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A location-allocation model for influenza pandemic outbreaks: A case study in India

Author

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  • Yashoda Devi

    (Indian Institute of Management Kashipur)

  • Sabyasachi Patra

    (Indian Institute of Management Kashipur)

  • Surya Prakash Singh

    (Indian Institute of Technology Delhi)

Abstract

Previous pandemics in 1918, 1957, 1968, and the ongoing COVID-19 pandemic have provided sufficient evidence of health concerns caused by influenza pandemics. The existing health care system is overwhelmed by the surging demand of susceptible and infected individuals due to the COVID-19 outbreak. It is crucial to identify and isolate infected individuals to prevent pandemic spread. Thus, a mixed-integer linear programming model is proposed in this study for the location-allocation of health care facility networks (i.e., temporary testing laboratories). The objective of this study is to ensure that test samples from various geographical locations reach testing laboratories as soon as possible and at minimum cost to ensure timely testing. Hence, the proposed model has two objectives: (i) minimization of the total cost and (ii) minimization of the maximum travel time from a patient node to a testing facility. Furthermore, to prevent capacity underutilization, the capacity of temporary testing laboratories is tailored in the model. A case study in Maharashtra, India, is used to demonstrate the real-life applicability of the proposed model. The study results has interesting implications for decision- and policy-makers.

Suggested Citation

  • Yashoda Devi & Sabyasachi Patra & Surya Prakash Singh, 2022. "A location-allocation model for influenza pandemic outbreaks: A case study in India," Operations Management Research, Springer, vol. 15(1), pages 487-502, June.
    • Handle: RePEc:spr:opmare:v:15:y:2022:i:1:d:10.1007_s12063-021-00216-w
    DOI: 10.1007/s12063-021-00216-w
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    1. Afshin Kordi & Arash Nemati, 2024. "Simultaneous sensitivity analysis of mixed-integer location-allocation models using machine learning tools: cancer hospitals’ network design," Operational Research, Springer, vol. 24(2), pages 1-32, June.

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