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Artificial Intelligence (AI)-Based Solution to Malaria Fatalities In Africa: An Exploratory Review

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Computer-Based Solutions to Investigate Biological- and Health-Related Problems

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14 July 2023

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18 July 2023

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Abstract
Malaria remains a primary public health challenge in Africa, and there is growing interest in leveraging artificial intelligence (AI) to raise malaria interventions. This research examines the possible influence, challenges, and recommendations for implementing AI-based personalized malaria interventions in Africa. AI offers several opportunities in malaria management, including early detection and prediction of outbreaks, improved diagnosis, personalized interventions, optimal treatment recommendations, surveillance and response, resource optimization, and research innovation. However, the implementation of AI in malaria interventions faces various challenges. These include data availability and quality, infrastructure and resource constraints, contextual relevance and generalizability, ethical and privacy considerations, integration into healthcare workflows, and the need to build trust and acceptance among stakeholders. To address these challenges, it is recommended to strengthen data infrastructure, build local capacity in AI technologies, contextualize AI models to local settings, address ethical considerations, establish monitoring and evaluation frameworks, promote collaboration and knowledge sharing, and secure sustainable funding and long-term commitment. By considering these recommendations, stakeholders can work towards implementing AI-based personalized malaria interventions in Africa that can contribute to improving malaria control outcomes, reducing the weight of the disease, and advancing public health in the region.
Keywords: 
Subject: Computer Science and Mathematics  -   Information Systems

Introduction

Artificial intelligence has revolutionized various sectors, including healthcare [1,2]. These advancements have opened up new possibilities for personalized interventions in malaria control in Africa. With the high prevalence of malaria in Africa and its impact on morbidity and mortality, there is a pressing need for innovative approaches to improve disease diagnosis and management. One promising avenue is the application of AI algorithms to personalize malaria intervention systems [3], taking into account the specific needs and challenges faced in different regions of Africa [4]. AI Algorithms for Optimal Malaria Intervention Strategies, a group of experts in the field has recently used AI approaches to demonstrate success in searching for optimal malaria intervention strategies [5,6]. By leveraging AI algorithms, these experts were able to analyze the dense and varied data on malaria control and identify effective strategies tailored to specific contexts. These AI algorithms consider not only the quantity of data but also its quality and specificity, recognizing that accurate predictive models rely on accurate and relevant input data. The implementation of personalized malaria intervention systems through AI has the potential to significantly impact disease control in Africa [3,7]. By tailoring interventions to the specific needs of different regions, these systems can address existing challenges more effectively [8,9].
They can optimize the allocation of resources, such as mosquito control measures and healthcare services, to areas that are most in need. This targeted approach can help reduce the burden of malaria by improving prevention, diagnosis, and treatment strategies. Moreover, personalized malaria intervention systems can enhance the efficiency of healthcare delivery by prioritizing resources and interventions based on data-driven insights.
They can also contribute to early detection and response efforts, thereby preventing the spread of malaria and reducing its impact on individuals and communities. By leveraging AI algorithms, these systems can analyze large amounts of data in real-time, making it possible to identify patterns and trends that may not be immediately observable to human analysts [10]. Utilizing AI to personalize malaria intervention systems also holds promise for improving disease diagnosis[11].

