recent works indicate that belief in COVID-19 conspiracy theories was inversely related to adherence to health-protective behaviors and trust in guidance from public health experts [114,115]. In a comprehensive study of 82 hoaxes related to the 2023 MPox outbreak and their spread on social media, researchers found that the sources behind these hoaxes were mostly unknown (73.17%), making it challenging to identify the primary disinformants. In the remaining instances (26.83%), sources included figures with public notoriety (18.29%), fictitious sources (6.1%), and impersonated identities (2.44%). The predominant format of these hoaxes was a combination of image and text (39%), followed by primarily text-based hoaxes (36.6%) [116]. In a separate study analyzing conspiracy theories related to the MPox outbreak on TikTok, 153 videos were identified and analyzed. The most prevalent theme (46.4% of videos) asserted that MPox was a deliberately orchestrated pandemic introduced for power, control, or financial gain. A second category (33.3% of videos) revolved around vaccines, with content alleging that MPox was an excuse to mandate vaccines worldwide. To add to this, approximately 17.6% of videos claimed that the WHO was involved in the MPox outbreak to gain more power and potentially override national laws [117].
To summarize, these works show that virus outbreaks in the recent past have been associated with several conspiracy theories which have been investigated and analyzed by researchers from different disciplines. However, none of those works studied the emergence of the new conspiracy theory involving the MVD and the emergency alert signal sent by FEMA in the United States on October 4, 2023.

2.3. Review of Applications of Google Trends in Healthcare

Google Trends data has been of interest to researchers for the mining and analysis of the underlying web behavior related to various emerging technologies [118,119], global affairs [120,121], humanitarian issues [122,123], societal problems [124,125], and needs of different diversity groups [126,127]. In the last decade and a half, the utilization, applications, and use cases of Google Trends to mine, monitor, interpret, and analyze web behavior during epidemics, pandemics, and virus outbreaks have attracted a significant amount of attention from researchers from different disciplines. Ginsberg et al. [128] used Google trends to track influenza-like illness (ILI) for early detection and rapid response. By analyzing the relative frequency of specific queries, the authors accurately estimated ILI activity in various U.S. regions. Kapitány-Fövény et al. [129] utilized Google Trends to forecast the incidence of Lyme disease in Germany. The study spanned from 2013 to 2018, with data on Lyme disease incidence obtained from the Robert Koch Institute and Google search volumes for "Borreliose" in Germany. The authors applied a SARIMA model to the Lyme disease incidence time series and incorporated Google Trends data as an external regressor. The results showed that Google Trends data correlated well with reported Lyme disease incidence. Verma et al. [130] used Google Trends to predict disease outbreaks in India. The research explored the correlation between Google Trends data for diseases like malaria, dengue fever, chikungunya, and enteric fever in 2016 in Haryana and Chandigarh and IDSP data. The results show a strong temporal correlation between Google Trends data and the IDSP data, suggesting that Google Trends could be used as an early warning tool for disease outbreaks. The work of Young et al. [131] involved using Google Trends to predict weekly state-level cases of syphilis in the United States. By analyzing web behavior related to keywords associated with syphilis, the study aimed to determine whether such data could serve as a supplementary tool for monitoring and predicting syphilis outbreaks. Another work by Young et al. [132] involved using Google trends to forecast new HIV diagnosis cases in the United States. The study collected Google Trends search volume data for HIV-related keywords and combined it with state-level HIV case reports from the CDC. They developed a predictive model using a negative binomial approach and the Least Absolute Shrinkage and Selection Operator (LASSO) method. Morsy et al. [133] used Google trends to predict the Zika virus in Brazil and Columbia. It aimed to determine whether the search volume for the term 'Zika' on Google Trends could serve as an early surveillance system for anticipating Zika outbreaks. The researchers used time-series forecasting models to establish a relationship between the weekly Zika cases and the corresponding Google search query data. As can be seen from this review, in these works Google Trends was used for the mining and analysis of relevant web behavior during virus outbreaks of the past such as Lyme disease, malaria, syphilis, HIV, ILI, and Zika virus. However, none of these works focused on the analysis of web behavior in the context of the 2023 MVD outbreak.
To summarize, time series forecasting, investigation of conspiracy theories, and web behavior mining and analysis using Google Trends during virus outbreaks have attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Big Data, Data Analysis, Data Science, and Computer Science in the last few years. However, prior works in this field have multiple limitations as follows:
  • The works that applied time series forecasting models on relevant web behavior did not investigate the web behavior data related to the 2023 MVD outbreak.
  • Some of the works related to the applications of time series forecasting models to model web behavior during virus outbreaks did not focus on:
    o
    studying the web behavior from different geographic regions.
    o
    comparing the performance of different time series forecasting models to determine the optimal model for studying web behavior in different regions.
  • Even though several works in this field have studied the development and dissemination of conspiracy theories related to virus outbreaks in the recent past such as COVID-19 and MPox, none of those works studied the relevant web behavior data in the context of the new conspiracy theory involving the Marburg Virus and the FEMA emergency alert signal.
  • Relevant web behavior data from Google Trends has been mined and analyzed in several prior works in this field to understand and interpret multimodal components of web behavior during virus outbreaks. However, such works did not focus on mining, analyzing, or interpreting the web behavior related to new conspiracy theories involving the Marburg Virus and the FEMA emergency alert signal.
The work presented in this paper aims to address these research gaps. The step-by-step methodology that was followed is outlined in Section 3 and the results are presented and discussed in Section 4.

3. Methodology

This section is divided into two parts. In Section 3.1 an overview of the working of Google Trends and the procedure that was followed for data collection using Google Trends is presented. Section 3.2 presents the methodology that was followed for the development of the time series forecasting models and the models for correlation analysis, which were applied to the data collected from Google Trends.

3.1. Overview of the Data Collection Architecture and Description of Data Collection

The data analyzed in this research work was collected from Google Trends [134]. Google Trends is a web-based tool provided by Google that allows users to delve into and assess the search interest and prevalence of topics, keywords, or search queries over time. It equips individuals with the means to gauge how frequently specific terms are queried on Google from different geographic regions, offering valuable insights into the dynamic trends and curiosities of online users [135]. Furthermore, Google Trends provides geographic data, facilitating the identification of regions or counties where a topic garners the greatest attention. This tool also provides information regarding related queries, spotlighting frequently associated search terms with the chosen topic, and facilitating the exploration of interconnected trends and inquiries of interest to users. Google Trends also supports comparative analysis, allowing users to gauge the relative popularity of multiple search terms [136].
Google Trends offers three key benefits when compared to traditional surveys. First, it eliminates the cost associated with data collection and analysis, in contrast to conventional surveys, which often come with financial implications. Second, conducting routine surveys across a diverse global user base can be a formidable challenge, whereas Google Trends effortlessly taps into the worldwide search data generated daily on Google, simplifying the process of data collection and analysis. Third, Google Trends provides data that can be easily mined and analyzed, avoiding delays inherent in traditional surveys, which may be subject to time constraints related to participant recruitment and inclusion criteria [137,138]. There are two mathematical equations that underline the functioning of Google Trends, which are shown as Equation (1) and Equation (2). In these equations, “q” represents the number of searches for the query in the location “l” during the period “t”. Here, Q(l, t) is the set of all the queries made from “l” during t, and π ( n(q,l,t)>τ ) is a dummy variable. The dummy variable serves as an indicator, taking the value 1 when the query meets the popularity threshold n(q,l,t)>τ and 0 otherwise. To add to this, Equation (1) yields Relative Popularity (RP) values that are subsequently scaled to fit within a range of 0 to 100, and Equation (2) provides the numerical value of the Google Trends Index (GTI).
R P q ,   l   ,   t = n ( q ,   l ,   t ) ∑ q ∈ Q ( l ,   t ) n ( q ,   l ,   t ) × π ( n ( q , l , t ) > τ )
G T I q , l , t = R P ( q , l , t ) m a x R P ( q , l , t ) t ∈ 1,2 , . . . . . , T   × 100
Google Trends offers a range of features that provide valuable insights related to web behavior on Google. The "Explore" feature allows users to dig deeper into online interests, enabling the exploration of keyword popularity over chosen time periods and regions. Google Trends also provides "Trending Searches", offering both daily search trends and real-time search trends for a selected region. For those interested in historical trends, the "Year in Searches" feature lets users explore what was trending in a specific region during a particular year. Additionally, Google Trends offers "Subscriptions", allowing users to receive updates on specific topics or trending searches via email. These features collectively make Google Trends a powerful tool for the mining and analysis of web behavior on different topics with a specific focus on virus outbreaks.
For the work presented in this paper. Google Trends was used for collecting data regarding the 2023 MVD outbreak and the conspiracy theory linking the MVD outbreak, a zombie outbreak, and the FEMA emergency alert signal. The workflow diagram in Figure 1 shows the step-by-step procedure that was followed for data collection using Google Trends. At first, the search queries were set to compile MVD-related and zombie-related search interests, and the geolocation was set to worldwide. Thereafter, in the “Search Category” option on Google Trends, all categories option was selected and for the type of search data to be mined, “Web Search” was selected as the relevant web behavior data was being mined. After setting these specifications for the data mining process, an API call to Google Trends was performed for the weekly data between October 2, 2023, to October 9, 2023. There were primarily two reasons why the data mining was performed for this time range. First, the date when the FEMA emergency alert signal was broadcasted was October 4, 2023, and the search interest data on that day as well as around that day is relevant to investigate. Second, Google Trends provides several options for data mining. Although custom timelines can be provided to the Google Trends API. However, selecting the timeline as “Past 7 days” provides the hourly search interest data for each day in the 7-day period. In this work, the investigation also included the analysis of search interests related to this conspiracy theory right after the broadcasting of the FEMA emergency alert signal. So, obtaining the hourly search interest data was necessary. After this data collection was completed, the master dataset comprised the hourly search interests related to MVD and search interests related to zombies (in the context of MVD-related conspiracy theory) between October 2, 2023, to October 9, 2023, for 216 regions. As this data was collected using Google Trends, as per the Google Trends algorithm, the highest value of the search interest was 100 and the lowest value was 0. The names of these 216 regions are shown in Table 1. These regions recorded significant search interests related to MVD and this conspiracy theory, so the data of search interests from these regions was included in the development of the master dataset.

3.3. Methodology for performing Time Series Forecasting

The data collected using Google Trends (discussed in Section 3.2) comprised the search interests related to MVD recorded from relevant Google Searches from the 216 regions. As Google Searches serve as an indicator of the needs, interests, motives, concerns, perspectives, and opinions of the global population during a virus outbreak, several prior works in this field have developed time series forecasting models to accurately predict web behavior during virus outbreaks (reviewed in Section 2.1). As discussed in Section 2.1, such works did not focus on predicting web behavior related to the recent outbreak of MVD. To add to this, several works related to time series forecasting used only one specific model for time series forecasting out of some of the most popular models such as ARIMA, Autocorrelation, or Long Short-Term Memory network (LSTM). The work presented in this paper addresses both limitations. More specifically, programs were written in Python 3.11.5 to develop and implement all these models – ARIMA, Autocorrelation, and LSTM on the web searches related to MVD emerging from 216 regions (Table 1) and the performance characteristics of these models per region for all the 216 regions was computed. The pseudocodes of these programs are shown in Algorithms 1, 2, and 3, respectively.
Algorithm 1: ARIMA for Time Series Forecasting of Web Behavior related to MVD
Input: Master Dataset for Analysis
Output: ARIMA Forecast for the Data, Performance Metrics (RMSE, MSE, AE)
File Path
dataframe = load the data files
regions[] = region names
for each region in regions do:
    dataset = get values from dataframe: marburg virus: <region>
    dataset = convert dataset to float32
    x = dataset
    size = calculate size as 75% of all x
    split x into:
        train: from start to size
        test: from size to end x
    history = train value
    predictions_test = empty list
    for data in test do:
       model = history, order=(0,1,0)
       model_fit_test = fit model
       output_test = forecast by fitted model
       yhat_test = output[0]
       predictions_test 🠠append(yhat)
       obs_test = test[data]
       history 🠠append(obs)
    end for
    predictions_train = empty list
    for data in train do:
       model_train = ARIMA history, order(0,1,0)
       model_fit_train 🠠fit model
       output_train 🠠get forecast
       yhat_train 🠠output_train[0]
       predictions_train 🠠append(yhat_train)
       obs_train 🠠train[data]
       history 🠠append(obs_train)
    RMSE = calculate RMSE (test, prediction_test), calculate RMSE (train, prediction_train)
    MSE = calculate MSE (test, prediction_test), calculate MSE (train, prediction_train)    
    AE = calculate AE (test, prediction_test), calculate AE (train, prediction_train)
    predictionsplot = empty list
    end for
    for data from 0 to dataset length do:
       if data <= predicitons length do:
            predictionsplot 🠠append(np.nan)
       else:
            index = length of dataset – data
            predictionsplot 🠠 append_prediction(index)
    plot (dataset label = ground truth, predictions_train, predictions_test)
    show and save the plot
    end for
end for
Algorithm 2: Autocorrelation for Time Series Forecasting of Web Behavior related to MVD
Input: Master Dataset for Analysis
Output: Autocorrelation Forecast for the Data, Performance Metrics (RMSE, MSE, AE)
dataframe = load the data files
for each region in regions do:
    dataset = get values from dataframe: marburg virus: <region>
    dataset = convert dataset to float32
    x = dataset
    size = calculate size as 75% of all x
    split x into:
        train: from start to size
        test: from size to end x
    windows = 24
    model = Autoreg(train, lags= 24)
    model_fit = fit the model(training data)
    coef = coefficients from the model fit
    lag = last 24 values of the dataset
    prediction_test = empty list
    for each data in test do:
       length = history length
       lag = last window value in history
       yhat = coef [0]
       for each d in 0 to windows – 1 do:
           yhat_test+= coef[d+1] * lag[windows-d-1]
       obs_test = test [data]
       prediction_test 🠠append(yhat_test)
       history 🠠append(obs_test)
    end for
    prediction_train = empty list
    for data in train do:
       length = length of history
       lag = last window values from history
       yhat_train = coef[0]
     end for
    for each data in history do:
             yhat_train += coef[d + 1] * lag[window - d – 1]
     obs_train🠠train[data]
     prediction_train 🠠append(yhat_train)
     history 🠠append(obs_train)
     end for
    RMSE = calculate RMSE (test, prediction_test), calculate RMSE (train, prediction_train)
    MSE = calculate MSE (test, prediction_test), calculate MSE (train, prediction_train)    
    AE = calculate AE (test, prediction_test), calculate AE (train, prediction_train)
    for each t3 from 0 to the length of the series do:
       if t3 <= length of predictions2 then:
              predicionsplot 🠠 append(np.nan)
       else:
              index2 🠠 length of dataset – data
              predictionsplot     append_prediction(index)
    plot (dataset label = ground truth, predictions_train, predictions_test)
    show and save the plot
    end for
end for
Algorithm 3: LSTM for Time Series Forecasting of Web Behavior related to MVD
Input: Master Dataset for Analysis
Output: Autocorrelation Forecast for the Data, Performance Metrics (RMSE, MSE, AE)
dataframe = load the data files
for each region in regions do:
    tf.keras.utils.set_random_seed(1)
    tf.config.experimental.enable_op_determinism()
    Function create_dataset(dataset, look_back=1):
               dataX = empty list
               dataY = empty list
               for i from 0 to (len(dataset)-look_back-1 do:
                     a = dataset segment from i and size look_back
                     dataX 🠠append(a)
                     dataY 🠠append(dataset[i+look_back, 0]
                     np.array (dataX)
                     np.array (dataY)
                     return (data)
               end for
    end of Function
    dataset = get values from dataframe: marburg virus: <region>
dataset = dataframe.values
dataset = convert dataset (float32)
scaler = MiniMaxScaler(feature_range=(0,1))
dataset = fit, transform dataset
train_size = 75% of all dataset
test_size = len(dataset) – train_size
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = reshape trainX with dimension
testX = reshape testX with dimension
model = Sequential()
model.add(LSTM(100, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
trainPredict = inverse transform by scaler
trainY = inverse transform by scaler
testPredict = inverse transform by scaler
testY = inverse transform by scaler
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
    RMSE = calculate RMSE (test, testPredict), calculate RMSE (train, trainPredict)
    MSE = calculate MSE (test, testPredict), calculate MSE (train, trainPredict)    
    AE = calculate AE (test, testPredict), calculate AE (train, trainPredict)
testPredictPlot = np.empty_like(dataset)
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
    plot (dataset label = ground truth, trainPredictPlot, testPredictPlot)
    show and save the plot
end for
Figure 2 shows a flowchart that outlines the working of these models and how the same was applied to the master dataset. As can be seen from Figure 2 and Algorithms 1, 2, and 3, the performance of these models for time series forecasting was evaluated by computing the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) for both the train set and the test set. The results of the same are presented in Section 4.

