Robust Epidemiological Analytics
55 Pages Posted: 23 Jul 2021 Last revised: 5 Oct 2022
Date Written: October 2, 2022
During pandemics, policymakers must make critical decisions over 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 compartmental model, are indispensable tools for predicting how a pandemic may spread over time and how different public health interventions could affect the outcome. However, deterministic compartmental models do not account for the uncertainty associated with the model parameters nor reflect the stochastic nature of infection growth rates. Hence, optimizing these models may not yield the desired optimal outcomes under the impact of risk and uncertainty. To address these issues, we develop a robust epidemiological compartmental model, which provides prediction intervals specified by an ambiguity tolerance parameter that quantifies the acceptable tolerance level for violating the intervals under risk and uncertainty. We then propose a robust epidemiological optimization model that lexicographically minimizes the ambiguity tolerances for violating healthcare resource constraints under ambiguity. We develop a python-based Robust Epidemiological AnaLytics Modeling (REALM) toolkit, which facilitates modeling and solving robust epidemiological optimization problems. The case study illustrates how we can apply the robust epidemiological modeling framework. Using real-world data from Singapore to calibrate epidemiological parameters of the model, we derive optimal bed-capacity and vaccination decisions to inform policy decision-makers. Numerical results demonstrate that our robust epidemiological analytics models yield solutions that outperform the benchmark models in satisfying healthcare resource capacity constraints, such as controlling the number of infections and deaths.
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
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