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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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COVID-19 outbreak: A data-driven optimization model for allocation of patients.

Sobhan Sarkar1, Anima Pramanik2, J Maiti2,3

  • 1Division of Management Science, Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh-EH8 9JS, UK.

Computers & Industrial Engineering
|September 15, 2021
PubMed
Summary

This study presents an optimization model for efficient COVID-19 patient allocation during healthcare resource constraints. The model identifies key factors for effective patient distribution to manage the pandemic response.

Keywords:
COVID-19Compartmental modelData-driven decision makingOptimization modelPareto analysisPatient allocation in India

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Area of Science:

  • Public Health
  • Operations Research
  • Epidemiology

Background:

  • The COVID-19 pandemic has overwhelmed global healthcare systems, leading to resource shortages and delayed patient admissions.
  • This crisis necessitates innovative solutions for managing patient flow and optimizing hospital capacity.

Purpose of the Study:

  • To develop a data-driven optimization model for effective patient allocation in hospitals amidst the COVID-19 pandemic.
  • To identify the most affected cities and critical factors influencing patient distribution.

Main Methods:

  • A compartmental model was developed to characterize COVID-19 spread.
  • Pareto analysis was used to identify highly affected cities.
  • An optimization model was created for patient allocation, validated with Indian city data.
  • Sensitivity analysis was performed to assess model robustness.

Main Results:

  • The study identified ten cities as most affected by COVID-19.
  • The optimization model demonstrated efficient strategies for patient allocation.
  • Key determinants for patient allocation include cooperation, inter-city distances, patient numbers, and hospital bed capacity.

Conclusions:

  • The proposed data-driven model offers effective strategies for optimizing patient allocation in resource-constrained healthcare settings.
  • Understanding key influencing factors is crucial for managing pandemic-related healthcare challenges.