Analytics with Robust Epidemiological Compartmental Optimization Models

Abstract

Problem definition: 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 Susceptible-Exposed-Infectious-Recovered-type (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. Methodology/results: To address these issues, we develop an epidemiological analytics framework based on the ambiguity tolerance measure and 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. Managerial implications: Employing real-world data from Singapore, we investigate various resource management scenarios, including testing, bed, and vaccine capacity allocations. Our numerical results showcase that our robust epidemiological analytics models outperform deterministic counterpart benchmarks, particularly in the number of hospitalized cases and deaths, given healthcare resource capacity constraints. The results demonstrate the benefits of accounting for risk and ambiguity in disease propagation when addressing epidemiological optimization models. Funding: The research of C. Fu was supported by the National Natural Science Foundation of China [Grants 72401229, 72310107003, and 72271201]. The research of M. Zhou was supported by the National Natural Science Foundation of China [Grants 72301075 and 72293564/72293560]. The research of J. Xie was supported by the Deutsche Forschungsgemeinschaft [Grant 543063591]. The research of M. Sim was supported by the Ministry of Education, Singapore under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0984 .