VOLUME 15 NUMBER 1 (January to June 2022)

Jayrold P. Arcede^1,7, Randy L. Caga-anan^2,7, Youcef Mammeri³, Rhoda A.Namoco^4,7, Ian Christian A. Gonzales⁵, Zython Paul Lachica^7,8, and May Anne E. Mata*^6,7,8

¹Department of Mathematics, Caraga State University, Butuan City,
     Philippines
²Department of Mathematics and Statistics,
     MSU-Iligan Institute of Technology, Iligan City, Philippines
³Laboratoire Amiénois de Mathématique Fondamentale et Appliquée,
     CNRS UMR 7352, Universite de Picardie Jules Verne,
     80069 Amiens, France
⁴Department of Applied Mathematics,
     University of Science and Technology of Southern Philippines,
     Cagayan De Oro, Philippines
⁵Center for Health Development Northern Mindanao,
     Department of Health, Cagayan de Oro City, and Philippines
⁶Department of Mathematics, Physics and Computer Science,
     University of the Philippines Mindanao, Davao City, Philippines
⁷Center for Applied Modeling, Data Analytics, and Bioinformatics,
     for Decision Support System in Health, University of the Philippines
     Mindanao, Davao City, Philippines
⁸University of the Philippines Resilience Institute,
     University of the Philippines, Diliman, Diliman, Quezon City, Philippines

The COVID-19 pandemic has severely impacted individuals living in developing countries, including the Philippines. Possibly, COVID-19 might not be the last pandemic that can hit the country hard. To provide timely and evidence-based insights for health policymakers to control the spread of a novel infectious disease that might arise in the future, we proposed a mechanistic model that captures the dynamics of an infectious disease during its early phase, when testing capacities are limited. Specifically, we aimed to understand the COVID-19 dynamics during its early phase by formulating a mechanistic model that uses the monitoring data (e.g. number of PUMs, PUIs from Northern Mindanao, Philippines), which are the available information on hand during that time. Closed-form formulas for the basic and effective reproduction numbers of the model were obtained to gain insights on the transmissibility of COVID-19. Sensitivity analysis was done to identify the epidemiological parameters that significantly affect the disease dynamics. We also provided numerical experiments to simulate COVID-19 dynamics. The results showed that the increasing basic reproduction number and disease transmission rate (from the susceptible to exposed population) were highly correlated. With limited testing capacities and unavailability of vaccines during the early phase of the outbreak, the combination of containment, lockdown, social distancing, and amplified efforts to quarantine exposed individuals can reduce the disease transmission rate. We further highlight that monitoring data, when modeled appropriately, can provide insights that can serve as a guide to our policymakers to craft evidence-based health protocols. Consequently, we note to use appropriate models when laboratory-based disease reports (e.g. those cases identified from RT-PCR tests) are available since the model is tailored fit to an early epidemic.