The Malaria Crisis in Africa

Malaria constitutes one of the major public health crises in Africa [12]. The continent bears the highest burden of malaria cases and deaths worldwide, with sub-Saharan Africa being the most affected region [13].
The application of AI in personalizing malaria intervention systems can be a significant step toward addressing the malaria crisis in Africa. By leveraging AI technologies, such as image processing, deep learning, and machine learning, these personalized systems can automate the detection and diagnosis of malaria in blood smears, as well as classify healthy and infected blood cells [14]. These automated systems have the potential to overcome the limitations of traditional manual methods by providing faster, more accurate, and more efficient results. Moreover, by integrating multiple malaria interventions into these personalized systems, there is a potential to further reduce the prevalence and morbidity of malaria in Africa [15]. By integrating AI into malaria intervention systems, healthcare professionals can prioritize resources and interventions based on data-driven insights, leading to more efficient and effective healthcare delivery[16].
Moreover, some key aspects of the malaria crisis in Africa include the following factors:
  • High prevalence and impact: Malaria is endemic in many African countries, with transmission occurring year-round in most regions. According to the World Health Organization (WHO), Africa accounted for about 94% of global malaria cases and deaths in 2019 [8]. Malaria disproportionately affects vulnerable populations, such as young children and pregnant women, leading to severe illness, hospitalizations, and deaths [8].
  • Socioeconomic burden: Malaria has a significant socioeconomic impact on African countries. It poses a barrier to economic development by reducing productivity, increasing healthcare costs, and straining already fragile healthcare systems [17]. Malaria-related expenses, including treatment, prevention, and lost productivity, place a heavy burden on individuals, families, and communities [18].
  • Limited access to healthcare: Access to quality healthcare services, including malaria prevention, diagnosis, and treatment, remains a challenge in many parts of Africa[17]. Remote and underserved areas often lack adequate healthcare infrastructure [1], trained healthcare personnel, diagnostic facilities, and essential antimalarial drugs. This hampers early detection, prompt treatment, and effective management of malaria cases.
  • Vector resistance and environmental factors: Malaria control efforts face challenges due to the development of mosquito vector resistance to insecticides [19,20], particularly in Africa. This resistance complicates vector control strategies such as indoor residual spraying and insecticide-treated bed nets. Additionally, environmental factors such as climate change and deforestation contribute to the spread of malaria by altering mosquito breeding habitats and transmission patterns [21,22].
  • Limited resources and funding: Many African countries face resource constraints and inadequate funding for malaria control programs [23,24]. This limits their capacity to implement comprehensive prevention and treatment interventions, conduct surveillance, and promote research and innovation. International support and sustained investment are crucial to address the funding gap and strengthen malaria control efforts in Africa.
  • Socio-cultural factors and behavioral challenges: Socio-cultural factors and behavioral practices also influence the malaria crisis in Africa [8]. Lack of awareness, misconceptions, and low health literacy can affect preventive behaviors, such as consistent use of insecticide-treated bed nets and adherence to antimalarial treatment. Cultural beliefs, access to healthcare information, and community engagement play a vital role in combating malaria [25].
However, addressing the malaria crisis in Africa requires a comprehensive and multi-faceted approach. This includes scaling up proven interventions such as insecticide-treated bed nets, indoor residual spraying, prompt diagnosis, and effective treatment with antimalarial drugs. Strengthening healthcare systems, improving access to quality healthcare, promoting research and innovation, and enhancing community engagement are indispensable strategies through the adoption of proactive measures since the active measures have not yielded the desired result.

Use of AI in Disease Intervention Systems

Artificial Intelligence (AI) has emerged as a powerful tool in disease intervention, revolutionizing the way we approach prevention, diagnosis, treatment, and surveillance [26,27]. It is therefore very important to note that while AI shows great promise in disease intervention, it should be integrated into existing healthcare systems ethically and responsibly. Collaboration between AI experts, healthcare professionals, and policymakers is essential to ensure the safe, effective, and equitable deployment of AI technologies for disease intervention [28,29].
However, AI can be applied to disease intervention in the following areas:
  • Early detection and diagnosis: AI algorithms can analyze vast amounts of medical data, including patient records, medical images (such as X-rays, CT scans, and MRIs), and genetic information[30], to aid in early detection and diagnosis of diseases [31]. AI can help healthcare providers identify patterns and markers that may not be easily recognizable by humans [32], leading to earlier intervention and improved patient outcomes.
  • Precision medicine: AI enables personalized treatment approaches by analyzing individual patient data, including genetic information, lifestyle factors, and medical history. This allows for tailored treatment plans and the identification of optimal drug therapies, leading to more effective interventions with reduced side effects [33].
  • Drug discovery and development: AI can accelerate the drug discovery process by analyzing vast amounts of scientific literature, biological data, and clinical trial result[34]s. Machine learning algorithms can identify potential drug targets, predict the effectiveness of drug candidates, and optimize drug design, saving time and resources in the drug development pipeline[35].
  • Epidemic outbreak prediction and control: AI algorithms can analyze various data sources, such as disease surveillance data, social media feeds, and climate information, to predict and track disease outbreaks. This enables early warning systems and targeted interventions to control the spread of diseases, such as influenza, dengue, or Ebola, by optimizing resource allocation and response strategies [36,37].
  • Public health planning and resource allocation: AI can assist public health authorities in planning and allocating resources efficiently. By analyzing population health data, AI algorithms can identify disease hotspots, assess risk factors, and predict healthcare resource needs. This information helps policymakers make informed decisions about resource allocation, intervention strategies, and public health campaigns [38,39].
  • Telemedicine and remote monitoring: AI technologies, combined with telemedicine platforms and wearable devices, enable remote monitoring of patients' health conditions. AI algorithms can analyze real-time patient data, detect abnormalities, and provide timely alerts to healthcare providers. This facilitates early intervention, reduces hospital visits, and improves patient outcomes, particularly for chronic diseases [40].
  • Disease surveillance and tracking: AI-powered systems can analyze large volumes of data from multiple sources, including electronic health records, social media, and environmental sensors, to track the spread of diseases, monitor population health trends, and provide real-time situational awareness to public health agencies[41,42]. This allows for prompt responses and targeted interventions [43].

