Department of Mathematics, Ateneo de Manila University, Philippines.
Department of Mathematics, Caraga State University, Philippines.
Epidemics. 2022 Sep;40:100599. doi: 10.1016/j.epidem.2022.100599. Epub 2022 Jun 20.
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.
在全球范围内，疾病监测和数学建模一直是政府应对 COVID-19 大流行的重要工具。面对动荡的危机，建模工作必须随着时间的推移不断发展，为大流行干预措施提出政策建议。在本文中，我们记录了数学建模如何为指导菲律宾大流行政策的轨迹做出贡献。我们介绍了 FASSSTER 平台核心的 FASSSTER COVID-19 房室模型的数学规范，该平台是菲律宾使用的基于情景的疾病建模和分析工具包。我们追溯了国家、地区和省级不断演变的流行病学分析如何指导政府行动；相反，新出现的政策问题如何促使随后的模型开发和分析。在大流行的各个阶段，FASSSTER 模型的模拟输出与经验观察到的病例轨迹高度相关（r=94%-99%，p<.001）。随后，模型模拟被用于预测拟议干预措施的结果，包括校准社区隔离水平以及改善医疗保健系统容量。本研究表明，FASSSTER 模型如何能够实施分阶段的方法，逐步扩大经济活动，同时限制 COVID-19 的传播。这项工作指出了本地化、灵活和响应式数学建模的重要性，适用于大流行情报和一般的数据驱动政策制定。