3.4. Methodology for Correlation Analysis

The section presents the specifics of the correlation analysis that was performed on the master dataset. The dataset contained search interests from relevant Google Searches related to MVD and the conspiracy theory for each region in the list of 216 regions. For each region, the correlations between these two types of search interests were investigated using Pearson’s correlation. Thereafter, the nature of the correlation i.e., statistically significant, or not statistically significant was determined based on the p-value of the correlation. To add to this, the correlation between the search interest data related to this conspiracy theory in the United States and the remaining countries was also evaluated using Pearson’s correlation to determine the nature of the correlation i.e., statistically significant, or not statistically significant. Figure 3 represents a flowchart that shows the step-by-step process that was performed in this regard to develop and apply the models for correlation analysis. Algorithm 4 represents the pseudocode of the program that was written in Python 3.11.5 to check for correlations between web behavior related to MVD and this conspiracy theory and to determine the nature of the same. Another program was also written to check for correlations between the web behavior related to this conspiracy theory in the United States and other countries. To avoid possible redundancy, the pseudocode of that program is not presented in this paper.
Algorithm 4: Correlation between MVD and Conspiracy Theory related Web Behavior
Input: Master Dataset for Analysis
Output: Pearson’s r-value and p-value for each region
dataframe = load the data files
files = get the list of all CSV files in the master dataset using a recursive search
country = empty list
Name = empty list
for each file_name in files do:
       i🠠0, col1🠠empty list, col2🠠empty list
       for each date in the first column of f do:
            if specific date exists then:
               if second column of f at the ith row is an integer or is digit then:
                         col1🠠append the integer value
               else
                         col1🠠append 0
               if third column of f at the ith row is an integer or is digit then:
                         col2🠠append the integer value
              else
                         col2🠠append 0
           end if
        increment i
end for
country🠠append col, col2
r_value = empty list, p_value = empty list, significance = empty list
for each entry c in country do:
     stat_1 = calculate pearson correlation between c[0] and c[1]
     p_1🠠 extract second value from stat_1
     p_0🠠extract first value from stat_1
     r_value🠠 p_0, p_value🠠p_1
     if p_1 is less than 0.05 then:
               significance🠠statically significant
     else:
               significance🠠not significant
end for
open file in writing mode as CSV output:
           writer = CSV writer for CSV output
           write the header row with columns
            for i from 0 to length of country do:
               row🠠 empty list
               row [i]🠠 append(name[i], r_value[i], p_value[i], significance[i])
               write row to the CSV
            end for

4. Results and Discussion

This section presents the results and highlights the novel findings of this work. As discussed in Section 3, Algorithms 1, 2, and 3 were applied to the web behavior data related to MVD present in the dataset and the results of forecasting for each region were plotted and computed using RMSE, MSE, and MAE. As a result of the same, a graph was plotted per model per region resulting in 648 graphs (3 plots per region x 216 regions). To avoid possible redundancy, the graphs of 9 regions (selected at random) are shown in Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12, respectively.
The complete results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithms 1, 2, and 3 (ARIMA, Autocorrelation, and LSTM) on the data of all 216 regions are presented in Tables 2, 3, and 4, respectively.
It is worth mentioning here that for multiple regions the search interests related to MVD were constant during this 7-day period. So, for those regions, the RMSE, MSE, and MAE are reported to be 0 in Tables 2, 3, and 4. The performance metrics reported in Tables 2, 3, and 4, allow comparisons of the performance of the time series forecasting models (ARIMA, Autocorrelation, and LSTM) which were developed and implemented on the dataset using Algorithms 1, 2, and 3, respectively. These performance metrics reveal that there wasn’t any particular time series forecasting model that always outperformed the other two models for every region. However, the results presented in Tables 2, 3, and 4 serve as a framework for the identification of the optimal time series forecasting model for predicting MVD virus-related web behavior in different regions. For instance, for the United States, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.46291, 0.805232, and 0.7681, respectively. So, based on the same, it can be concluded that the ARIMA model (Algorithm 1) is best suited to forecast web behavior related to MVD emerging from the United States. Similarly, for Canada, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.845154, 0.932133, and 1.1596. So, based on the same, it can once again be concluded that the ARIMA model (Algorithm 1) is best suited to forecast web behavior related to MVD emerging from Canada. However, for China, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 10.89779, 11.35232, and 8.1723. So, based on the same, it can be concluded that the LSTM model (Algorithm 3) is best suited to forecast web behavior related to MVD emerging from China. In a similar manner, an optimal model for performing forecasting of MVD-related web behavior can be deduced for each region out of all the 216 regions listed in Table 1, based on analysis of the findings presented in Table 2, Table 3 and Table 4.
Thereafter, the results of correlation analysis are presented. As shown in Figure 3, two types of correlations were investigated. First, the correlation between search interests related to MVD and search interests related to zombies (in the context of MVD-related conspiracy theory) stated as Model 1 in Figure 3, was investigated. Second, the correlation between the zombie-related search interests (in the context of MVD-related conspiracy theory) in the United States and other regions, stated as Model 2 in Figure 3, was investigated. The results of applying Model 1 on the master dataset are shown in Table 5.
As can be seen from Table 5, the list of regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of MVD-related conspiracy theory) on Google on October 4, 2023, were Argentina, Bhutan, Burundi, France, Ghana, Lebanon, Madagascar, Myanmar (Burma), Peru, Romania, South Africa, South Korea, United States, and Uruguay. This is an interesting finding as historically zombie-related web searches on Google had no correlation with web searches on Google related to MVD. In this context, October 4, 2023, was selected as the date for investigation because the FEMA emergency alert signal was broadcast on that day and the conspiracy theory was that this signal would activate the Marburg virus in people who have been vaccinated and turn some of them into zombies. Thereafter, the second correlation model (Model 2 in Figure 3) was run on the master dataset to check for correlations between zombie-related web searches on Google in the United States and zombie-related web searches from the list of 215 remaining regions. The results of the same are shown in Table 6.
As can be seen from Table 6, the list of regions where this correlation was statistically significant were Canada, Hong Kong, Mauritania, Mongolia, Northern Mariana Islands, Taiwan, Timor-Leste, and Uzbekistan. This is also an interesting finding as the FEMA emergency alert signal was broadcasted only in the United States. However, the results show that the zombie-related searches (in the context of MVD-related conspiracy theory) from the United States had a statistically significant correlation with zombie-related searches (in the context of MVD-related conspiracy theory) emerging from multiple other regions even though no emergency signal or similar was broadcasted in those regions. Thereafter, an analysis was also performed to determine the list of regions out of these 216 regions where there was a positive increase in zombie-related searches (in the context of MVD-related conspiracy theory) between 2 PM and 3 PM (EST) on October 4, 2023. This time range was specifically chosen for this analysis as the FEMA emergency alert signal was broadcast at 2.20 PM (EST) on October 4, 2023. The results are shown in Table 7.
Thereafter, further analysis of the trends of search interests in regions where there was a statistically significant correlation between MVD-related web searches and zombie-related web searches (in the context of MVD-related conspiracy theory) was performed. In this analysis, the trends of zombie-related web searches (in the context of MVD-related conspiracy theory) during the entire day on October 4, 2023, were analyzed.
It is worth noting that in Figure 13 and Figure 14, the Y-axis represents the value of search interests as obtained from Google Trends and the X-axis represents the hour, where 12.01 to 1.00 is considered hour 1, 1.01 to 2.00 is considered hour 2, and so on. From Figure 13 and Figure 14, the trends and variations of searches in these regions can be observed. For instance, there was a peak in search interests in multiple regions between 2 PM and 3 PM. At the same time, it is interesting to note that there was a peak in search interests in Bhutan between 5 PM to 8 PM. A different pattern can be seen in Argentina, where the peak in search interests occurred between 2 AM to 5 AM. In a similar manner, these Figures can be analyzed to interpret the similarities and variations in terms of the trends in zombie-related web searches (in the context of MVD-related conspiracy theory) on October 4, 2023, in different geographic regions where there was a statistically significant correlation between MVD-related web searchers on Google and zombie-related web searches (in the context of MVD-related conspiracy theory) on Google.
The research work presented and discussed in this paper has a few limitations. First, the data obtained by Google Trends is the data generated by only a certain percentage of the worldwide population who have access to the internet and opt to use Google as their primary search engine. Second, it is important to note that the data collected from Google Trends and analyzed in this work represents the relative search volumes rather than absolute values of the total amount of Google Searches emerging from different geographic regions. Finally, there is a notable inadequacy related to the disclosure of the methodology and underlying algorithms used by Google in producing search interest data.

5. Conclusion

As a result of outbreak of the MVD in February 2023 and the high fatality rate of the same, on a global scale, people have been devoting a substantial amount of time to social media platforms and the internet in general over the last few months to acquire and disseminate information pertaining to MVD. During virus outbreaks in the recent past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from different fields such as Healthcare, Epidemiology, Big Data, Data Analysis, Data Science, and Computer Science utilized Google Trends to extract and analyze multimodal components of web behavior of the general public in order to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. During such virus outbreaks of the past, the application of time series forecasting models such as ARIMA, LSTM, and Autocorrelation to web searches to model, predict, and forecast the web behavior of the general public in the context of the outbreaks also attracted the attention of researchers from different disciplines. Furthermore, the paradigms of web behavior on the internet during virus outbreaks of the past also led to the development and dissemination of conspiracy theories that led to a range of reactions in the general public. For example, during the outbreak of COVID-19, a popular conspiracy theory was that 5G towers had a role in the transmission of the virus. The analysis of such conspiracy theories during virus outbreaks of the past has also been relevant to understanding the underlying patterns of information seeking and sharing on the internet. The outbreak of MVD and an electronic alert (for testing purposes) sent by the Federal Emergency Management Agency (FEMA) to all television, radio, and mobile devices throughout the United States on October 4, 2023, has given rise to an unconventional conspiracy theory that associates the Marburg Virus with a zombie outbreak. Specifically, the conspiracy theory was centered around the concept that the FEMA alert would activate the Marburg virus in people who have been vaccinated and turn some of them into zombies. This conspiracy theory spread like wildfire on the internet to the extent that soon after the FEMA alert signal was broadcast, Jeremy Edwards (press secretary and deputy director of public affairs at FEMA) provided a statement to the public to clarify that he was not a zombie. Due to this recent outbreak of MVD and the conspiracy theory involving the same, it is imperative to conduct an investigation into the underlying patterns of web behavior in order to get a comprehensive understanding of the paradigms of information seeking and sharing used by the general public in this particular context. No prior work in this field thus far has focused on the same. Therefore, the work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. It presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using three models - ARIMA, LSTM, and Autocorrelation. The results of this analysis in terms of RMSE, MSE, and MAE are presented and discussed. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. For instance, for the United States, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.46291, 0.805232, and 0.7681, respectively. So, based on the same, it can be concluded that the ARIMA model is best suited to forecast web behavior related to MVD emerging from the United States. Similarly, for Canada, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.845154, 0.932133, and 1.1596. So, based on the same, it can once again be concluded that the ARIMA model is best suited to forecast web behavior related to MVD emerging from Canada. However, for China, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 10.89779, 11.35232, and 8.1723. So, based on the same, it can be concluded that the LSTM model is best suited to forecast web behavior related to MVD emerging from China. The paper also presents the findings from investigating two types of web behavior for correlations. First, the correlation between search interests related to MVD and search interests related to zombies (in the context of MVD-related conspiracy theory) was investigated. Second, the correlation between zombie-related search interests (in the context of MVD-related conspiracy theory) in the United States and other regions was investigated. The findings from the first analysis show that the list of regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of MVD-related conspiracy theory) on Google on October 4, 2023, were Argentina, Bhutan, Burundi, France, Ghana, Lebanon, Madagascar, Myanmar (Burma), Peru, Romania, South Africa, South Korea, United States, and Uruguay. This is an interesting finding as historically zombie-related web searches on Google had no correlation with web searches on Google related to MVD. The findings from the second analysis show that the list of regions where this correlation was statistically significant were Canada, Hong Kong, Mauritania, Mongolia, Northern Mariana Islands, Taiwan, Timor-Leste, and Uzbekistan. This is also an interesting finding as the FEMA emergency alert signal was broadcasted only in the United States. Finally, the paper also presents an analysis of variation and degree of increase of search interests in the context of this conspiracy theory emerging from different geographic regions. As per the best knowledge of the authors, no similar work has been done in this field thus far. Future work would involve detecting and analyzing the popular topics represented in Google Searches about this conspiracy theory to interpret the specific themes of information seeking and sharing on Google in the context of this conspiracy theory.