Personalizing Malaria Interventions with AI

Personalizing malaria interventions using AI can enhance the effectiveness and efficiency of prevention, diagnosis, and treatment strategies [44].
AI algorithms can analyze various data sources, including epidemiological data, climate data, population demographics, and individual health records, to predict an individual's risk of contracting malaria. By considering factors such as location, travel history, environmental conditions, and personal health data, AI models can identify high-risk individuals or populations [45]. This information can guide the targeted deployment of preventive measures, such as the distribution of insecticide-treated bed nets or indoor residual spraying, in areas with the greatest need [46].
Moreso, AI-powered systems can provide personalized recommendations for malaria prevention [45]. By considering individual characteristics, such as age, gender, pregnancy status, and underlying health conditions, AI algorithms can generate tailored guidelines on the appropriate use of bed nets, insect repellents, and other preventive measures [47]. Additionally, AI-powered mobile applications can deliver personalized education and reminders to promote adherence to preventive measures [48].
AI can as well aid in the early detection and diagnosis of malaria by analyzing symptoms, medical history, and diagnostic test results [49]. Machine learning algorithms can be trained on large datasets of malaria-related data to develop predictive models for identifying suspected cases accurately [50]. AI-powered diagnostic tools, such as computer vision algorithms for analyzing blood smears, can assist healthcare providers in regions with limited access to laboratory facilities or expert microscopists [51].
By analyzing patient characteristics, such as age, weight, genetic factors, and drug resistance patterns, AI models can help identify the most effective antimalarial drugs and dosage regimens [52]. This approach can optimize treatment outcomes, minimize adverse effects, and prevent the development of drug resistance [53].
AI can facilitate the monitoring and detection of adverse events associated with antimalarial drugs. By analyzing real-time patient data, including treatment regimens and reported symptoms, AI algorithms can identify potential adverse events and provide early warnings to healthcare providers. This can improve pharmacovigilance and patient safety during malaria treatment [54], [55]. AI-powered systems can also support remote monitoring and follow-up of malaria patients. By integrating with telemedicine platforms and wearable devices, AI algorithms can analyze patient-generated health data, such as temperature, symptoms, and treatment adherence, to detect treatment responses and identify the need for additional interventions [56]. This approach improves patient care, reduces the burden on healthcare facilities, and enables timely interventions for high-risk individuals.
Finally, it is crucial to ensure that the use of AI in personalizing malaria interventions is ethically sound, respects privacy, and promotes equity. Collaboration among AI experts, healthcare professionals, and local communities is vital to develop context-specific and culturally appropriate AI-driven solutions for malaria prevention, diagnosis, and treatment in Africa and other affected regions.

