Version 1
: Received: 21 June 2024 / Approved: 21 June 2024 / Online: 21 June 2024 (08:54:59 CEST)
How to cite:
Arguedas-Flatts, Y.; Benavides-Murillo, F. Data Analysis of COVID-19 in Costa Rica as a Case Study in Developing Countries. Preprints2024, 2024061493. https://doi.org/10.20944/preprints202406.1493.v1
Arguedas-Flatts, Y.; Benavides-Murillo, F. Data Analysis of COVID-19 in Costa Rica as a Case Study in Developing Countries. Preprints 2024, 2024061493. https://doi.org/10.20944/preprints202406.1493.v1
Arguedas-Flatts, Y.; Benavides-Murillo, F. Data Analysis of COVID-19 in Costa Rica as a Case Study in Developing Countries. Preprints2024, 2024061493. https://doi.org/10.20944/preprints202406.1493.v1
APA Style
Arguedas-Flatts, Y., & Benavides-Murillo, F. (2024). Data Analysis of COVID-19 in Costa Rica as a Case Study in Developing Countries. Preprints. https://doi.org/10.20944/preprints202406.1493.v1
Chicago/Turabian Style
Arguedas-Flatts, Y. and Francisco Benavides-Murillo. 2024 "Data Analysis of COVID-19 in Costa Rica as a Case Study in Developing Countries" Preprints. https://doi.org/10.20944/preprints202406.1493.v1
Abstract
This paper provides a retrospective analysis of published COVID-19 data and focuses on Costa Rica as an example of data analysis and validation in developing countries. The COVID-19 outbreak has highlighted the importance of robust health systems and adequate resources to test and manage diseases. However, due to limited resources and excessive burdens, developing countries face unique challenges in this regard. This makes it difficult to conduct widespread testing. The lack of testing kits, laboratory facilities, trained personnel, and the necessary infrastructure is a significant barrier. Additionally, logistical issues such as transportation and distribution of testing kits in remote areas further complicate the situation. On the other hand, universal healthcare in developing countries, despite the lack of extensive testing, can still provide reliable data on the spread and impact of the disease. Universal healthcare can lead to better records keeping and data collection, as all individuals, regardless of their economic status, have access to medical services. This inclusivity ensures a more comprehensive and representative data set. Therefore, while testing is an important tool in managing the pandemic, universal healthcare systems can still provide valuable insights in its absence. This description can be applied to the Costa Rica COVID-19 scenario: lack of resources for an adequate testing methodology but a universal healthcare system that ensures inclusivity. In this work, we show that the number of active cases of COVID-19 for Costa Rica does not provide reliable information that can be used to adjust the parameters of a mathematical model, so the effectiveness of the contention measures cannot be precisely stated. However, the number of hospitalizations is more reliable in the centralized and universal healthcare system, but quantifying the effectiveness of such measures requires a different set of tools. Traditionally, most pandemic data analysis is based on the estimation of the Rt number based on the number of active cases. We propose a methodology based on digital signal processing principles and elementary differential calculus. To our knowledge, these kinds of methodologies have not been of widespread use in pandemic data analysis, and, as we show, they may provide a valuable descriptive analysis of pandemic tendencies and they are an accessible, fast and reliable decision-making tool.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
