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Responses to COVID-19 with probabilistic programming

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Title
Responses to COVID-19 with probabilistic programming
Author(s)
Assem Zhunis; Tung-Duong Mai; Sundong Kim
Publication Date
2022-11
Journal
Frontiers in Public Health, v.10
Publisher
Frontiers Media S.A.
Abstract
The COVID-19 pandemic left its unique mark on the twenty-first century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major initial non-pharmaceutical interventions. We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96% along with a 98% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination. Copyright © 2022 Zhunis, Mai and Kim.
URI
https://pr.ibs.re.kr/handle/8788114/12546
DOI
10.3389/fpubh.2022.953472
ISSN
2296-2565
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > 1. Journal Papers (저널논문)
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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