Case Studies: AI-Based Malaria Interventions in Africa

There are already promising case studies of AI-based malaria interventions in Africa that demonstrate the potential impact of personalized systems. For example, researchers in Uganda have developed an AI-based diagnostic tool that uses machine learning algorithms to analyze images of blood samples and accurately detect malaria parasites [57].
This tool significantly reduces the time and expertise required for malaria diagnosis, allowing healthcare providers to provide timely treatment and interventions. Additionally, AI algorithms have been used to identify high-risk areas for malaria transmission by analyzing variables such as climate, population density, and mosquito breeding sites[58,59]. Such AI-based interventions can help target resources and interventions to the areas with the highest risk, leading to more effective prevention and control strategies. Furthermore, AI can also be utilized in the surveillance and monitoring of malaria cases. By analyzing data from various sources such as electronic health records, mobile phone networks, and satellite imagery, AI algorithms can identify patterns and trends in malaria cases that can inform targeted intervention strategies and enable proactive responses to outbreaks [60].
Here are some specific existing malaria intervention systems:
  • Mobile applications for malaria prevention and education: Several mobile applications have been developed that utilize AI to provide malaria prevention information and education to individuals. For example, the Malaria Buddy app developed by the Kenya Medical Research Institute (KEMRI) uses AI algorithms to provide personalized recommendations for malaria prevention and treatment based on user inputs and geolocation data. These apps aim to empower individuals with information and promote behavior change for malaria prevention and control [61,62].
  • MalariaGEN: MalariaGEN is an international research consortium that incorporates AI and genomics to study malaria and develop strategies for its control[50,63]. The consortium collects genetic data from malaria parasites and human populations across Africa and uses AI techniques to analyze this data and gain insights into the genetic factors influencing malaria transmission and drug resistance. This information can guide the development of targeted interventions and inform malaria control policies.
These examples illustrate how AI is being applied to various aspects of malaria intervention, including diagnosis, outbreak prediction, prevention, and genomics research. However, it's important to note that the field of AI-based malaria interventions is dynamic, and there may be ongoing or upcoming initiatives that have not been covered here.
Challenges in Applying AI to Malaria Interventions in Africa
While the application of AI in malaria interventions holds promise, several challenges need to be addressed for successful implementation. Some of the envisaged challenges include the following:
  • Data quality and accessibility: AI algorithms rely on large volumes of high-quality data for training and validation[64]. However, in many malaria-endemic regions, data may be limited, incomplete, or of variable quality. Additionally, data sharing and accessibility among different stakeholders, such as healthcare providers, researchers, and AI developers, can be a challenge [65]. Addressing these issues requires data standardization, data-sharing agreements, and investments in data collection and infrastructure.
  • Bias and fairness: AI algorithms can inherit biases present in the data used for training, leading to biased predictions and recommendations [66,67]. In the context of malaria interventions, biases can arise from disparities in access to healthcare, socioeconomic factors, and regional variations in data representation. It is crucial to mitigate and address biases to ensure fairness and equitable outcomes in AI-based malaria interventions.
  • Contextual relevance: Malaria is a complex disease affected by multiple factors such as geography, climate, local healthcare systems, and cultural practices[68]. AI models developed in one context may not directly translate to another context due to these variations. Adapting AI models and algorithms to specific local conditions and incorporating contextual factors is essential for their effective application in malaria interventions.
  • Limited resources and infrastructure: Many regions affected by malaria face resource constraints and limited healthcare infrastructure. The deployment of AI technologies requires appropriate hardware, software, and technical expertise [69], which may not be readily available in resource-limited settings. Building and maintaining the necessary infrastructure, as well as providing training and technical support, are crucial for the successful application of AI in malaria interventions.
  • Ethical considerations: AI raises ethical concerns related to privacy, consent, and data security [70]. Protecting the privacy of individuals whose data is used in AI models, obtaining informed consent, and ensuring secure storage and processing of data are critical. Additionally, the responsible and transparent use of AI, addressing potential biases, and ensuring accountability in decision-making are ethical considerations that need to be addressed.
  • User acceptance and trust: The acceptance and trust of AI-based interventions among healthcare providers, policymakers, and the community are essential for their successful implementation [71,72] . Building trust requires transparent communication about the capabilities and limitations of AI, addressing concerns related to job displacement, and involving stakeholders in the development and decision-making processes.
Addressing these challenges requires collaboration between AI researchers, healthcare professionals, policymakers, and local communities. It is essential to ensure that AI-based interventions are developed and implemented in a responsible, ethical, and context-specific manner to maximize their potential in malaria control and prevention

Conclusion

AI-based personalized malaria interventions hold great promise in improving the effectiveness of malaria control efforts in Africa. By leveraging AI technologies, healthcare systems can better identify and target high-risk individuals and populations, optimize resource allocation, enhance early detection and prediction of malaria outbreaks, and provide personalized treatment recommendations.
However, the successful implementation of AI-based personalized malaria interventions comes with its share of challenges. These challenges include data availability and quality, infrastructure and resource constraints, contextual relevance and generalizability, ethical and privacy considerations, integration into healthcare workflows, and the need for building trust and acceptance among stakeholders.
Addressing these challenges requires collaborative efforts between AI researchers, healthcare professionals, policymakers, and local communities. It is crucial to ensure the responsible and ethical use of AI, protect data privacy, and involve stakeholders throughout the process to foster trust and acceptance.
Despite these challenges, the potential impact of AI on malaria control in Africa is significant. AI can complement existing strategies, optimize interventions, and enhance the overall effectiveness of malaria control programs. By continuously refining and adapting AI models to local contexts and needs, we can move closer to the goal of reducing the burden of malaria and improving the health outcomes of affected populations in Africa.
As AI technologies continue to advance and more research is conducted in this field, the future of AI-based personalized malaria interventions remains promising. By staying committed to the ethical and responsible implementation of AI, we can harness the full potential of this technology to combat malaria and improve public health in Africa and beyond.

