Analytics with Robust Epidemiological Compartmental Optimization Models

Abstract

During pandemics, policymakers must make critical decisions about public health interventions and allocations of scarce resources in response to rapidly evolving diseases under high levels of uncertainty. Epidemiological models, such as the SEIR-type compartmental model, are indispensable tools for predicting how a pandemic may spread over time and how different public health interventions could affect the outcome. Based on such predictions, deterministic compartmental optimization models can be adopted to attain effective public health intervention decisions. However, deterministic models often neglect parameter uncertainty and the risks inherent in the stochastic compartment dynamics, leading to less robust solutions. To address these issues, we have developed an epidemiological analytics framework based on stochastic compartmental models. We introduce a robust epidemiological optimization model that lexicographically minimizes the ambiguity tolerances associated with violating healthcare resource constraints. Leveraging the asymptotic Gaussian property, we employ Gaussian approximation to enhance the efficiency of evaluating robust epidemiological constraints. To streamline and automate its application for practitioners and policymakers, we develop a Python-based Robust Epidemiological AnaLytics Modeling (REALM) toolkit. Employing real-world data from Singapore and Maryland, we implement our modeling framework and toolkit in two case studies. We delve into various resource management scenarios, including testing, bed, and vaccine capacity allocations. Our numerical results showcase that our robust epidemiological analytics models consistently outperform benchmark models, particularly in the number of hospitalized cases and deaths, given healthcare resource capacity constraints.

Note: Funding: There is no funding to be reported for this research.

Declaration of Interests: The authors have no competing interest in this research.

Keywords: COVID-19, epidemiological model, riskiness index, robust optimization, vaccine allocation