Date of Publication
7-11-2022
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Statistics Major in Actuarial Science
Subject Categories
Applied Mathematics | Earth Sciences | Physical Sciences and Mathematics
College
College of Science
Department/Unit
Mathematics and Statistics Department
Thesis Advisor
Vio Jianu Mojica
Defense Panel Chair
Karl Man Collado
Defense Panel Member
Rechel G. Arcilla
Abstract/Summary
Spatiotemporal analysis on the recent Coronavirus disease (COVID-19) pandemic is deemed important in policy-making to alleviate the risks of an outbreak. The data from March 15, 2020 to March 15, 2022 was visualized through choropleth maps. Analysis was done using the Moran’s I statistic, logistic growth model, and negative binomial space-time scan statistic to identify and explain COVID-19 patterns in the Philippines and National Capital Region (NCR). Spatial autocorrelation in the Philippines per province was higher compared to NCR per city. A classical logistic model provided a good fit for COVID-19 counts in the Philippines for the whole period and in NCR, aggregated by quarantine classifications. The negative binomial scan statistic found 107 significant hotspot clusters, areas that reported a sudden increase in relative risk as compared to their baseline period, in the Philippines and 37 in NCR that existed during the time period. Future epidemiological research could apply or advance the analyses done in this study by considering other related factors. Proactive development of policies with the use of these studies would be quintessential.
Abstract Format
html
Language
English
Format
Electronic
Physical Description
33 leaves
Keywords
COVID-19 (Disease)--Philippines; Statistics
Recommended Citation
Bernardo, L. B., Lim, A. B., & Ramos, M. D. (2022). Analyzing spatiotemporal patterns of COVID-19 in the Philippines. Retrieved from https://animorepository.dlsu.edu.ph/etdb_math/11
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Embargo Period
7-11-2022