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Discrete Stochastic Optimization for Public Health Interventions with Constraints

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Abstract

Many public health threats exist, motivating the need to find optimal intervention strategies. Given the stochastic nature of the threats (e.g., the spread of pandemic influenza, the occurrence of drug overdoses, and the prevalence of alcohol-related threats), deterministic optimization approaches may be inappropriate. In this paper, we implement a stochastic optimization method to address aspects of the 2009 H1N1 and the COVID-19 pandemics, with the spread of disease modeled by the open-source Monte Carlo simulations, FluTE, and Covasim, respectively. Without testing every possible option, the objective of the optimization is to determine the best combination of intervention strategies so as to result in minimal economic loss to society. To reach our objective, this application-oriented paper uses the discrete simultaneous perturbation stochastic approximation method (DSPSA), a recursive simulation-based optimization algorithm, to update the input parameters in the disease simulation software so that the output iteratively approaches minimal economic loss. Assuming that the simulation models for the spread of disease (FluTE for H1N1 and Covasim for COVID-19 in our case) are accurate representations for the population being studied, the simulation-based strategy we present provides decision makers a powerful tool to mitigate potential human and economic losses from any epidemic. The basic approach is also applicable in other public health problems, such as opioid abuse and drunk driving.

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Data Availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

Code Availability

Codes are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Yan Zhou and Mengdan Zhang (the Johns Hopkins University) for all the preliminary work done during the course of this research.

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Authors and Affiliations

  1. Northwestern University, Evanston, USA

    Zewei Li

  2. The Johns Hopkins University Applied Physics Laboratory, Laurel, USA

    James C. Spall

Authors
  1. Zewei Li
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  2. James C. Spall
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Contributions

JS encouraged ZL to investigate the application of stochastic optimization in public health and supervised the findings of this work. Both authors were involved in developing the methods and algorithms. ZL performed the numerical simulations and analysis. Both authors discussed the results. ZL wrote the final manuscript under the guidance of JS.

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Correspondence to James C. Spall.

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Appendix

Appendix

Since all the 20 tracts we picked are located within California State and Los Angeles County, the State FPIS code and county FIPS code are 06 and 037 respectively for all tracts. The tract FIPS codes for the selected 20 tracts are 101110, 101120, 101210, 101220, 101300, 101400, 102101, 102102, 103101, 103102, 103200, 103300, 103400, 104103, 104104, 104105, 104106, 104107, 104201, and 104202.

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Li, Z., Spall, J.C. Discrete Stochastic Optimization for Public Health Interventions with Constraints. Oper. Res. Forum 3, 68 (2022). https://doi.org/10.1007/s43069-022-00176-2

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