Author Contributions

Conceptualization, N.T.; methodology, N.T., S.C, K.A.P, N.A., A.P., R.S.; software, N.T., S.C, K.A.P, A.P., R.S.; validation, N.T.; formal analysis, N.T., S.C, K.A.P, N.A., A.P., R.S.; investigation, N.T., S.C, K.A.P, A.P., R.S.; resources, N.T.; data curation, N.T.; writing—original draft preparation, N.T., C.H., V.K.; writing—review and editing, N.T.; visualization, N.T., S.C, K.A.P, A.P., R.S.; supervision, N.T.; project administration, N.T.; funding acquisition, Not Applicable. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data analyzed in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. A workflow diagram to represent the data collection and the development of the master dataset using Google Trends.
Figure 1. A workflow diagram to represent the data collection and the development of the master dataset using Google Trends.
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Figure 2. A flowchart to represent the application of Algorithm 1 (Model 1), Algorithm 2 (Model 2), and Algorithm 3 (Model 3) to the master dataset.
Figure 2. A flowchart to represent the application of Algorithm 1 (Model 1), Algorithm 2 (Model 2), and Algorithm 3 (Model 3) to the master dataset.
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Figure 3. A flowchart that represents different forms of correlation analysis that was performed on the dataset.
Figure 3. A flowchart that represents different forms of correlation analysis that was performed on the dataset.
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Figure 4. Figure 4. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Australia using Autocorrelation, ARIMA, and LSTM
Figure 4. Figure 4. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Australia using Autocorrelation, ARIMA, and LSTM
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Preprints 89124 g005
Figure 5. Figure 5. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Canada using Autocorrelation, ARIMA, and LSTM
Figure 5. Figure 5. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Canada using Autocorrelation, ARIMA, and LSTM
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Figure 6. Figure 6. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Morocco using Autocorrelation, ARIMA, and LSTM
Figure 6. Figure 6. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Morocco using Autocorrelation, ARIMA, and LSTM
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Figure 7. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Ukraine using Autocorrelation, ARIMA, and LSTM
Figure 7. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Ukraine using Autocorrelation, ARIMA, and LSTM
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Figure 8. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in the USA using Autocorrelation, ARIMA, and LSTM
Figure 8. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in the USA using Autocorrelation, ARIMA, and LSTM
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Figure 9. Figure 9. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Uruguay using Autocorrelation, ARIMA, and LSTM
Figure 9. Figure 9. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Uruguay using Autocorrelation, ARIMA, and LSTM
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Figure 10. Figure 10. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Ireland using Autocorrelation, ARIMA, and LSTM
Figure 10. Figure 10. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Ireland using Autocorrelation, ARIMA, and LSTM
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Figure 11. Figure 11. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in France using Autocorrelation, ARIMA, and LSTM
Figure 11. Figure 11. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in France using Autocorrelation, ARIMA, and LSTM
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Figure 12. Figure 12. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Denmark using Autocorrelation, ARIMA, and LSTM
Figure 12. Figure 12. Representation of the results of Time Series Forecasting of the Search Interests related to MVD in Denmark using Autocorrelation, ARIMA, and LSTM
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Figure 13. Trends in zombie-related web searches (in the context of MVD-related conspiracy theory) on October 4, 2023, in Argentina, Bhutan, Burundi, France, Ghana, Lebanon, Madagascar.
Figure 13. Trends in zombie-related web searches (in the context of MVD-related conspiracy theory) on October 4, 2023, in Argentina, Bhutan, Burundi, France, Ghana, Lebanon, Madagascar.
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Figure 14. Trends in zombie-related web searches (in the context of MVD-related conspiracy theory) on October 4, 2023, in Myanmar (Burma), Peru, Romania, South Africa, South Korea, the United States, and Uruguay.
Figure 14. Trends in zombie-related web searches (in the context of MVD-related conspiracy theory) on October 4, 2023, in Myanmar (Burma), Peru, Romania, South Africa, South Korea, the United States, and Uruguay.
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Table 1. List of 216 regions for which data was collected using Google Trends.
Table 1. List of 216 regions for which data was collected using Google Trends.
List of Regions
Afghanistan, Åland Islands, Albania, Algeria, American Somoa, Andorra, Angola, Antigua & Barbuda, Argentina, Armenia, Aruba, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bermuda, Bhutan, Bolivia, Bosnia & Herzegovina, Botswana, Brazil, British Virgin Islands, Brunei, Bulgaria, Burkina, Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Cayman Islands, Chad, Chile, China, Colombia, Comoros, Congo – Brazzaville, Congo – Kinshasa, Costa Rica, Côte d’Ivoire, Croatia, Cuba, Curaçao, Cyrpus, Czechia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Faroe Islands, Fiji, Finland, France, French, Guiana, French, Polynesia, Gabon, Gambia, Georgia, Germany, Ghana, Gibraltar, Greece, Greenland, Grenada, Guadeloupe, Guam, Guatemala, Guernsey, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hong Kong, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Isle of Man, Israel, Italy, Jamaica, Japan, Jersey, Jordan, Kazakhstan, Kenya, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Macao, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Martinique, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar (Burma), Namibia, Nepal, Netherlands, New Caledonia, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Northern Mariana Islands, Norway, Oman, Pakistan, Palestine, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Puerto Rico, Qatar, Réunion, Romania, Russia, Rwanda, Samoa, San Marino, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Sint Maarten, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Korea, South Sudan, Spain, Sri Lanka, St. Barthélemy, St. Helena, St. Kitts & Nevis, St. Lucia, St. Martin, St. Pierre & Miquelon, St. Vincent & Grenadines, Sudan, Suriname, Sweden, Switzerland, Syria, Taiwan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad & Tobago, Tunisia, Türkiye, Turkmenistan, Turks & Caicos Islands, U.S. Virgin Islands, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, USA, Uzbekistan, Vanuatu, Venezuela, Vietnam, Western Sahara, Yemen, Zambia, Zimbabwe
Table 2. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 1 on the master dataset.
Table 2. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 1 on the master dataset.
Country Name RMSE for ARIMA (Train Set) MSE for ARIMA (Train Set) MAE for ARIMA (Train Set) RMSE for ARIMA (Test Set) MSE for ARIMA (Test Set) MAE for ARIMA (Test Set)
Afghanistan 0 0 0 0 0 0
Åland Islands 0 0 0 0 0 0
Albania 13.78808 190.1111 3.809524 20.00595 400.2381 6.095238
Algeria 9.59249 92.01587 3.68254 22.13272 489.8571 6.571429
American Samoa 0 0 0 0 0 0
Andorra 0 0 0 0 0 0
Angola 9.029933 81.53968 3.396825 2.488067 6.190476 0.761905
Antigua & Barbuda 16.74837 280.5079 7.253968 5.442338 29.61905 2.380952
Argentina 8.276952 68.50794 2.571429 1.091089 1.190476 0.47619
Armenia 0 0 0 0 0 0
Aruba 0 0 0 0 0 0
Australia 5.370407 28.84127 2.222222 7.309485 53.42857 2.952381
Austria 16.61277 275.9841 5.126984 5.019011 25.19048 1.809524
Azerbaijan 16.67762 278.1429 5.47619 4.396969 19.33333 2.190476
Bahamas 13.89616 193.1032 5.261905 7.857359 61.7381 3.404762
Bahrain 12.02181 144.5238 5.873016 16.74885 280.5238 8.095238
Bangladesh 7.380261 54.46825 3.801587 5.561346 30.92857 2.166667
Barbados 10.33257 106.7619 3.873016 9.209209 84.80952 3.904762
Belarus 0 0 0 0 0 0
Belgium 14.64772 214.5556 4.285714 3.070598 9.428571 0.952381
Belize 9.096485 82.74603 3.68254 6.488084 42.09524 2.904762
Benin 7.529835 56.69841 3.650794 13.73386 188.619 5.095238
Bermuda 0 0 0 0 0 0
Bhutan 12.06892 145.6587 4.833333 8.063734 65.02381 2.880952
Bolivia 11.99669 143.9206 4.047619 8.524475 72.66667 3.809524
Bosnia & Herzegovina 7.268108 52.8254 2.079365 7.057586 49.80952 2
Botswana 18.51833 342.9286 5.865079 7.851297 61.64286 3.833333
Brazil 1.339272 1.793651 0.619048 2.654735 7.047619 1.238095
British Virgin Islands 0 0 0 0 0 0
Brunei 16.08213 258.6349 5.730159 14.56512 212.1429 5.095238
Bulgaria 17.32463 300.1429 6.460317 13.12758 172.3333 5.47619
Burkina Faso 21.58924 466.0952 7.825397 6.636838 44.04762 2.904762
Burundi 9.040473 81.73016 2.984127 9.829499 96.61905 4
Cambodia 16.83628 283.4603 7.920635 15.45654 238.9048 8.809524
Cameroon 6.508846 42.36508 2.730159 24.94756 622.381 10.57143
Canada 2.875733 8.269841 1.746032 0.845154 0.714286 0.47619
Cape Verde 18.35886 337.0476 7.761905 12.40584 153.9048 6.095238
Cayman Islands 6.670237 44.49206 2.142857 5.300494 28.09524 2.380952
Chad 8.387443 70.34921 3.047619 3.690399 13.61905 1.666667
Chile 9.139136 83.52381 1.444444 15.43651 238.2857 2.571429
China 20.72534 429.5397 9.047619 10.89779 118.7619 4.285714
Côte d’Ivoire 7.041825 49.5873 2.412698 13.30592 177.0476 5.047619
Colombia 5.540615 30.69841 1.603175 0.872872 0.761905 0.380952
Comoros 5.889188 34.68254 2.126984 6.113996 37.38095 2.714286
Congo - Brazzaville 11.52774 132.8889 3.809524 8.582929 73.66667 3.47619
Congo - Kinshasa 13.79383 190.2698 5.190476 4.918381 24.19048 2.142857
Costa Rica 11.30599 127.8254 4.619048 27.79431 772.5238 11.28571
Croatia 15.55431 241.9365 5.253968 16.54719 273.8095 5.238095
Cuba 14.12754 199.5873 5.650794 15.76615 248.5714 3.714286
Curaçao 0 0 0 0 0 0
Cyprus 14.44969 208.7937 5.142857 9.534399 90.90476 4.619048
Czechia 9.922317 98.45238 3.246032 7.453028 55.54762 3.5
Denmark 13.02013 169.5238 4.396825 9.329931 87.04762 3.571429
Djibouti 0 0 0 0 0 0
Dominica 0 0 0 0 0 0
Dominican Republic 18.38305 337.9365 6.571429 6.879922 47.33333 2.47619