Recommendation

Based on the potential impact and challenges discussed, here are some recommendations for the implementation of AI-based personalized malaria interventions in Africa:
  • Strengthen data infrastructure: Invest in data collection, management, and sharing systems to improve the availability and quality of data for AI models. This includes standardizing data collection methods, ensuring interoperability between different systems, and promoting data-sharing collaborations among stakeholders.
  • Build local capacity: Foster partnerships between AI researchers, healthcare professionals, and local institutions to build local expertise in AI technologies and their application in malaria control. This includes training healthcare workers on AI tools, promoting AI education in academic institutions, and supporting local AI research and development initiatives.
  • Contextualize AI models: Adapt AI models to the local context by considering epidemiological, cultural, and healthcare system factors. Collaborate closely with local stakeholders to ensure that the AI models are relevant, effective, and acceptable within the specific malaria-endemic regions in Africa.
  • Address ethical considerations: Prioritize ethical principles, such as privacy protection, informed consent, and fairness, throughout the implementation of AI-based interventions. Establish clear guidelines and protocols to handle personal health data, ensure transparency in AI algorithms, and engage communities to address concerns and build trust.
  • Establish monitoring and evaluation frameworks: Develop robust monitoring and evaluation frameworks to assess the impact and effectiveness of AI-based personalized malaria interventions. This includes tracking key performance indicators, conducting regular evaluations, and incorporating feedback from healthcare providers and communities to continuously improve the interventions.
  • Promote collaboration and knowledge sharing: Foster collaboration between researchers, policymakers, healthcare providers, and communities to share best practices, lessons learned, and successful case studies in implementing AI-based interventions. This can be facilitated through conferences, workshops, and online platforms for knowledge exchange.
  • Sustainable funding and long-term commitment: Secure sustainable funding for AI-based interventions in malaria control to ensure their long-term implementation and impact. Advocate for continued support from governments, international organizations, and donor agencies to maintain the momentum in leveraging AI technologies for malaria interventions.
By following these recommendations, stakeholders can work towards successfully implementing AI-based personalized malaria interventions in Africa, leading to improved malaria control outcomes, reduced burden of the disease, and better health outcomes for affected populations.

