Analyzing the dynamics of COVID-19 transmission in select regions of the Philippines: A modeling approach to assess the impact of various tiers of community quarantines
May Anne E. Mata,
Rey Audie S. Escosio,
El Veena Grace A. Rosero,
Jhunas Paul T. Viernes,
Loreniel E. Anonuevo,
Bryan S. Hernandez,
Joel M. Addawe,
Rizavel C. Addawe,
Carlene P.C. Pilar-Arceo,
Victoria May P. Mendoza,
Aurelio A. de los Reyes, V
Affiliations
May Anne E. Mata
Mindanao Center for Disease Watch and Analytics (DiWA), University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines; Interdisciplinary Applied Modeling (IAM) Laboratory, University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines; Department of Mathematics, Physics, and Computer Science, University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines; University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines; Corresponding author at: Mindanao Center for Disease Watch and Analytics (DiWA), University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines.
Rey Audie S. Escosio
University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines; Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines; Faculdade de Ciências, Universidade de Lisboa, Lisbon, 1749-016, Portugal; BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, 1749-016, Portugal
El Veena Grace A. Rosero
Interdisciplinary Applied Modeling (IAM) Laboratory, University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines; Department of Mathematics, Physics, and Computer Science, University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines
Jhunas Paul T. Viernes
Department of Mathematics and Computer Science, University of the Philippines Baguio, Baguio City, 2600, Philippines
Loreniel E. Anonuevo
Mindanao Center for Disease Watch and Analytics (DiWA), University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines; Mapúa Malayan Colleges Mindanao, Davao City, 8000, Philippines; Mathematics Department, Caraga State University, Ampayon, Butuan City, 8600, Philippines
Bryan S. Hernandez
Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines
Joel M. Addawe
University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines; Department of Mathematics and Computer Science, University of the Philippines Baguio, Baguio City, 2600, Philippines
Rizavel C. Addawe
University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines; Department of Mathematics and Computer Science, University of the Philippines Baguio, Baguio City, 2600, Philippines
Carlene P.C. Pilar-Arceo
University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines; Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines
Victoria May P. Mendoza
University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines; Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines
Aurelio A. de los Reyes, V
Mindanao Center for Disease Watch and Analytics (DiWA), University of the Philippines Mindanao, Tugbok District, Davao City, 8000, Philippines; University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City, 1101, Philippines; Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines
The COVID-19 pandemic has significantly impacted communities worldwide, and effective management strategies are critical to reduce transmission rates and minimize the impact of the disease. In this study, we modeled and analyzed the COVID-19 transmission dynamics and derived relevant epidemiological values for three regions of the Philippines, namely, the National Capital Region (NCR), Davao City, and Baguio City, under different community quarantine implementations. The unique features and differences of these regions-of-interest were accounted for in simulating the disease spread and in estimating key epidemiological parameters fitted to the reported COVID-19 cases. Results support the robustness of the model formulated and provides insights into the effect of the government's implemented intervention protocols. With a forecasting feature, this modeling framework is beneficial for science-based decision support, policy making, and assessment for recent and future pandemics wherever regions-of-interest.