Ecuador 8.286056 68.65873 2.722222 4.49603 20.21429 1.928571
Egypt 9.951868 99.03968 5.214286 7.123068 50.7381 3.404762
El Salvador 14.10449 198.9365 3.222222 2.43975 5.952381 1
Equatorial Guinea 14.94275 223.2857 5.603175 18.76547 352.1429 7.952381
Estonia 13.93238 194.1111 4.428571 22.47856 505.2857 9.333333
Eswatini 17.02706 289.9206 6.968254 21.07809 444.2857 9.333333
Ethiopia 18.62879 347.0317 7.714286 23.99603 575.8095 10.47619
Faroe Islands 0 0 0 0 0 0
Fiji 20.84523 434.5238 8.634921 13.99149 195.7619 6.428571
Finland 7.618899 58.04762 2.412698 3.450328 11.90476 1.52381
France 1.480026 2.190476 0.603175 1.647509 2.714286 0.761905
French Guiana 0 0 0 0 0 0
French Polynesia 0 0 0 0 0 0
Gabon 8.54679 73.04762 3.142857 14.2361 202.6667 6.095238
Gambia 14.50999 210.5397 5.968254 11.53256 133 3.952381
Georgia 15.58082 242.7619 5.587302 5.550633 30.80952 2.190476
Germany 2.173067 4.722222 1.18254 2.198484 4.833333 1.214286
Ghana 13.6376 185.9841 7.095238 25.91837 671.7619 11.66667
Gibraltar 0 0 0 0 0 0
Greece 7.662525 58.71429 2.857143 19.79177 391.7143 7.238095
Greenland 0 0 0 0 0 0
Grenada 14.38363 206.8889 4.47619 12.02775 144.6667 4.619048
Guadeloupe 5.747325 33.03175 2.126984 2.115701 4.47619 0.666667
Guam 0 0 0 0 0 0
Guatemala 14.58799 212.8095 5.365079 5.89996 34.80952 2.380952
Guernsey 0 0 0 0 0 0
Guinea 12.59567 158.6508 5.285714 11.49534 132.1429 4.952381
Guinea-Bissau 17.11956 293.0794 4.952381 12.12828 147.0952 4.333333
Guyana 9.763066 95.31746 1.857143 14.83561 220.0952 2.619048
Haiti 10.07433 101.4921 3.650794 1.690309 2.857143 0.761905
Honduras 11.29827 127.6508 4.31746 9.892277 97.85714 3.952381
Hong Kong 10.86497 118.0476 4.460317 9.170346 84.09524 3.52381
Hungary 7.077799 50.09524 2.936508 15.23155 232 6.571429
Iceland 8.991177 80.84127 3.619048 28.24721 797.9048 9.142857
India 1.939563 3.761905 0.888889 0.9759 0.952381 0.380952
Indonesia 1.425393 2.031746 0.809524 1.195229 1.428571 0.714286
Iran 8.369446 70.04762 3.31746 6.33208 40.09524 3.238095
Iraq 17.57027 308.7143 8.126984 12.94126 167.4762 6.380952
Ireland 1.268069 1.608 0.488 1.625687 2.642857 0.642857
Isle of Man 0 0 0 0 0 0
Israel 11.54494 133.2857 5.031746 19.18705 368.1429 10.2381
Italy 4.059087 16.47619 1.888889 2.77746 7.714286 1.142857
Jamaica 6.163126 37.98413 2.873016 10.28175 105.7143 3.857143
Japan 11.6585 135.9206 4.492063 13.88216 192.7143 5.095238
Jersey 0 0 0 0 0 0
Jordan 5.61602 31.53968 2.206349 9.337584 87.19048 3.571429
Kazakhstan 0 0 0 0 0 0
Kenya 9.204899 84.73016 3.968254 16.88617 285.1429 8
Kosovo 8.073079 65.1746 2.15873 9.623879 92.61905 3.52381
Kuwait 15.4509 238.7302 7 24.9819 624.0952 10.85714
Kyrgyzstan 0 0 0 0 0 0
Laos 0 0 0 0 0 0
Latvia 12.78454 163.4444 4.492063 21.67839 469.9524 6.619048
Lebanon 16.85701 284.1587 6.285714 7.412987 54.95238 2.380952
Lesotho 11.6986 136.8571 5.365079 26.57245 706.0952 12.90476
Liberia 11.81303 139.5476 5.039683 16.28248 265.119 7.833333
Libya 10.5492 111.2857 5.190476 11.81605 139.619 4.809524
Liechtenstein 0 0 0 0 0 0
Lithuania 11.90038 141.619 3.809524 6.45866 41.71429 2.380952
Luxembourg 6.737717 45.39683 2 24.15919 583.6667 10.2381
Macao 14.08985 198.5238 4.285714 6.561068 43.04762 2.190476
Madagascar 11.76894 138.5079 3.52381 25.11213 630.619 9.190476
Malawi 13.69973 187.6825 4.603175 17.36718 301.619 6.095238
Malaysia 4.101877 16.8254 2.222222 4.649629 21.61905 2
Maldives 12.7895 163.5714 4.301587 21.52629 463.381 9.333333
Mali 10.19103 103.8571 3.873016 19.22548 369.619 10.90476
Malta 13.12093 172.1587 4.857143 5.191568 26.95238 2.285714
Martinique 12.62336 159.3492 4.285714 29.56188 873.9048 13.38095
Mauritania 20.30404 412.254 8.761905 12.12043 146.9048 4.857143
Mauritius 12.63593 159.6667 4.777778 24.51336 600.9048 11.04762
Mexico 4.037522 16.30159 1.380952 1.759329 3.095238 1
Moldova 0 0 0 0 0 0
Mongolia 8.125504 66.02381 2.626984 4.753445 22.59524 2.214286
Montenegro 13.7708 189.6349 3.444444 8.799351 77.42857 3.142857
Morocco 17.04033 290.373 7.309524 17.09985 292.4048 7.119048
Mozambique 11.9227 142.1508 3.007937 8.617535 74.2619 3.595238
Myanmar (Burma) 9.760627 95.26984 2.349206 15.51497 240.7143 3.047619
Namibia 12.94524 167.5794 4.944444 22.81969 520.7381 7.166667
Nepal 16.66381 277.6825 6.52381 4.353433 18.95238 1.428571
Netherlands 15.66363 245.3492 4.52381 10.36937 107.5238 4.333333
New Caledonia 0 0 0 0 0 0
New Zealand 6.948792 48.28571 3.047619 10.13246 102.6667 3.904762
Nicaragua 5.087333 25.88095 1.515873 1.870829 3.5 0.880952
Niger 4.739232 22.46032 1.809524 22.90872 524.8095 7.52381
Nigeria 9.814955 96.33333 3.888889 9.763879 95.33333 3.52381
North Macedonia 14.08928 198.5079 4.063492 4.203173 17.66667 1.857143
Northern Mariana Islands 0 0 0 0 0 0
Norway 8.161563 66.61111 3.02381 4.896549 23.97619 2.309524
Oman 12.69921 161.2698 5.666667 21.22218 450.381 8.52381
Pakistan 9.568467 91.55556 3.349206 6.06316 36.7619 2.190476
Palestine 0 0 0 0 0 0
Panama 14.65097 214.6508 5.444444 18 324 5.571429
Papua New Guinea 0 0 0 0 0 0
Paraguay 15.67882 245.8254 4.984127 18.52926 343.3333 7.190476
Peru 10.9982 120.9603 3.738095 1.779513 3.166667 0.833333
Philippines 1.43095 2.047619 0.714286 2.035401 4.142857 0.809524
Poland 4.712361 22.20635 1.920635 6.611678 43.71429 2.952381
Portugal 15.74348 247.8571 5.730159 13.20714 174.4286 4.428571
Puerto Rico 15.98064 255.381 5.809524 2.21467 4.904762 0.904762
Qatar 15.13694 229.127 6.047619 30.71451 943.381 12.71429
Réunion 13.52159 182.8333 4.277778 12.2756 150.6905 5.880952
Romania 5.274978 27.8254 2.412698 9.162553 83.95238 3.190476
Russia 3.825561 14.63492 1.333333 1.889822 3.571429 0.904762
Rwanda 19.74721 389.9524 6.555556 19.97141 398.8571 9.571429
Samoa 0 0 0 0 0 0
San Marino 0 0 0 0 0 0
Saudi Arabia 13.8587 192.0635 6.666667 11.56966 133.8571 5.952381
Senegal 17.12605 293.3016 6.507937 5.928141 35.14286 2.809524
Serbia 10.56123 111.5397 3.47619 23.95929 574.0476 8.666667
Seychelles 13.68118 187.1746 5.936508 16.06831 258.1905 7.047619
Sierra Leone 16.17881 261.754 4.97619 17.98611 323.5 8.02381
Singapore 4.708149 22.16667 1.642857 6.559254 43.02381 2.738095
Sint Maarten 0 0 0 0 0 0
Slovakia 18.30973 335.246 7.357143 12.3645 152.881 4.738095
Slovenia 16.02379 256.7619 5.571429 16.71754 279.4762 6.285714
Solomon Islands 0 0 0 0 0 0
Somalia 7.06433 49.90476 3.285714 21.97726 483 9.571429
South Africa 4.974538 24.74603 2.761905 8.745067 76.47619 3.809524
South Korea 10.1848 103.7302 4.698413 8.807464 77.57143 4.952381
South Sudan 15.41799 237.7143 5.079365 14.0153 196.4286 7.095238
Spain 2.817181 7.936508 1.15873 1.812654 3.285714 0.809524
Sri Lanka 19.99127 399.6508 7.888889 12.70171 161.3333 4.952381
St. Barthe_lemy 0 0 0 0 0 0
St. Helena 20.96747 439.6349 11.09524 20.79034 432.2381 9.857143
St. Kitts & Nevis 0 0 0 0 0 0
St. Lucia 6.988653 48.84127 2.619048 22.619 511.619 6.952381
St. Martin 0 0 0 0 0 0
St. Pierre & Miquelon 0 0 0 0 0 0
St. Vincent & Grenadines 11.6046 134.6667 2.84127 15.86551 251.7143 4.190476
Sudan 20.89182 436.4683 9.452381 14.86046 220.8333 8.261905
Suriname 0 0 0 0 0 0
Sweden 13.94661 194.5079 4.650794 9.795529 95.95238 2.761905
Switzerland 14.39246 207.1429 4.111111 14.48973 209.9524 3.857143
Syria 0 0 0 0 0 0
Taiwan 12.05543 145.3333 5.31746 2.581989 6.666667 1.238095
Tajikistan 0 0 0 0 0 0
Tanzania 20.46949 419 6.968254 26.45301 699.7619 13.19048
Thailand 2.603417 6.777778 1.015873 2.78602 7.761905 1.190476
Timor-Leste 0 0 0 0 0 0
Togo 15.50627 240.4444 5.714286 26.70741 713.2857 11.7619
Trinidad & Tobago 11.38294 129.5714 4.428571 33.98669 1155.095 16.71429
Türkiye 2.134375 4.555556 0.968254 2.845213 8.095238 1.190476
Tunisia 17.81341 317.3175 6.47619 22.72192 516.2857 7.333333
Turkmenistan 0 0 0 0 0 0
Turks & Caicos Islands 0 0 0 0 0 0
U.S. Virgin Islands 0 0 0 0 0 0
Uganda 16.43216 270.0159 6.920635 29.3428 861 12.52381
Ukraine 7.148648 51.10317 2.849206 6.269731 39.30952 2.642857
United Arab Emirates 13.60964 185.2222 6.984127 10.91962 119.2381 4.380952
United Kingdom 0.629941 0.396825 0.285714 0.899735 0.809524 0.238095
United States 2.33843 5.468254 0.928571 0.46291 0.214286 0.214286
Uruguay 14.211 201.9524 5.285714 10.68154 114.0952 4.904762
Uzbekistan 14.3737 206.6032 3.873016 8.41201 70.7619 3.47619
Vanuatu 0 0 0 0 0 0
Venezuela 9.136531 83.47619 2.301587 5.830952 34 1.809524
Vietnam 2.081666 4.333333 0.857143 2.171241 4.714286 1
Western Sahara 0 0 0 0 0 0
Yemen 0 0 0 0 0 0
Zambia 13.4772 181.6349 5.698413 14.46342 209.1905 6.285714
Zimbabwe 12.54832 157.4603 5.190476 14.32613 205.2381 6.380952
Table 3. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 2 on the master dataset.
Table 3. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 2 on the master dataset.
Country Name RMSE of Autocorrelation (Train Set) MSE of Autocorrelation (Train Set) MAE of Autocorrelation
(Train Set)		 RMSE of Autocorrelation (Test Set) MSE of Autocorrelation (Test Set) MAE of Autocorrelation (Test Set)
Afghanistan 0 0 0 0 0 0
Åland Islands 0 0 0 0 0 0
Albania 9.547236 266.6762 3.449842 16.33022 266.6762 6.070559
Algeria 5.983334 309.8742 3.026799 17.60324 309.8742 8.755387
American Samoa 0 0 0 0 0 0
Andorra 0 0 0 0 0 0
Angola 6.06661 31.05337 3.182088 5.572555 31.05337 3.188894
Antigua & Barbuda 10.53139 29.9032 5.786529 5.468382 29.9032 4.685652
Argentina 5.160407 3.124834 1.915264 1.76772 3.124834 0.977066
Armenia 0 0 0 0 0 0
Aruba 0 0 0 0 0 0
Australia 4.096864 33.99733 2.078919 5.830723 33.99733 2.770755
Austria 10.83433 22.50378 4.518217 4.743815 22.50378 3.749277
Azerbaijan 11.01557 13.87758 3.86089 3.725262 13.87758 3.252013
Bahamas 9.789052 48.23301 5.390787 6.944999 48.23301 4.775422
Bahrain 8.451745 203.8561 6.220559 14.27782 203.8561 9.989248
Bangladesh 4.524827 24.08574 2.864864 4.907722 24.08574 3.710554
Barbados 8.051779 44.85521 2.938033 6.697403 44.85521 2.92712
Belarus 0 0 0 0 0 0
Belgium 8.85279 8.422725 2.9068 2.902193 8.422725 2.344049
Belize 6.027706 22.24202 3.190105 4.716145 22.24202 2.821407
Benin 4.729482 112.321 2.869876 10.59816 112.321 4.86543
Bermuda 0 0 0 0 0 0
Bhutan 7.641408 61.58425 4.080188 7.847563 61.58425 4.348069
Bolivia 8.978993 41.01737 4.208674 6.404481 41.01737 3.756414
Bosnia & Herzegovina 4.779151 28.62655 1.992647 5.350379 28.62655 2.262842
Botswana 12.67149 78.44828 6.246121 8.857103 78.44828 6.110068
Brazil 0.882356 3.935664 0.516444 1.983851 3.935664 0.931681
British Virgin Islands 0 0 0 0 0 0
Brunei 9.480926 308.6751 4.700313 17.56915 308.6751 8.426272
Bulgaria 11.95635 83.69336 6.315804 9.148408 83.69336 6.237365
Burkina Faso 14.31467 41.75848 7.148378 6.46208 41.75848 5.400082
Burundi 7.373451 65.44432 3.422807 8.089767 65.44432 5.022543
Cambodia 11.88999 345.2464 8.282581 18.58081 345.2464 12.45667
Cameroon 4.86618 348.1162 2.915288 18.65787 348.1162 9.084295