References

  1. Mahajan, T. Vaidya, A. Gupta, S. Rane, and S. Gupta. Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Res. Stat. Treat. 2019, 2, 182–189. [Google Scholar] [CrossRef]
  2. E. J. Emanuel and R. M. Wachter. Artificial intelligence in health care: will the value match the hype? Jama 2019, 321, 2281–2282. [Google Scholar] [CrossRef] [PubMed]
  3. K. Ganasegeran and S. A. Abdulrahman. Artificial intelligence applications in tracking health behaviors during disease epidemics. Ganasegeran and S. A. Abdulrahman. Artificial intelligence applications in tracking health behaviors during disease epidemics. Hum. Behav. Anal. Using Intell. Syst. 2020, 141–155. [Google Scholar]
  4. M. K. Gourisaria, S. Das, R. Sharma, S. S. Rautaray, and M. Pandey. A deep learning model for malaria disease detection and analysis using deep convolutional neural networks. Int. J. Emerg. Technol. 2020, 11, 699–704. [Google Scholar]
  5. J. P. Romero-Leiton and E. Ibargüen-Mondragón. Stability analysis and optimal control intervention strategies of a malaria mathematical model. Appl. Sci. 2019, 21.
  6. K. O. Okosun, O. Rachid, and N. Marcus. Optimal control strategies and cost-effectiveness analysis of a malaria model. BioSystems 2013, 111, 83–101. [Google Scholar] [CrossRef]
  7. Otaigbe. Scaling up artificial intelligence to curb infectious diseases in Africa. Front. Digit. Heal. 2022, 4, 1030427. [Google Scholar] [CrossRef] [PubMed]
  8. N. Ogbaga and J. O. Ugah. A Model of personalized Mobile Persuasive System for Malaria Prevention and Control in Africa. Int. J. Sci. Eng. Res. 2022, 13, 6. [Google Scholar]
  9. P. C. Dykes et al. Pilot testing fall TIPS (tailoring interventions for patient safety): a patient-centered fall prevention toolkit. Jt. Comm. J. Qual. Patient Saf. 2017, 43, 403–413. [Google Scholar]
  10. J. Kietzmann, J. Paschen, and E. Treen. Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. J. Advert. Res. 2018, 58, 263–267. [Google Scholar] [CrossRef]
  11. N. Schwalbe and B. Wahl. Artificial intelligence and the future of global health. Lancet 2020, 395, 1579–1586. [Google Scholar] [CrossRef]
  12. E. Sherrard-Smith et al. The potential public health consequences of COVID-19 on malaria in Africa. Nat. Med. 2020, 26, 1411–1416. [Google Scholar] [CrossRef]
  13. M. Samuel Nkwo, R. M. Samuel Nkwo, R. Orji, I. Ajah, J. Igwe, I. Ogbaga, and C. Chigozie-Okwum. Design Opportunities for Persuasive Mobile Apps to Support Maternal and Child Healthcare and Help-seeking Behaviors. in 3rd African Human-Computer Interaction Conference: Inclusiveness and Empowerment 2021, 33–42.
  14. J. J. Van Bavel et al. National identity predicts public health support during a global pandemic. Nat. Commun. 2022, 13, 517. [Google Scholar] [CrossRef]
  15. J. -L. A. Ndiaye et al. Repeated treatment of recurrent uncomplicated Plasmodium falciparum malaria in Senegal with fixed-dose artesunate plus amodiaquine versus fixed-dose artemether plus lumefantrine: a randomized, open-label trial. Malar. J. 2011, 10, 1–12. [Google Scholar]
  16. N. Bachmann, S. Tripathi, M. Brunner, and H. Jodlbauer. The contribution of data-driven technologies in achieving the sustainable development goals. Sustainability 2022, 14, 2497. [Google Scholar] [CrossRef]
  17. S. Tang et al. Economic burden of malaria inpatients during National Malaria Elimination Programme: estimation of hospitalization cost and its inter-province variation. Malar. J. 2017, 16, 1–10. [Google Scholar]
  18. Degarege, K. Fennie, D. Degarege, S. Chennupati, and P. Madhivanan. Improving socioeconomic status may reduce the burden of malaria in sub Saharan Africa: A systematic review and meta-analysis. PLoS One 2019, 14, e0211205. [Google Scholar] [CrossRef]
  19. P. Mnzava et al. Implementation of the global plan for insecticide resistance management in malaria vectors: progress, challenges and the way forward. Malar. J. 2015, 14, 1–9. [Google Scholar]
  20. H. Ranson and N. Lissenden. Insecticide resistance in African Anopheles mosquitoes: a worsening situation that needs urgent action to maintain malaria control. Trends Parasitol. 2016, 32, 187–196. [Google Scholar] [CrossRef]
  21. L. S. M. Chaves, J. Fry, A. Malik, A. Geschke, M. A. M. Sallum, and M. Lenzen. Global consumption and international trade in deforestation-associated commodities could influence malaria risk. Nat. Commun. 2020, 11, 1258. [Google Scholar] [CrossRef]
  22. Y. A. Afrane, A. K. Githeko, and G. Yan. The ecology of Anopheles mosquitoes under climate change: case studies from the effects of deforestation in East African highlands. Ann. N. Y. Acad. Sci. 2012, 1249, 204–210. [Google Scholar] [CrossRef]
  23. S. Yaya, A. Otu, and R. Labonté. Globalisation in the time of COVID-19: repositioning Africa to meet the immediate and remote challenges. Global. Health 2020, 16, 1–7. [Google Scholar]
