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Responses to COVID-19 with probabilistic programming.

Assem Zhunis1,2, Tung-Duong Mai1,2,3, Sundong Kim2,4

  • 1School of Computing, KAIST, Daejeon, South Korea.

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Summary

This study found that combining social distancing with contact tracing effectively reduced COVID-19 transmission by 96%. This strategy also minimized economic and human capital losses by 98%, offering a balanced approach to pandemic response.

Keywords:
COVID-19SEIRD modeleconomic impactnon-pharmaceutical interventionprobabilistic programmingsimulation

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Area of Science:

  • Epidemiology
  • Public Health Policy
  • Economic Impact Analysis

Background:

  • The COVID-19 pandemic necessitated widespread government interventions.
  • Non-pharmaceutical interventions (NPIs) incurred significant economic and human capital costs.
  • Balancing public health protection with economic stability is crucial for effective pandemic strategies.

Purpose of the Study:

  • To quantify the efficiency of initial non-pharmaceutical interventions during the COVID-19 pandemic.
  • To develop a model assessing the trade-offs between public health outcomes and economic costs.
  • To provide an open-source framework for evaluating NPI efficacy.

Main Methods:

  • Utilized a probabilistic programming approach.
  • Developed a generative simulation model incorporating economic and human capital costs.
  • Simulated virus spread and policy impacts across 10 countries.

Main Results:

  • Social distancing combined with contact tracing emerged as the most successful policy.
  • This combination reduced virus transmission rates by 96%.
  • Economic and human capital losses were reduced by 98%.

Conclusions:

  • The integration of social distancing and contact tracing offers an optimal strategy for pandemic management.
  • The developed framework and findings can inform future public health policy decisions.
  • A balanced approach prioritizing both health and economic factors is achievable.