Canada 2.264004 0.868871 1.600211 0.932133 0.868871 0.783274
Cape Verde 11.46525 110.7488 5.873995 10.52372 110.7488 6.187318
Cayman Islands 5.534619 22.40385 2.6401 4.73327 22.40385 3.189553
Chad 5.274818 10.57232 2.418889 3.25151 10.57232 1.981079
Chile 1.705141 237.0363 0.797758 15.39598 237.0363 3.170539
China 14.75331 128.8751 8.077444 11.35232 128.8751 7.216554
Côte d’Ivoire 9.282463 432.434 3.350107 20.79505 432.434 11.19471
Colombia 3.824796 1.991984 1.483824 1.411377 1.991984 1.206169
Comoros 4.57128 52.32465 2.782305 7.233578 52.32465 4.655305
Congo - Brazzaville 7.258426 54.2349 3.104642 7.364435 54.2349 3.767057
Congo - Kinshasa 9.466793 17.27012 4.488946 4.155734 17.27012 3.452318
Costa Rica 6.153947 677.8751 3.521392 26.03604 677.8751 14.58405
Croatia 9.937297 322.4361 4.849895 17.95651 322.4361 8.571793
Cuba 9.673677 115.8269 4.788526 10.76229 115.8269 3.96974
Curaçao 0 0 0 0 0 0
Cyprus 10.9392 105.7162 5.734284 10.28184 105.7162 7.269532
Czechia 6.525328 38.41361 2.827252 6.197872 38.41361 3.605674
Denmark 9.850139 56.46845 4.841825 7.514549 56.46845 4.829783
Djibouti 0 0 0 0 0 0
Dominica 0 0 0 0 0 0
Dominican Republic 12.21003 51.48443 5.875481 7.175265 51.48443 5.859984
Ecuador 5.591392 15.12805 2.355274 3.889479 15.12805 2.040708
Egypt 6.149146 45.76967 4.304301 6.765329 45.76967 4.941281
El Salvador 47.795 4031.202 17.61659 63.49175 4031.202 28.50461
Equatorial Guinea 10.05492 185.1663 4.273236 13.60758 185.1663 6.827011
Estonia 9.493435 320.6342 4.461361 17.90626 320.6342 10.35126
Eswatini 10.11582 292.2951 5.50259 17.09664 292.2951 8.906344
Ethiopia 11.90093 255.0395 7.506921 15.96996 255.0395 8.341224
Faroe Islands 0 0 0 0 0 0
Fiji 14.37325 132.6001 7.861227 11.51521 132.6001 7.789546
Finland 5.233477 6.194874 2.325577 2.488951 6.194874 1.932455
France 1.120485 2.003828 0.607629 1.415566 2.003828 0.863699
French Guiana 0 0 0 0 0 0
French Polynesia 0 0 0 0 0 0
Gabon 6.045124 165.8478 3.727497 12.87819 165.8478 7.123294
Gambia 9.52169 71.51402 5.396629 8.456596 71.51402 5.872677
Georgia 12.25343 100.2455 7.121271 10.01227 100.2455 7.364313
Germany 1.575677 2.520911 1.036421 1.587738 2.520911 1.119649
Ghana 8.127446 471.2825 5.614317 21.70904 471.2825 13.16765
Gibraltar 0 0 0 0 0 0
Greece 5.555145 244.8915 2.747845 15.64901 244.8915 7.481647
Greenland 0 0 0 0 0 0
Grenada 12.86441 151.6387 6.159252 12.31417 151.6387 8.262845
Guadeloupe 4.134032 3.222124 1.949929 1.795028 3.222124 1.376126
Guam 0 0 0 0 0 0
Guatemala 9.344646 29.69501 5.26385 5.449313 29.69501 4.369718
Guernsey 0 0 0 0 0 0
Guinea 8.020519 95.65707 4.661525 9.780443 95.65707 5.539834
Guinea-Bissau 11.16234 85.9241 4.971446 9.269526 85.9241 4.876213
Guyana 2.517421 235.7208 0.902204 15.3532 235.7208 3.061355
Haiti 6.585808 6.792683 3.144837 2.606278 6.792683 2.40467
Honduras 9.296952 57.77398 4.400755 7.60092 57.77398 4.649517
Hong Kong 7.353977 41.40477 3.82363 6.434654 41.40477 3.466788
Hungary 5.225407 122.7043 3.140391 11.0772 122.7043 5.309302
Iceland 5.922715 419.9142 3.113922 20.49181 419.9142 8.49435
India 1.289805 0.900497 0.749746 0.948945 0.900497 0.731705
Indonesia 1.103112 0.782783 0.686736 0.88475 0.782783 0.668795
Iran 5.515225 31.68146 2.994191 5.628629 31.68146 3.877756
Iraq 12.55775 87.60657 7.057821 9.359838 87.60657 6.396014
Ireland 0.974491 1.729115 0.547556 1.314958 1.729115 0.70643
Isle of Man 0 0 0 0 0 0
Israel 7.903272 196.6148 4.960509 14.02194 196.6148 8.713525
Italy 3.133917 4.316491 1.612017 2.077617 4.316491 1.404546
Jamaica 4.290459 71.54644 2.75134 8.458513 71.54644 5.632011
Japan 7.735241 106.0027 4.238804 10.29576 106.0027 4.884764
Jersey 0 0 0 0 0 0
Jordan 3.708129 86.17836 1.7919 9.28323 86.17836 4.655009
Kazakhstan 0 0 0 0 0 0
Kenya 5.960261 137.8703 3.875778 11.74182 137.8703 7.767086
Kosovo 6.359459 49.82043 1.686356 7.058359 49.82043 3.073302
Kuwait 10.19731 714.7134 6.009618 26.73413 714.7134 15.32199
Kyrgyzstan 0 0 0 0 0 0
Laos 0 0 0 0 0 0
Latvia 8.898493 233.3365 4.326054 15.27536 233.3365 6.396053
Lebanon 11.15287 53.26224 5.987507 7.298098 53.26224 5.779635
Lesotho 7.575091 401.5749 4.781264 20.03934 401.5749 10.72093
Liberia 7.775482 175.9486 4.44222 13.26456 175.9486 8.237929
Libya 7.048344 111.3744 4.542626 10.55341 111.3744 5.525318
Liechtenstein 0 0 0 0 0 0
Lithuania 9.031481 24.0281 3.696522 4.901846 24.0281 2.652109
Luxembourg 5.203751 584.4337 2.55309 24.17506 584.4337 11.75841
Macao 16.37218 626.3795 7.214573 25.02758 626.3795 12.00941
Madagascar 8.056012 373.3813 3.166608 19.32308 373.3813 9.879311
Malawi 9.456212 214.1751 4.590584 14.63472 214.1751 6.548388
Malaysia 3.027362 14.55967 1.985457 3.815713 14.55967 2.213564
Maldives 11.05308 538.4476 5.323819 23.20447 538.4476 13.88415
Mali 7.121835 306.3747 4.064986 17.50356 306.3747 11.27971
Malta 9.056621 31.83283 4.657467 5.642059 31.83283 4.712883
Martinique 9.748098 689.4085 5.245546 26.25659 689.4085 15.4876
Mauritania 13.13171 88.15271 7.200627 9.388968 88.15271 7.662908
Mauritius 8.761748 453.3572 3.836104 21.29219 453.3572 11.63681
Mexico 2.612147 2.922535 1.126163 1.709542 2.922535 1.043981
Moldova 0 0 0 0 0 0
Mongolia 5.169104 11.07499 2.21608 3.32791 11.07499 2.116271
Montenegro 10.46805 102.2567 4.440561 10.11221 102.2567 6.26818
Morocco 12.27848 241.1229 6.936316 15.52813 241.1229 8.58022
Mozambique 8.961407 35.63636 2.413428 5.969619 35.63636 2.639244
Myanmar (Burma) 2.407638 235.912 1.352414 15.35943 235.912 3.643141
Namibia 7.874785 239.0842 4.49069 15.46235 239.0842 6.310737
Nepal 12.96567 29.01789 5.968387 5.386826 29.01789 3.733779
Netherlands 10.6473 61.49948 4.320568 7.842161 61.49948 4.930311
New Caledonia 0 0 0 0 0 0
New Zealand 5.092617 77.35123 2.885639 8.794955 77.35123 5.253943
Nicaragua 3.552405 1.662006 1.395157 1.289188 1.662006 0.812994
Niger 3.915178 266.4439 1.529212 16.32311 266.4439 4.886317
Nigeria 6.694565 52.00449 4.023198 7.211414 52.00449 4.40873
North Macedonia 10.75917 13.58104 3.545521 3.685246 13.58104 2.548404
Northern Mariana Islands 0 0 0 0 0 0
Norway 5.832329 14.17639 2.866471 3.765155 14.17639 2.452245
Oman 7.612618 479.1339 4.955876 21.88913 479.1339 10.12697
Pakistan 6.305306 46.19294 3.045765 6.796539 46.19294 4.200318
Palestine 0 0 0 0 0 0
Panama 7.976283 265.3628 5.033874 16.28996 265.3628 8.038425
Papua New Guinea 0 0 0 0 0 0
Paraguay 9.36601 264.2494 4.172757 16.25575 264.2494 6.955869
Peru 7.624232 5.534475 3.702979 2.352547 5.534475 2.082666
Philippines 0.885505 3.714283 0.54972 1.927247 3.714283 1.078276
Poland 3.660544 29.904 2.081432 5.468454 29.904 2.813396
Portugal 10.46164 101.0983 5.017028 10.05477 101.0983 5.165927
Puerto Rico 10.56894 16.33975 4.928882 4.042246 16.33975 3.629358
Qatar 11.173 452.4833 6.37943 21.27166 452.4833 10.9481
Réunion 8.809801 73.17947 3.499218 8.554501 73.17947 4.389758
Romania 3.335597 44.26107 2.133118 6.652899 44.26107 3.023861
Russia 2.254237 3.029526 1.0317 1.740553 3.029526 1.230431
Rwanda 11.03336 237.3087 4.619325 15.40483 237.3087 7.975922
Samoa 0 0 0 0 0 0
San Marino 0 0 0 0 0 0
Saudi Arabia 9.002735 77.40342 5.940556 8.797921 77.40342 6.727711
Senegal 11.0033 35.37583 5.909475 5.947758 35.37583 4.661211
Serbia 7.371186 269.5461 3.115696 16.41786 269.5461 6.858306
Seychelles 8.71973 166.6366 5.428664 12.90878 166.6366 8.045354
Sierra Leone 10.5304 186.5661 4.744025 13.65892 186.5661 7.072295
Singapore 3.037643 21.66193 1.64868 4.654238 21.66193 2.354216
Sint Maarten 0 0 0 0 0 0
Slovakia 13.65778 174.2823 7.24118 13.2016 174.2823 6.851287
Slovenia 10.04812 190.0684 4.762657 13.78653 190.0684 7.438249
Solomon Islands 0 0 0 0 0 0
Somalia 5.218272 314.7882 3.14686 17.74227 314.7882 8.515299
South Africa 3.03295 81.68939 2.224909 9.038219 81.68939 5.821945
South Korea 6.544425 42.94735 4.220322 6.553423 42.94735 4.802469
South Sudan 10.60482 110.5208 5.30904 10.51289 110.5208 6.626357
Spain 1.771953 3.135097 0.968608 1.770621 3.135097 1.226833
Sri Lanka 13.79162 122.2438 8.492347 11.05639 122.2438 9.245106
St. Barthélemy 0 0 0 0 0 0
St. Helena 15.04679 340.1374 11.16958 18.44281 340.1374 13.80565
St. Kitts & Nevis 0 0 0 0 0 0
St. Lucia 4.452625 297.2686 2.350638 17.24148 297.2686 7.538465
St. Martin 0 0 0 0 0 0
St. Pierre & Miquelon 0 0 0 0 0 0
St. Vincent & Grenadines 6.327033 278.5168 2.980384 16.68882 278.5168 6.829061
Sudan 13.20677 120.0613 7.874987 10.95725 120.0613 7.766244
Suriname 0 0 0 0 0 0
Sweden 11.12028 58.18556 5.451402 7.627945 58.18556 4.439981
Switzerland 9.620514 106.2319 4.179798 10.30689 106.2319 4.711903
Syria 0 0 0 0 0 0
Taiwan 8.542505 15.32091 4.834299 3.914193 15.32091 3.708639
Tajikistan 0 0 0 0 0 0
Tanzania 13.1928 315.0844 5.843148 17.75062 315.0844 8.659294
Thailand 1.851518 4.061004 0.964724 2.015193 4.061004 1.023623
Timor-Leste 0 0 0 0 0 0
Togo 12.46273 797.6442 6.573539 28.2426 797.6442 16.86038
Trinidad & Tobago 7.231444 603.9144 3.430544 24.57467 603.9144 10.77016
Türkiye 1.404136 3.480567 0.863614 1.865628 3.480567 0.956541
Tunisia 12.71008 298.4444 7.344201 17.27554 298.4444 11.5082
Turkmenistan 0 0 0 0 0 0
Turks & Caicos Islands 0 0 0 0 0 0
U.S. Virgin Islands 0 0 0 0 0 0
Uganda 11.36024 450.5361 6.53962 21.22584 450.5361 8.999745
Ukraine 5.236922 22.31077 2.880307 4.723428 22.31077 3.211289
United Arab Emirates 9.81296 83.8914 6.364535 9.159225 83.8914 6.592083
United Kingdom 0.502379 0.518618 0.357571 0.720152 0.518618 0.395022
United States 2.039879 0.648399 1.119234 0.805232 0.648399 0.716547
Uruguay 11.51783 67.03862 4.861215 8.187712 67.03862 4.772031
Uzbekistan 11.15424 32.51257 3.755985 5.70198 32.51257 3.538633
Vanuatu 0 0 0 0 0 0
Venezuela 6.573665 18.05486 2.575041 4.249101 18.05486 2.422974
Vietnam 1.382188 3.861088 0.888741 1.964965 3.861088 1.055109
Western Sahara 0 0 0 0 0 0
Yemen 0 0 0 0 0 0
Zambia 9.028182 153.7403 5.60782 12.3992 153.7403 7.333309
Zimbabwe 7.700711 137.9502 4.806979 11.74522 137.9502 7.465501
Table 4. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 3 on the master dataset.
Table 4. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 3 on the master dataset.
Country Name RSME for LSTM (Train Set) MSE for LSTM (Train Set) MAE for LSTM (Train Set) RSME for LSTM (Test Set) MSE for LSTM (Test Set) MAE for LSTM (Test Set)
Afghanistan 0 0 0 0 0 0
Åland Islands 0 0 0 0 0 0
Albania 9.7336 94.7436 3.5539 16.6119 275.9568 5.6548
Algeria 6.6306 43.9647 3.5466 15.7074 246.7213 5.0684
American Samoa 0 0 0 0 0 0
Andorra 0 0 0 0 0 0
Angola 6.4459 41.5499 3.3475 2.4405 5.956 2.2836
Antigua & Barbuda 11.0468 122.031 6.0127 4.8425 23.4496 4.6417
Argentina 5.7685 33.2755 2.3629 1.5811 2.4999 1.5477
Armenia 0 0 0 0 0 0
Aruba 0 0 0 0 0 0
Australia 4.1825 17.493 2.2748 5.5779 31.113 2.8582
Austria 11.5956 134.458 4.6221 4.0483 16.3884 3.0249
Azerbaijan 11.5166 132.6323 4.5556 3.4383 11.8218 2.9832