  24. R. W. Snow, E. A. Okiro, P. W. Gething, R. Atun, and S. I. Hay. Equity and adequacy of international donor assistance for global malaria control: an analysis of populations at risk and external funding commitments. Lancet 2010, 376, 1409–1416. [Google Scholar] [CrossRef] [PubMed]
  25. N. Ogbaga and I. A. Ajah. Co-Designing a Mobile Intervention to Support the Prevention and Control of Malaria in Low-Middle- Income Communities in Nigeria. Int. J. Inf. Secur. Priv. an Digit. Forensics 2021, 5, 12. [Google Scholar]
  26. M. Patel, M. Surti, and M. Adnan. Artificial intelligence (AI) in Monkeypox infection prevention. J. Biomol. Struct. Dyn. 2022, 1–5.
  27. M. Yagi, K. Yamanouchi, N. Fujita, H. Funao, and S. Ebata. Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning. J. Clin. Med. 2023, 12, 4188. [Google Scholar] [CrossRef]
  28. J. H. Neylan, S. S. Patel, and T. B. Erickson. Strategies to counter disinformation for healthcare practitioners and policymakers. World Med. Heal. Policy 2022, 14, 428–436. [Google Scholar] [CrossRef]
  29. Lauterbach. Artificial intelligence and policy: quo vadis? Digit. Policy, Regul. Gov. 2019, 21, 238–263. [Google Scholar]
  30. M. Siddiq. ML-based Medical Image Analysis for Anomaly Detection in CT Scans, X-rays, and MRIs. Devot. J. Community Serv. 2020, 2, 53–64. [Google Scholar]
  31. M. A. Al-Antari. Artificial Intelligence for Medical Diagnostics—Existing and Future AI Technology! Diagnostics 2023, 13, 688. [Google Scholar] [CrossRef]
  32. Meskó and, M. Görög. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit. Med. 2020, 3, 126. [Google Scholar] [CrossRef]
  33. M. R. Kosorok and E. B. Laber. Precision medicine. Annu. Rev. Stat. its Appl. 2019, 6, 263–286. [Google Scholar] [CrossRef]
  34. R. Gupta, D. Srivastava, M. Sahu, S. Tiwari, R. K. Ambasta, and P. Kumar. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol. Divers. 2021, 25, 1315–1360. [Google Scholar] [CrossRef] [PubMed]
  35. K. -K. Mak, M. K. Balijepalli, and M. R. Pichika. Success stories of AI in drug discovery-where do things stand? Expert Opin. Drug Discov. 2022, 17, 79–92. [Google Scholar] [CrossRef] [PubMed]
  36. L. Zino and M. Cao. Analysis, prediction, and control of epidemics: A survey from scalar to dynamic network models. IEEE Circuits Syst. Mag. 2021, 21, 4–23. [Google Scholar] [CrossRef]
  37. B. Schwartz, L. Billings, and E. M. Bollt. Dynamical epidemic suppression using stochastic prediction and control. Phys. Rev. E 2004, 70, 46220. [Google Scholar] [CrossRef]
  38. J. S. Humphreys. Delimiting ‘rural’: implications of an agreed ‘rurality’index for healthcare planning and resource allocation. Aust. J. Rural Health 1998, 6, 212–216. [Google Scholar] [CrossRef]
  39. M. S. Feldstein, M. A. Piot, and T. K. Sundaresan. Resource allocation model for public health planning: a case study of tuberculosis control. in Resource allocation model for public health planning: a case study of tuberculosis control 1973, 110.
  40. M. J. Field and J. Grigsby. Telemedicine and remote patient monitoring. Jama 2002, 288, 423–425. [Google Scholar] [CrossRef]
  41. S. Bansal, G. Chowell, L. Simonsen, A. Vespignani, and C. Viboud. Big data for infectious disease surveillance and modeling. J. Infect. Dis. 2016, 214, S375–S379. [Google Scholar] [CrossRef] [PubMed]
  42. J. S. Brownstein, B. Rader, C. M. Astley, and H. Tian. Advances in Artificial Intelligence for infectious-disease surveillance. N. Engl. J. Med. 2023, 388, 1597–1607. [Google Scholar] [CrossRef] [PubMed]
  43. E. Aiello, A. Renson, and P. Zivich. Social media-and internet-based disease surveillance for public health. Annu. Rev. Public Health 2020, 41, 101. [Google Scholar] [CrossRef]
  44. et al. The healthier healthcare management models for COVID-19. J. Infect. Public Health 2021, 14, 927–937. [Google Scholar] [CrossRef]
  45. B. Inambao. The additional effect of focal indoor residual spraying on incidence of malaria in a setting with high insecticide treated bed net coverage in Mansa District, Luapula Province.” University of Zambia 2019.
  46. F. O. Okumu and S. J. Moore. Combining indoor residual spraying and insecticide-treated nets for malaria control in Africa: a review of possible outcomes and an outline of suggestions for the future. Malar. J. 2011, 10, 1–13. [Google Scholar]
  47. G. Carrasco-Escobar, M. Moreno, K. Fornace, M. Herrera-Varela, E. Manrique, and J. E. Conn. The use of drones for mosquito surveillance and control. Parasit. Vectors 2022, 15, 473. [Google Scholar]
  48. V. Božić. THE ROLE OF ARTIFICIAL INTELLIGENCE IN INCREASING THE DIGITAL LITERACY OF HEALTHCARE WORKERS AND STANDARDIZATION OF HEALTHCARE. no. April, 1–13, 2023.
