Policy-Driven Mathematical Modeling for COVID-19 Pandemic Response in the Philippines

• Main Authors: De Lara-Tuprio, Elvira P, Estadilla, Carlo Delfin S, Macalalag, Jay Michael R, Teng, Timothy Robin Y, Uyheng, Joshua, Espina, Kennedy E, Pulmano, Christian E, Estuar, Ma. Regina Justina E, Sarmiento, Raymond Francis R
• Format: text
• Published: Archīum Ateneo 2022
• Subjects: COVID-19 pandemic; Compartmental model; Philippines; Public health policy; Mathematics; Public Health
• Online Access: https://archium.ateneo.edu/mathematics-faculty-pubs/205; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212903/
• Institution: Ateneo De Manila University

Around the world; disease surveillance and mathematical modeling have been vital tools for government responses to the COVID-19 pandemic. In the face of a volatile crisis; modeling efforts have had to evolve over time in proposing policies for pandemic interventions. In this paper; we document how mathematical modeling contributed to guiding the trajectory of pandemic policies in the Philippines. We present the mathematical specifications of the FASSSTER COVID-19 compartmental model at the core of the FASSSTER platform; the scenario-based disease modeling and analytics toolkit used in the Philippines. We trace how evolving epidemiological analysis at the national; regional; and provincial levels guided government actions; and conversely; how emergent policy questions prompted subsequent model development and analysis. At various stages of the pandemic; simulated outputs of the FASSSTER model strongly correlated with empirically observed case trajectories (–; ). Model simulations were subsequently utilized to predict the outcomes of proposed interventions; including the calibration of community quarantine levels alongside improvements to healthcare system capacity. This study shows how the FASSSTER model enabled the implementation of a phased approach toward gradually expanding economic activity while limiting the spread of COVID-19. This work points to the importance of locally contextualized; flexible; and responsive mathematical modeling; as applied to pandemic intelligence and for data-driven policy-making in general.