Bahamas 9.9308 98.6212 5.095 6.1092 37.3224 4.3574
Bahrain 8.9706 80.4724 6.1036 11.9741 143.38 7.3561
Bangladesh 4.8026 23.0654 2.796 3.8962 15.1806 2.4461
Barbados 7.2388 52.4007 2.9072 6.7102 45.0273 3.4134
Belarus 0 0 0 0 0 0
Belgium 10.2021 104.0831 3.7289 3.159 9.9791 2.926
Belize 5.5824 31.1636 2.9676 4.4613 19.9031 2.6012
Benin 5.1852 26.8863 3.2811 9.9706 99.4121 4.3851
Bermuda 0 0 0 0 0 0
Bhutan 7.9446 63.1166 4.1197 2.9389 8.6371 2.5615
Bolivia 9.2951 86.3995 4.4767 6.4988 42.2346 4.119
Bosnia & Herzegovina 5.0982 25.9919 1.9803 5.0099 25.0987 1.9786
Botswana 13.3231 177.504 5.5976 5.7334 32.8716 4.4417
Brazil 0.9128 0.8331 0.527 1.8464 3.4091 0.7874
British Virgin Islands 0 0 0 0 0 0
Brunei 9.7336 94.7437 4.1091 7.5546 57.0713 3.8908
Bulgaria 12.4939 156.0973 6.1303 9.1211 83.1944 5.3772
Burkina Faso 15.8841 252.3046 7.2073 5.3209 28.3122 4.2953
Burundi 6.3309 40.08 2.5513 6.8236 46.5612 3.018
Cambodia 11.8045 139.3459 6.8832 9.143 83.6081 7.0224
Cameroon 5.1216 26.2312 2.9666 18.5825 345.3106 7.9996
Canada 2.6211 6.87 1.8806 1.1596 1.3447 1.0067
Cape Verde 12.4699 155.4981 6.69 10.1468 102.9573 6.2734
Cayman Islands 4.6226 21.3684 1.9359 3.6719 13.4827 1.9941
Chad 5.8912 34.7066 2.7076 3.1258 9.7707 2.112
Chile 1.7669 3.122 0.5652 0.3484 0.1214 0.2118
China 15.108 228.2521 8.4072 8.1723 66.7864 6.0264
Colombia 3.9174 15.3456 1.5493 0.9076 0.8236 0.8517
Comoros 4.5292 20.5136 2.1552 4.7915 22.9588 2.7124
Congo - Brazzaville 7.8349 61.3854 3.3439 4.1571 17.2815 2.6733
Congo - Kinshasa 9.666 93.4313 5.0803 4.2111 17.7335 3.7272
Costa Rica 6.258 39.1631 3.4591 18.6752 348.7649 6.6876
Côte d’Ivoire 4.8461 23.4845 1.9603 9.3519 87.4575 3.6104
Croatia 11.0201 121.4419 4.758 16.1631 261.2445 6.0549
Cuba 8.7039 75.7581 4.2015 11.2715 127.0462 4.359
Curaçao 0 0 0 0 0 0
Cyrpus 10.738 115.3048 4.2122 6.805 46.3087 3.9958
Czechia 6.8512 46.9389 2.7323 5.1385 26.4047 2.9119
Denmark 10.105 102.111 4.4315 6.8243 46.5713 3.9834
Djibouti 0 0 0 0 0
Dominica 0 0 0 0 0 0
Dominican Republic 12.6183 159.2206 5.3687 5.3812 28.9569 4.1157
Ecuador 5.7229 32.7513 2.4758 3.9822 15.8582 2.3963
Egypt 6.8698 47.1942 4.7907 5.1174 26.188 4.015
El Salvador 10.4495 109.1914 3.653 2.358 5.5603 2.2743
Equatorial Guinea 10.323 106.5634 4.7069 12.9413 167.4777 5.6865
Estonia 9.6458 93.0424 3.7614 15.7026 246.5723 5.7316
Eswatini 11.5948 134.4386 6.2858 14.3755 206.6537 6.7783
Ethiopia 13.1243 172.2469 7.2049 16.5128 272.6729 8.1253
Faroe Islands 0 0 0 0 0 0
Fiji 14.9577 223.7334 7.9468 9.792 95.8828 6.61
Finland 5.2825 27.9046 2.1186 2.4099 5.8076 1.7092
France 1.1711 1.3714 0.6354 1.4494 2.1007 0.8726
French Guiana 0 0 0 0 0 0
French Polynesia 0 0 0 0 0 0
Gabon 6.2507 39.0707 3.2634 11.2189 125.8636 4.6094
Gambia 9.9696 99.3936 5.6935 8.2451 67.9817 4.8419
Georgia 12.4274 154.4396 5.5388 4.3531 18.9494 3.3823
Germany 1.6134 2.6031 1.0412 1.5203 2.3113 2.3113
Ghana 8.8601 78.5015 5.8293 17.6815 312.6347 8.4393
Gibraltar 0 0 0 0 0 0
Greece 5.5339 30.6236 2.4596 14.0572 197.6036 4.6971
Greenland 0 0 0 0 0 0
Grenada 5.9433 35.3233 3.6672 8.6918 75.5475 4.3353
Guadeloupe 4.5099 20.3389 2.0812 1.7321 3.0003 1.3416
Guam 0 0 0 0 0 0
Guatemala 10.0255 100.5112 4.9873 4.5187 20.4187 3.2909
Guernsey 0 0 0 0 0 0
Guinea 8.7292 76.1994 4.9657 8.9506 80.1132 5.006
Guinea-Bissau 11.9285 142.2889 4.4322 8.488 72.0512 4.0859
Guyana 2.8462 8.1007 1.3192 1.1068 1.2251 1.0212
Haiti 7.169 51.3949 3.4521 2.2058 4.8655 2.136
Honduras 9.5497 91.1969 5.1703 7.8877 62.2158 5.1749
Hong Kong 7.5697 57.3006 3.9035 6.4165 41.1715 3.5521
Hungary 5.3615 28.7455 2.9516 10.839 117.4837 4.988
Iceland 6.1778 38.1655 3.0443 20.0922 403.6946 6.2659
India 1.3647 1.8625 0.7525 0.7327 0.5368 0.5564
Indonesia 1.0416 1.085 0.7564 0.88 0.7743 0.7189
Iran 5.8074 33.7257 2.8256 4.2489 18.0528 2.6769
Iraq 13.019 169.4941 7.7934 9.1199 83.1722 6.6574
Ireland 1.022 1.0444 0.4302 1.319 1.7398 0.5476
Isle of Man 0 0 0 0 0 0
Israel 8.1316 66.1237 5.1369 12.9617 168.0064 7.0347
Italy 3.1791 10.1064 1.6558 2.0621 4.2523 1.433
Jamaica 4.3067 18.5479 2.5403 7.1705 51.4166 3.1201
Japan 8.6611 75.0151 4.0985 9.7806 95.6608 4.3546
Jersey 0 0 0 0 0 0
Jordan 3.8336 14.6967 1.9276 8.9228 79.616 3.7557
Kazakhstan 0 0 0 0 0 0
Kenya 6.2116 38.5844 3.3232 11.6125 134.8494 5.2644
Kosovo 5.8571 34.3056 2.3665 6.9648 48.5085 3.1255
Kuwait 10.7792 116.1902 6.1713 27.6976 767.1596 13.8547
Kyrgyzstan 0 0 0 0 0 0
Laos 0 0 0 0 0 0
Latvia 9.0329 81.5935 4.0577 15.363 236.0222 4.8141
Lebanon 11.8732 140.9722 5.5145 5.7114 32.6202 3.8723
Lesotho 7.8351 61.3887 5.0121 19.4631 378.8121 9.5002
Liberia 8.8601 78.502 4.7636 13.7897 190.1569 7.7805
Libya 7.2756 52.9345 4.8078 10.27 105.4736 5.4583
Liechtenstein 0 0 0 0 0 0
Lithuania 9.4687 89.6554 3.6307 4.7039 22.1269 2.4119
Luxembourg 5.4328 29.5153 2.0352 19.7734 390.9885 8.0012
Macao 10.3247 106.5986 4.4586 5.4317 29.5037 3.4083
Madagascar 8.1859 67.0095 3.0837 17.8244 317.7098 5.9622
Malawi 9.475 89.7765 3.9896 12.203 148.9136 4.5393
Malaysia 3.2457 10.5344 2.1438 3.4703 12.0428 2.018
Maldives 9.0192 81.3456 3.7675 19.1721 367.5694 7.3871
Mali 6.9092 47.7376 3.441 13.9836 195.5414 7.0168
Malta 9.4541 89.3806 4.5121 3.9692 15.7546 3.1479
Martinique 8.9073 79.3393 4.2866 21.8366 476.8377 10.2019
Mauritania 13.8648 192.2316 7.3301 8.6391 74.6336 6.0727
Mauritius 8.7301 76.2139 3.9971 19.6992 388.0597 9.2592
Mexico 2.7721 7.6848 1.1133 1.1494 1.321 0.7958
Moldova 0 0 0 0 0 0
Mongolia 5.6577 32.0097 2.1858 3.2 10.2402 1.7923
Montenegro 9.6568 93.2531 2.8187 6.145 37.7609 2.7985
Morocco 13.3511 178.2515 7.0624 11.0452 121.9971 5.9386
Mozambique 8.3459 69.6548 2.3825 5.9153 34.9914 2.6254
Myanmar (Burma) 2.7862 7.7632 1.6896 1.6621 2.7626 1.3676
Namibia 8.8703 78.6822 4.1666 16.1122 259.6045 5.3507
Nepal 12.7623 162.8764 6.2004 3.956 15.6502 3.4753
Netherlands 10.9354 119.582 4.1819 7.2466 52.513 4.3334
New Caledonia 0 0 0 0 0 0
New Zealand 5.3017 28.1082 2.6547 6.0864 37.0443 2.6479
Nicaragua 2.0237 4.0952 0.976 1.2777 1.6325 0.8717
Niger 3.9517 15.6159 2.3765 16.9702 287.9891 5.9528
Nigeria 7.3737 54.371 3.9092 6.8294 46.6407 3.2942
North Macedonia 10.1685 103.3987 3.4865 3.0711 9.4314 2.3936
Northern Mariana Islands 0 0 0 0 0 0
Norway 6.2307 38.8211 3.1065 3.4664 12.0158 2.4974
Oman 8.5704 73.451 5.2916 20.3843 415.5193 8.2069
Pakistan 6.5964 43.5123 3.1347 4.3147 18.6169 2.693
Palestine 0 0 0 0 0 0
Panama 7.9751 63.6014 4.062 6.3065 39.7718 3.3684
Papua New Guinea 0 0 0 0 0 0
Paraguay 10.0941 101.8901 3.8105 7.7531 60.1103 4.1264
Peru 8.3073 69.0105 3.7563 2.2829 5.2117 2.1975
Philippines 0.9395 0.8827 0.5583 1.7197 2.9573 0.716
Poland 3.8525 14.8416 2.1698 4.8759 23.774 2.8208
Portugal 11.0247 121.5435 4.7544 9.3369 87.177 4.4406
Puerto Rico 10.9783 120.5236 4.8751 3.6483 13.3099 3.5062
Qatar 11.8303 139.9554 6.1867 22.1425 490.2905 9.8335
Réunion 9.4096 88.5414 3.8012 8.316 69.1552 4.3294
Romania 3.64 13.2498 2.1245 6.4228 41.2524 2.6677
Russia 2.6839 7.2032 1.3379 1.3486 1.8187 1.0539
Rwanda 13.5939 184.7951 5.4912 14.0024 196.066 7.3586
Samoa 0 0 0 0 0
San Marino 0 0 0 0 0 0
Saudi Arabia 9.7894 95.8327 6.1933 7.8526 61.6632 5.6638
Senegal 11.5231 132.7812 5.6435 4.8321 23.3488 4.1911
Serbia 7.611 57.927 2.9919 17.0023 289.0791 5.6237
Seychelles 9.7918 95.8801 5.3783 11.0476 122.0504 6.0669
Sierra Leone 11.3299 128.3673 4.4383 12.7055 161.4305 5.8939
Singapore 3.3178 11.0079 1.6058 4.6327 21.4623 2.1795
Sint Maarten 0 0 0 0 0 0
Slovakia 13.6908 187.4372 6.9251 10.8927 118.6518 6.2567
Slovenia 11.0111 121.245 4.6098 16.2637 264.5094 7.3231
Solomon Islands 0 0 0 0 0 0
Somalia 5.5604 30.9179 3.0668 18.3508 336.7535 7.4377
South Africa 3.3346 11.1196 2.3929 7.63 58.2163 3.674
South Korea 6.9248 47.9534 4.1089 6.3652 40.5161 4.3734
South Sudan 12.0022 144.0526 5.4966 10.1429 102.8774 6.1574
Spain 1.9102 3.6489 0.9706 1.5247 2.3247 0.991
Sri Lanka 14.6585 214.872 7.5076 9.0582 82.0501 5.1946
St. Barthélemy 0 0 0 0 0 0
St. Helena 15.9633 254.8255 11.5785 14.6825 215.5751 10.5333
St. Kitts & Nevis 0 0 0 0 0 0
St. Lucia 4.7856 22.9015 2.1552 16.1445 260.6449 4.5544
St. Martin 0 0 0 0 0 0
St. Pierre & Miquelon 0 0 0 0 0 0
St. Vincent & Grenadines 5.2222 27.2719 1.8175 2.3939 5.7306 1.4965
Sudan 14.5817 212.6251 8.5627 10.8392 117.4878 8.11
Suriname 0 0 0 0 0 0
Sweden 11.6771 136.355 4.8831 7.5428 56.8932 3.8568
Switzerland 10.4482 109.1649 4.191 10.3482 107.0847 3.9763
Syria 0 0 0 0 0 0
Taiwan 8.8644 78.5776 5.0119 3.2031 10.2601 3.1236
Tajikistan 0 0 0 0 0 0
Tanzania 14.1549 200.3618 5.935 19.8001 392.0455 9.7499
Thailand 1.9054 3.6305 1.0064 1.9381 3.7561 1.0714
Timor-Leste 0 0 0 0 0 0
Togo 10.8943 118.6865 5.5198 18.8062 353.675 8.7223
Trinidad & Tobago 7.7539 60.1228 3.7991 23.8618 569.3841 10.0506
Tunisia 13.1773 173.6403 6.5249 16.0612 257.9623 6.6638
Türkiye 1.4536 2.1131 0.8458 1.9669 3.8686 0.9816
Turkmenistan 0 0 0 0 0 0
Turks & Caicos Islands 0 0 0 0 0 0
U.S. Virgin Islands 0 0 0 0 0 0
Uganda 11.888 141.3237 6.5723 20.396 415.9966 9.0454
Ukraine 5.6833 32.3001 2.9119 4.5614 20.806 2.7815
United Arab Emirates 10.7911 116.4471 6.5811 8.1413 66.2805 5.1778
United Kingdom 0.5346 0.2858 0.3984 0.7138 0.5095 0.379
Uruguay 11.8371 140.1164 4.9007 7.7883 60.6579 4.3273
USA 2.082 4.3347 1.0967 0.7681 0.59 0.6998
Uzbekistan 10.5539 111.384 3.6693 6.5989 43.5455 3.4136
Vanuatu 0 0 0 0 0 0
Venezuela 6.7539 45.6146 2.3677 4.2025 17.6609 2.055
Vietnam 1.4709 2.1637 0.8434 1.1411 1.3021 0.7516
Western Sahara 0 0 0 0 0 0
Yemen 0 0 0 0 0 0
Zambia 9.7314 94.6992 5.5258 9.9891 99.782 5.2326
Zimbabwe 8.7034 75.7499 4.6912 9.7996 96.0318 5.0848
Table 5. Results of correlation analysis between search interests related to MVD and search interests related to zombies (in the context of MVD-related conspiracy theory) in 216 regions.
Table 5. Results of correlation analysis between search interests related to MVD and search interests related to zombies (in the context of MVD-related conspiracy theory) in 216 regions.
Region Name Pearsons r value Pearsons p-value Nature of correlation
Afghanistan no correlation no correlation not significant
Åland Islands no correlation no correlation not significant
Albania -0.090702335 0.673391 not significant
Algeria 0.063822565 0.767019 not significant
American Samoa no correlation no correlation not significant
Andorra no correlation no correlation not significant
Angola -0.100306446 0.640968 not significant
Antigua & Barbuda -0.149116075 0.486791 not significant
Argentina 0.600519482 0.001917 statistically significant
Armenia no correlation no correlation not significant
Aruba no correlation no correlation not significant
Australia 0.292544706 0.165371 not significant
Austria 0.120643913 0.574431 not significant
Azerbaijan -0.195744703 0.359316 not significant