  49. K. B. Johnson et al. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 2021, 14, 86–93. [Google Scholar] [CrossRef] [PubMed]
  50. K. Y. Tai and J. Dhaliwal. Machine learning model for malaria risk prediction based on mutation location of large-scale genetic variation data. J. Big Data 2022, 9, 85. [Google Scholar] [CrossRef]
  51. M. Doan and A. E. Carpenter. Leveraging machine vision in cell-based diagnostics to do more with less. Nat. Mater. 2019, 18, 414–418. [Google Scholar] [CrossRef]
  52. R. Rayner et al. Model-informed drug development for anti-infectives: state of the art and future. Clin. Pharmacol. Ther. 2021, 109, 867–891. [Google Scholar] [CrossRef]
  53. S. He, L. G. Leanse, and Y. Feng. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases. Adv. Drug Deliv. Rev. 2021, 178, 113922. [Google Scholar] [CrossRef]
  54. G. N. Norén and I. R. Edwards. Modern methods of pharmacovigilance: detecting adverse effects of drugs. Clin. Med. (Northfield. Il). 2009, 9, 486. [Google Scholar] [CrossRef]
  55. P. J. Pitts, H. Le Louet, Y. Moride, and R. M. Conti. 21st century pharmacovigilance: efforts, roles, and responsibilities. Lancet Oncol. 2016, 17, e486–e492. [Google Scholar] [CrossRef]
  56. L. G. Melstrom, A. S. Rodin, L. A. Rossi, P. Fu Jr, Y. Fong, and V. Sun. Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning. J. Surg. Oncol. 2021, 123, 52–60. [Google Scholar] [CrossRef]
  57. et al. Application of Machine and Deep Learning Algorithms in Optical Microscopic Detection of Plasmodium Parasites: A Malaria Diagnostic Tool for the Future. Photodiagnosis Photodyn. Ther. 2022, 103198. [Google Scholar]
  58. L. J. Ugwu and T. Zewotir. Evaluating the effects of climate and environmental factors on under-5 children malaria spatial distribution using generalized additive models (GAMs). J. Epidemiol. Glob. Health 2020, 10, 304. [Google Scholar] [CrossRef] [PubMed]
  59. M. Javaid, M. S. Sarfraz, M. U. Aftab, Q. uz Zaman, H. T. Rauf, and K. A. Alnowibet. WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases. Int. J. Environ. Res. Public Health 2023, 20, 3740. [Google Scholar] [CrossRef]
  60. Malik and W., A. Khattak. Enhancing Urban Health: Machine Learning Applications in Environmental Managemen. Int. J. Sustain. Infrastruct. Cities Soc. 2023, 8, 1–21. [Google Scholar]
  61. G. Nevill et al. Insecticide-treated bednets reduce mortality and severe morbidity from malaria among children on the Kenyan coast. TroMed. Int. Heal. 1996, 1, 139–146. [Google Scholar]
  62. H. Owano, J. Kiplang’at, and C. Odini. Use of ICTs in Accessing Information by Researchers in Selected Public Research Institutions in Kenya. Odini. Use of ICTs in Accessing Information by Researchers in Selected Public Research Institutions in Kenya. Emerg. Trends Inf. Knowl. Manag. 2014, 3. [Google Scholar]
  63. S. Kirchner, B. J. Power, and A. P. Waters. Recent advances in malaria genomics and epigenomics. Genome Med. 2016, 8, 1–17. [Google Scholar]
  64. S. D. Auger, B. M. Jacobs, R. Dobson, C. R. Marshall, and A. J. Noyce. Big data, machine learning and artificial intelligence: a neurologist’s guide. Pract. Neurol. 2021, 21, 4–11. [Google Scholar]
  65. M. Matheny, S. T. Israni, M. Ahmed, and D. Whicher. Artificial intelligence in health care: The hope, the hype, the promise, the peril. Washington, DC Natl. Acad. Med. 2019, 10.
  66. Manresa-Yee and, S. Ramis. In Assessing gender bias in predictive algorithms using explainable AI. in Proceedings of the XXI International Conference on Human Computer Interaction, 1–8. 2021. [Google Scholar]
  67. N. Norori, Q. Hu, F. M. Aellen, F. D. Faraci, and A. Tzovara. Addressing bias in big data and AI for health care: A call for open science. Patterns 2021, 2.
  68. J. -A. Atkinson, A. Vallely, L. Fitzgerald, M. Whittaker, and M. Tanner. The architecture and effect of participation: a systematic review of community participation for communicable disease control and elimination. Implications for malaria elimination. Malar. J. 2011, 10, 1–33. [Google Scholar]
  69. H. Alami et al. Artificial intelligence and health technology assessment: anticipating a new level of complexity. J. Med. Internet Res. 2020, 22, e17707. [Google Scholar] [CrossRef]
  70. C. Stahl and D. Wright. Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Secur. Priv. 2018, 16, 26–33. [Google Scholar] [CrossRef]
  71. S. Prakash, J. N. Balaji, A. Joshi, and K. M. Surapaneni. Ethical Conundrums in the application of artificial intelligence (AI) in healthcare—a scoping review of reviews. J. Pers. Med. 2022, 12, 1914. [Google Scholar] [CrossRef] [PubMed]
  72. H. Siala and Y. Wang. SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc. Sci. Med. 2022, 296, 114782. [Google Scholar] [CrossRef] [PubMed]
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