TY - JOUR
T1 - Policy-driven mathematical modeling for COVID-19 pandemic response in the Philippines
AU - de Lara-Tuprio, Elvira
AU - Estadilla, Carlo Delfin S.
AU - Macalalag, Jay Michael R.
AU - Teng, Timothy Robin
AU - Uyheng, Joshua
AU - Espina, Kennedy E.
AU - Pulmano, Christian E.
AU - Estuar, Maria Regina Justina E.
AU - Sarmiento, Raymond Francis R.
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/6/25
Y1 - 2022/6/25
N2 - 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 (r=94%–99%, p<.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.
AB - 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 (r=94%–99%, p<.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.
KW - Compartmental model
KW - COVID-19 pandemic
KW - Philippines
KW - Policy
KW - Public health
UR - https://www.scopus.com/pages/publications/85133753713
U2 - 10.1016/j.epidem.2022.100599
DO - 10.1016/j.epidem.2022.100599
M3 - Article (Academic Journal)
C2 - 35763978
AN - SCOPUS:85133753713
SN - 1755-4365
VL - 40
JO - Epidemics
JF - Epidemics
M1 - 100599
ER -