Bahamas 0.125273682 0.559721 not significant
Bahrain -0.012787181 0.952711 not significant
Bangladesh -0.35785392 0.085994 not significant
Barbados 0.00500911 0.981467 not significant
Belarus no correlation no correlation not significant
Belgium 0.398859048 0.053522 not significant
Belize -0.056937592 0.79158 not significant
Benin -0.104711124 0.626304 not significant
Bermuda no correlation no correlation not significant
Bhutan 0.926431913 8.35E-11 statistically significant
Bolivia 0.052467553 0.807631 not significant
Bosnia & Herzegovina -0.129338946 0.546946 not significant
Botswana -0.088522736 0.680831 not significant
Brazil -0.094590223 0.660194 not significant
British Virgin Islands no correlation no correlation not significant
Brunei 0.100533352 0.640209 not significant
Bulgaria -0.182949839 0.392176 not significant
Burkina Faso -0.032792753 0.879096 not significant
Burundi 0.7706899 1.05E-05 statistically significant
Cambodia 0.179998984 0.399988 not significant
Cameroon -0.159382001 0.456936 not significant
Canada -0.082672028 0.700944 not significant
Cape Verde -0.108891312 0.612513 not significant
Cayman Islands -0.127280178 0.553399 not significant
Chad -0.112659974 0.600189 not significant
Chile -0.16714496 0.435013 not significant
China -0.09808207 0.648424 not significant
Colombia -0.087660028 0.683784 not significant
Comoros 0.138615038 0.518311 not significant
Congo - Brazzaville 0.043158214 0.841295 not significant
Congo - Kinshasa -0.108070629 0.615211 not significant
Costa Rica 0.159105556 0.457727 not significant
Côte d’Ivoire 0.008714964 0.967762 not significant
Croatia -0.23280304 0.27363 not significant
Cuba -0.205104729 0.336332 not significant
Curaçao no correlation no correlation not significant
Cyrpus -0.096785209 0.652786 not significant
Czechia 0.149096414 0.486849 not significant
Denmark 0.075805761 0.724799 not significant
Djibouti no correlation no correlation not significant
Dominica no correlation no correlation not significant
Dominican Republic -0.245334391 0.247889 not significant
Ecuador -0.109050224 0.611992 not significant
Egypt -0.213337626 0.316862 not significant
El Salvador -0.042349142 0.844235 not significant
Equatorial Guinea -0.218142785 0.305823 not significant
Estonia -0.075414291 0.726166 not significant
Eswatini 0.279329839 0.18621 not significant
Ethiopia -0.031797057 0.882742 not significant
Faroe Islands no correlation no correlation not significant
Fiji -0.121750998 0.570898 not significant
Finland 0.209053889 0.326905 not significant
France 0.668053741 0.00036 statistically significant
French Guiana no correlation no correlation not significant
French Polynesia no correlation no correlation not significant
Gabon 0.095426878 0.657366 not significant
Gambia -0.171380952 0.423293 not significant
Georgia 0.362478283 0.08173 not significant
Germany -0.010345017 0.961736 not significant
Ghana 0.414314395 0.044129 statistically significant
Gibraltar no correlation no correlation not significant
Greece -0.156444286 0.46538 not significant
Greenland no correlation no correlation not significant
Grenada -0.127654746 0.552222 not significant
Guadeloupe -0.111315525 0.604574 not significant
Guam no correlation no correlation not significant
Guatemala -0.153540723 0.473804 not significant
Guernsey no correlation no correlation not significant
Guinea -0.088577053 0.680645 not significant
Guinea-Bissau no correlation no correlation not significant
Guyana -0.075872122 0.724567 not significant
Haiti -0.036662844 0.864948 not significant
Honduras -0.10367876 0.629729 not significant
Hong Kong -0.292628068 0.165245 not significant
Hungary 0.066502821 0.757515 not significant
Iceland -0.134859125 0.529818 not significant
India 0.112910195 0.599374 not significant
Indonesia -0.132631908 0.536698 not significant
Iran 0.255540055 0.228129 not significant
Iraq -0.317866272 0.130111 not significant
Ireland 3.47E-18 1 not significant
Isle of Man no correlation no correlation not significant
Israel 0.094336362 0.661052 not significant
Italy 0.20022065 0.348213 not significant
Jamaica 0.257952873 0.223615 not significant
Japan -0.029859044 0.889845 not significant
Jersey no correlation no correlation not significant
Jordan -0.103746534 0.629504 not significant
Kazakhstan no correlation no correlation not significant
Kenya 0.004281525 0.984159 not significant
Kosovo -0.090909091 0.672687 not significant
Kuwait -0.098624292 0.646603 not significant
Kyrgyzstan no correlation no correlation not significant
Laos no correlation no correlation not significant
Latvia -0.082679045 0.70092 not significant
Lebanon 0.850399011 1.42E-07 statistically significant
Lesotho 0.015013135 0.944491 not significant
Liberia -0.139923493 0.514331 not significant
Libya -0.05606639 0.794702 not significant
Liechtenstein no correlation no correlation not significant
Lithuania -0.157291159 0.462937 not significant
Luxembourg -0.075264917 0.726688 not significant
Macao 0.013177024 0.951271 not significant
Madagascar 0.801624529 2.49E-06 statistically significant
Malawi -0.14378595 0.502668 not significant
Malaysia -0.066896998 0.75612 not significant
Maldives -0.027905873 0.897012 not significant
Mali -0.116449557 0.587902 not significant
Malta -0.111690871 0.603348 not significant
Martinique -0.116775918 0.586849 not significant
Mauritania -0.093022948 0.665502 not significant
Mauritius -0.03932881 0.855225 not significant
Mexico -0.083723737 0.697314 not significant
Moldova no correlation no correlation not significant
Mongolia -0.147166174 0.49257 not significant
Montenegro -0.072167714 0.737541 not significant
Morocco -0.046490381 0.829211 not significant
Mozambique -0.020588279 0.923928 not significant
Myanmar (Burma) 0.870771295 3.14E-08 statistically significant
Namibia -0.119343675 0.578592 not significant
Nepal -0.199158241 0.35083 not significant
Netherlands 0.077685891 0.718241 not significant
New Caledonia no correlation no correlation not significant
New Zealand 0.034430784 0.873103 not significant
Nicaragua -0.147146008 0.49263 not significant
Niger -0.104590084 0.626705 not significant
Nigeria 0.370403039 0.074795 not significant
North Macedonia -0.166045252 0.438084 not significant
Northern Mariana Islands no correlation no correlation not significant
Norway -0.316463118 0.13191 not significant
Oman -0.088556261 0.680716 not significant
Pakistan -0.055013307 0.79848 not significant
Palestine no correlation no correlation not significant
Panama -0.093918925 0.662465 not significant
Papua New Guinea no correlation no correlation not significant
Paraguay -0.313893217 0.13525 not significant
Peru 0.415475269 0.04348 statistically significant
Philippines 0.215999599 0.310717 not significant
Poland 0.145549599 0.497387 not significant
Portugal 0.178016266 0.405285 not significant
Puerto Rico 0.170419543 0.425938 not significant
Qatar -0.085169268 0.692335 not significant
Réunion -0.161577247 0.450679 not significant
Romania 0.436293089 0.033055 statistically significant
Russia -0.287145768 0.173678 not significant
Rwanda -0.08690683 0.686366 not significant
Samoa no correlation no correlation not significant
San Marino no correlation no correlation not significant
Saudi Arabia 0.095406704 0.657434 not significant
Senegal 0.073499192 0.732869 not significant
Serbia -0.267267654 0.206747 not significant
Seychelles 0.070774484 0.742439 not significant
Sierra Leone -0.146647907 0.494111 not significant
Singapore 0.04074778 0.850058 not significant
Sint Maarten no correlation no correlation not significant
Slovakia -0.192522789 0.367435 not significant
Slovenia -0.052580012 0.807226 not significant
Solomon Islands no correlation no correlation not significant
Somalia -0.098682014 0.64641 not significant
South Africa -0.515288309 0.009968 statistically significant
South Korea 0.505629707 0.011716 statistically significant
South Sudan -0.103285849 0.631034 not significant
Spain -0.010395398 0.961549 not significant
Sri Lanka -0.316011788 0.132492 not significant
St. Barthélemy no correlation no correlation not significant
St. Helena 0.046822547 0.828009 not significant
St. Kitts & Nevis no correlation no correlation not significant
St. Lucia -0.059897491 0.780996 not significant
St. Martin no correlation no correlation not significant
St. Pierre & Miquelon no correlation no correlation not significant
St. Vincent & Grenadines -0.199562952 0.349832 not significant
Sudan -0.090807231 0.673034 not significant
Suriname no correlation no correlation not significant
Sweden -0.21857412 0.304843 not significant
Switzerland -0.245401511 0.247755 not significant
Syria no correlation no correlation not significant
Taiwan -0.147082649 0.492818 not significant
Tajikistan no correlation no correlation not significant
Tanzania -0.110427608 0.607477 not significant
Thailand 0.069503569 0.746915 not significant
Timor-Leste no correlation no correlation not significant
Togo -0.109324789 0.611091 not significant
Trinidad & Tobago -0.155064952 0.469372 not significant
Tunisia -0.328907162 0.116573 not significant
Türkiye -0.131694408 0.539607 not significant
Turkmenistan no correlation no correlation not significant
Turks & Caicos Islands no correlation no correlation not significant
U.S. Virgin Islands no correlation no correlation not significant
Uganda -0.182196864 0.394161 not significant
Ukraine -0.338520286 0.10565 not significant
United Arab Emirates -0.03805935 0.859852 not significant
United Kingdom 0.110722888 0.606511 not significant
Uruguay 0.632244176 0.000918 statistically significant
USA 0.780639033 6.78E-06 statistically significant
Uzbekistan 0.164119111 0.44349 not significant
Vanuatu no correlation no correlation not significant
Venezuela -0.15483844 0.47003 not significant
Vietnam -0.192602426 0.367233 not significant
Western Sahara no correlation no correlation not significant
Yemen no correlation no correlation not significant
Zambia 0.033333374 0.877117 not significant
Zimbabwe -0.135748266 0.527083 not significant
Table 6. Results of correlation analysis between search interests related to zombies (in the context of MVD-related conspiracy theory) in the United States and remaining and the remaining 215 regions.
Table 6. Results of correlation analysis between search interests related to zombies (in the context of MVD-related conspiracy theory) in the United States and remaining and the remaining 215 regions.
Region Name Pearsons r value Pearsons p-value Nature of correlation
Afghanistan -0.09595 0.6556 not significant
Åland Islands 0.078697 0.714724 not significant
Albania 0.011993 0.955647 not significant
Algeria -0.1165 0.587731 not significant
American Samoa -0.1263 0.556475 not significant
