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, Estuar, Ma. Regina Justina, Sarmiento, Raymond Francis R
• Format: text
• Published: Archīum Ateneo 2022
• Subjects: COVID-19 pandemic; Compartmental model; Philippines; Public health; Policy; Epidemiology; Mathematics; Physical Sciences and Mathematics; Public Health
• Online Access: https://archium.ateneo.edu/mathematics-faculty-pubs/214; https://doi.org/10.1016/j.epidem.2022.100599
• 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 ( = 94%–99%, < .001). 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