Responses to COVID-19 with probabilistic programming
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 govern...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-11-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.953472/full |
| _version_ | 1811219147171823616 |
|---|---|
| author | Assem Zhunis Assem Zhunis Tung-Duong Mai Tung-Duong Mai Tung-Duong Mai Sundong Kim Sundong Kim |
| author_facet | Assem Zhunis Assem Zhunis Tung-Duong Mai Tung-Duong Mai Tung-Duong Mai Sundong Kim Sundong Kim |
| author_sort | Assem Zhunis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T07:21:28Z |
| format | Article |
| id | doaj.art-49173939f373446087f42ea5e704b5bf |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T07:21:28Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-49173939f373446087f42ea5e704b5bf2022-12-22T03:42:18ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-11-011010.3389/fpubh.2022.953472953472Responses to COVID-19 with probabilistic programmingAssem Zhunis0Assem Zhunis1Tung-Duong Mai2Tung-Duong Mai3Tung-Duong Mai4Sundong Kim5Sundong Kim6School of Computing, KAIST, Daejeon, South KoreaData Science Group, Institute for Basic Science, Daejeon, South KoreaSchool of Computing, KAIST, Daejeon, South KoreaData Science Group, Institute for Basic Science, Daejeon, South KoreaSamsung Electronics, Seoul, South KoreaData Science Group, Institute for Basic Science, Daejeon, South KoreaAI Graduate School, GIST, Gwangju, South KoreaThe 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.https://www.frontiersin.org/articles/10.3389/fpubh.2022.953472/fullCOVID-19probabilistic programmingSEIRD modelnon-pharmaceutical interventionsimulationeconomic impact |
| spellingShingle | Assem Zhunis Assem Zhunis Tung-Duong Mai Tung-Duong Mai Tung-Duong Mai Sundong Kim Sundong Kim Responses to COVID-19 with probabilistic programming Frontiers in Public Health COVID-19 probabilistic programming SEIRD model non-pharmaceutical intervention simulation economic impact |
| title | Responses to COVID-19 with probabilistic programming |
| title_full | Responses to COVID-19 with probabilistic programming |
| title_fullStr | Responses to COVID-19 with probabilistic programming |
| title_full_unstemmed | Responses to COVID-19 with probabilistic programming |
| title_short | Responses to COVID-19 with probabilistic programming |
| title_sort | responses to covid 19 with probabilistic programming |
| topic | COVID-19 probabilistic programming SEIRD model non-pharmaceutical intervention simulation economic impact |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.953472/full |
| work_keys_str_mv | AT assemzhunis responsestocovid19withprobabilisticprogramming AT assemzhunis responsestocovid19withprobabilisticprogramming AT tungduongmai responsestocovid19withprobabilisticprogramming AT tungduongmai responsestocovid19withprobabilisticprogramming AT tungduongmai responsestocovid19withprobabilisticprogramming AT sundongkim responsestocovid19withprobabilisticprogramming AT sundongkim responsestocovid19withprobabilisticprogramming |