Andorra 0.343671 0.100117 not significant
Angola -0.04035 0.851492 not significant
Antigua & Barbuda 0.088904 0.679529 not significant
Argentina 0.382087 0.065397 not significant
Armenia -0.16178 0.450095 not significant
Aruba 0.014382 0.946823 not significant
Australia -0.3429 0.10093 not significant
Austria 0.046433 0.82942 not significant
Azerbaijan -0.06898 0.748768 not significant
Bahamas 0.124811 0.561182 not significant
Bahrain -0.01622 0.940023 not significant
Bangladesh -0.03106 0.885457 not significant
Barbados -0.08025 0.709316 not significant
Belarus 0.073615 0.732464 not significant
Belgium 0.034153 0.874118 not significant
Belize -0.20214 0.343505 not significant
Benin 0.079631 0.711478 not significant
Bermuda 0.119492 0.578115 not significant
Bhutan -0.02986 0.889843 not significant
Bolivia 0.255697 0.227834 not significant
Bosnia & Herzegovina -0.02618 0.903345 not significant
Botswana 0.005875 0.978264 not significant
Brazil 0.367615 0.077181 not significant
British Virgin Islands 0.042156 0.844936 not significant
Brunei -0.19561 0.359643 not significant
Bulgaria -0.12217 0.569568 not significant
Burkina Faso 0.097461 0.650512 not significant
Burundi 0.005958 0.977956 not significant
Cambodia 0.342958 0.10087 not significant
Cameroon 0.03725 0.862805 not significant
Canada 0.466469 0.021577 statistically significant
Cape Verde 0.096654 0.653228 not significant
Cayman Islands -0.13711 0.522906 not significant
Chad -0.26642 0.20824 not significant
Chile 0.203786 0.339516 not significant
China -0.2226 0.295803 not significant
Colombia -0.04168 0.846681 not significant
Comoros 0.16589 0.438519 not significant
Congo - Brazzaville 0.039259 0.855478 not significant
Congo - Kinshasa -0.01833 0.932257 not significant
Costa Rica -0.04959 0.817994 not significant
Côte d’Ivoire 0.088248 0.681771 not significant
Croatia 0.090166 0.67522 not significant
Cuba 0.024516 0.909469 not significant
Curaçao 0.117277 0.585235 not significant
Cyrpus -0.03672 0.864739 not significant
Czechia -0.18693 0.381775 not significant
Denmark 0.207912 0.329615 not significant
Djibouti -0.03659 0.865206 not significant
Dominica 0.014971 0.944645 not significant
Dominican Republic -0.06974 0.74608 not significant
Ecuador 0.307463 0.143872 not significant
Egypt 0.066507 0.757499 not significant
El Salvador -0.06432 0.765254 not significant
Equatorial Guinea -0.18634 0.383297 not significant
Estonia -0.00644 0.976169 not significant
Eswatini -0.02406 0.911145 not significant
Ethiopia 0.099336 0.644216 not significant
Faroe Islands -0.00035 0.998714 not significant
Fiji -0.17233 0.42068 not significant
Finland 0.073906 0.731445 not significant
France 0.098315 0.647641 not significant
French Guiana -0.05422 0.801314 not significant
French Polynesia -0.16382 0.444329 not significant
Gabon 0.128604 0.549245 not significant
Gambia -0.09829 0.647714 not significant
Georgia 0.062136 0.773018 not significant
Germany 0.120046 0.576343 not significant
Ghana 0.035662 0.868604 not significant
Gibraltar 0.062093 0.773169 not significant
Greece 0.091659 0.670135 not significant
Greenland 0.151738 0.479073 not significant
Grenada -0.16569 0.439078 not significant
Guadeloupe -0.17654 0.409241 not significant
Guam -0.19768 0.354478 not significant
Guatemala -0.08468 0.694017 not significant
Guernsey 0.016417 0.939308 not significant
Guinea 0.093087 0.665285 not significant
Guinea-Bissau no correlation no correlation not significant
Guyana -0.05355 0.803751 not significant
Haiti 0.097893 0.649058 not significant
Honduras -0.0947 0.659824 not significant
Hong Kong -0.42424 0.038813 statistically significant
Hungary 0.107359 0.617554 not significant
Iceland 0.0398 0.853508 not significant
India 0.039949 0.852966 not significant
Indonesia -0.06099 0.777109 not significant
Iran -0.19558 0.359735 not significant
Iraq 0.135739 0.527111 not significant
Ireland 0.051086 0.812607 not significant
Isle of Man 0.073164 0.734045 not significant
Israel -0.09267 0.666686 not significant
Italy 0.07234 0.736935 not significant
Jamaica 0.213237 0.317096 not significant
Japan -0.35339 0.090271 not significant
Jersey 0.16045 0.453885 not significant
Jordan 0.088905 0.679525 not significant
Kazakhstan -0.20714 0.331451 not significant
Kenya 0.018438 0.931856 not significant
Kosovo -0.14557 0.497324 not significant
Kuwait 0.19782 0.354143 not significant
Kyrgyzstan -0.05025 0.815624 not significant
Laos -0.16247 0.44816 not significant
Latvia 0.287447 0.173207 not significant
Lebanon 0.110645 0.606766 not significant
Lesotho 0.06515 0.762308 not significant
Liberia -0.21222 0.319458 not significant
Libya 0.08889 0.679576 not significant
Liechtenstein -0.07501 0.727568 not significant
Lithuania -0.11568 0.590391 not significant
Luxembourg 0.094974 0.658897 not significant
Macao -0.05698 0.791434 not significant
Madagascar -0.09165 0.670158 not significant
Malawi -0.04478 0.835413 not significant
Malaysia -0.3219 0.125036 not significant
Maldives 0.076795 0.721346 not significant
Mali -0.08522 0.692171 not significant
Malta -0.04598 0.831055 not significant
Martinique -0.27954 0.185867 not significant
Mauritania 0.819754 9.51E-07 statistically significant
Mauritius -0.03636 0.86604 not significant
Mexico 0.393424 0.057171 not significant
Moldova -0.03068 0.886823 not significant
Mongolia 0.412104 0.045387 statistically significant
Montenegro -0.09137 0.671121 not significant
Morocco 0.160407 0.454008 not significant
Mozambique -0.17488 0.413754 not significant
Myanmar (Burma) 0.034417 0.873154 not significant
Namibia 0.121531 0.571599 not significant
Nepal -0.11723 0.585373 not significant
Netherlands 0.14285 0.505484 not significant
New Caledonia -0.2661 0.208817 not significant
New Zealand 0.124488 0.562206 not significant
Nicaragua -0.03112 0.885239 not significant
Niger 0.156955 0.463905 not significant
Nigeria 0.217549 0.307175 not significant
North Macedonia 0.173099 0.418589 not significant
Northern Mariana Islands 0.646713 0.000638 statistically significant
Norway 0.08754 0.684195 not significant
Oman -0.03557 0.868938 not significant
Pakistan -0.13549 0.527882 not significant
Palestine 0.098489 0.647058 not significant
Panama 0.168015 0.432593 not significant
Papua New Guinea -0.07654 0.722227 not significant
Paraguay 0.153044 0.475254 not significant
Peru 0.234161 0.270762 not significant
Philippines -0.39349 0.057128 not significant
Poland 0.173083 0.418631 not significant
Portugal 0.143596 0.50324 not significant
Puerto Rico -0.02542 0.906154 not significant
Qatar 0.125201 0.55995 not significant
Réunion 0.054686 0.799656 not significant
Romania 0.025209 0.906921 not significant
Russia -0.05316 0.805152 not significant
Rwanda 0.014844 0.945115 not significant
Samoa -0.28245 0.181134 not significant
San Marino 0.060922 0.777342 not significant
Saudi Arabia -0.03375 0.875606 not significant
Senegal 0.072302 0.737068 not significant
Serbia -0.1206 0.574572 not significant
Seychelles 0.013748 0.949161 not significant
Sierra Leone -0.14807 0.489887 not significant
Singapore 0.255118 0.228925 not significant
Sint Maarten -0.12123 0.572562 not significant
Slovakia 0.048977 0.820217 not significant
Slovenia 0.127292 0.553361 not significant
Solomon Islands 0.013335 0.950688 not significant
Somalia 0.109009 0.612126 not significant
South Africa 0.052403 0.807865 not significant
South Korea 0.368282 0.076606 not significant
South Sudan -0.18285 0.392433 not significant
Spain 0.139112 0.516797 not significant
Sri Lanka -0.05101 0.81287 not significant
St. Barthélemy 0.071335 0.740467 not significant
St. Helena 0.001053 0.996102 not significant
St. Kitts & Nevis -0.05172 0.810321 not significant
St. Lucia -0.24535 0.24786 not significant
St. Martin -0.08901 0.679155 not significant
St. Pierre & Miquelon 0.02774 0.897619 not significant
St. Vincent & Grenadines 0.132365 0.537525 not significant
Sudan -0.11314 0.598624 not significant
Suriname 0.054396 0.800697 not significant
Sweden 0.136607 0.524449 not significant
Switzerland 0.017598 0.934952 not significant
Syria 0.015707 0.941928 not significant
Taiwan 0.4215 0.040229 statistically significant
Tajikistan 0.172028 0.421518 not significant
Tanzania -0.1645 0.442414 not significant
Thailand -0.01067 0.960521 not significant
Timor-Leste 0.5755 0.003256 statistically significant
Togo 0.099706 0.642978 not significant
Trinidad & Tobago 0.137748 0.520958 not significant
Tunisia 0.106702 0.619719 not significant
Türkiye 0.284156 0.178401 not significant
Turkmenistan 0.137217 0.522581 not significant
Turks & Caicos Islands 0.319718 0.127765 not significant
U.S. Virgin Islands 0.06417 0.765787 not significant
Uganda 0.013549 0.949899 not significant
Ukraine 0.089928 0.67603 not significant
United Arab Emirates -0.05261 0.80713 not significant
United Kingdom 0.39561 0.055681 not significant
Uruguay 0.204763 0.337156 not significant
USA -0.42842 0.036735 statistically significant
Uzbekistan -0.07757 0.718644 not significant
Vanuatu 0.110428 0.607474 not significant
Venezuela 0.118984 0.579746 not significant
Vietnam 0.151467 0.479867 not significant
Western Sahara 0.088108 0.68225 not significant
Yemen 0.2617 0.216722 not significant
Zambia -0.07489 0.727997 not significant
Table 7. Representation of regions where there was a positive increase in zombie-related searches (in the context of MVD-related conspiracy theory) between 2 PM and 3 PM (EST) on October 4, 2023.
Table 7. Representation of regions where there was a positive increase in zombie-related searches (in the context of MVD-related conspiracy theory) between 2 PM and 3 PM (EST) on October 4, 2023.
Region Name Search interest at 2 PM Search interest at 3 PM Percentage
increase
Algeria 6 16 166.6667
Argentina 31 39 25.80645
Austria 18 20 11.11111
Belgium 19 22 15.78947
Bolivia 10 22 120
Cambodia 22 77 250
Canada 52 58 11.53846
Costa Rica 5 10 100
Cuba 13 14 7.692308
Denmark 29 33 13.7931
Dominican Republic 4 5 25
Finland 8 9 12.5
France 23 25 8.695652
Greece 8 18 125
Guatemala 4 7 75
Hungary 11 21 90.90909
India 64 70 9.375
Indonesia 62 66 6.451613
Israel 13 16 23.07692
Italy 32 34 6.25
Jersey 2 10 400
Lebanon 6 10 66.66667
Mexico 39 41 5.128205
Morocco 16 19 18.75
Nigeria 31 47 51.6129
Palestine 6 8 33.33333
Poland 39 46 17.94872
Portugal 12 14 16.66667
Qatar 20 23 15
Senegal 8 9 12.5
Slovenia 2 4 100
South Korea 80 87 8.75
Spain 38 41 7.894737
Sri Lanka 22 34 54.54545
Sweden 27 32 18.51852
Switzerland 11 14 27.27273
Taiwan 16 87 443.75
Tunisia 6 11 83.33333
Turks & Caicos Islands 4 7 75
Ukraine 25 28 12
United Kingdom 18 19 5.555556
United States 51 100 96.07843
Uruguay 2 8 300
Vietnam 55 62 12.72727
Zambia 11 14 27.27273
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