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Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis

The COVID-19 pandemic imposes new challenges on the capability of governments in intervening with the information dissemination and reducing the risk of infection outbreak. To reveal the complexity behind government intervention decision, we build a bi-layer network diffusion model for the informati...

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Detalles Bibliográficos
Autores principales: Lu, Yao, Ji, Zheng, Zhang, Xiaoqi, Zheng, Yanqiao, Liang, Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795931/
https://www.ncbi.nlm.nih.gov/pubmed/33379205
http://dx.doi.org/10.3390/ijerph18010147
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author Lu, Yao
Ji, Zheng
Zhang, Xiaoqi
Zheng, Yanqiao
Liang, Han
author_facet Lu, Yao
Ji, Zheng
Zhang, Xiaoqi
Zheng, Yanqiao
Liang, Han
author_sort Lu, Yao
collection PubMed
description The COVID-19 pandemic imposes new challenges on the capability of governments in intervening with the information dissemination and reducing the risk of infection outbreak. To reveal the complexity behind government intervention decision, we build a bi-layer network diffusion model for the information-disease dynamics that were intervened in and conduct a full space simulation to illustrate the trade-off faced by governments between information disclosing and blocking. The simulation results show that governments prioritize the accuracy of disclosed information over the disclosing speed when there is a high-level medical recognition of the virus and a high public health awareness, while, for the opposite situation, more strict information blocking is preferred. Furthermore, an unaccountable government tends to delay disclosing, a risk-averse government prefers a total blocking, and a low government credibility will discount the effect of information disclosing and aggravate the situation. These findings suggest that information intervention is indispensable for containing the outbreak of infectious disease, but its effectiveness depends on a complicated way on both external social/epidemic factors and the governments’ internal preferences and governance capability, for which more thorough investigations are needed in the future.
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spelling pubmed-77959312021-01-10 Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis Lu, Yao Ji, Zheng Zhang, Xiaoqi Zheng, Yanqiao Liang, Han Int J Environ Res Public Health Article The COVID-19 pandemic imposes new challenges on the capability of governments in intervening with the information dissemination and reducing the risk of infection outbreak. To reveal the complexity behind government intervention decision, we build a bi-layer network diffusion model for the information-disease dynamics that were intervened in and conduct a full space simulation to illustrate the trade-off faced by governments between information disclosing and blocking. The simulation results show that governments prioritize the accuracy of disclosed information over the disclosing speed when there is a high-level medical recognition of the virus and a high public health awareness, while, for the opposite situation, more strict information blocking is preferred. Furthermore, an unaccountable government tends to delay disclosing, a risk-averse government prefers a total blocking, and a low government credibility will discount the effect of information disclosing and aggravate the situation. These findings suggest that information intervention is indispensable for containing the outbreak of infectious disease, but its effectiveness depends on a complicated way on both external social/epidemic factors and the governments’ internal preferences and governance capability, for which more thorough investigations are needed in the future. MDPI 2020-12-28 2021-01 /pmc/articles/PMC7795931/ /pubmed/33379205 http://dx.doi.org/10.3390/ijerph18010147 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lu, Yao
Ji, Zheng
Zhang, Xiaoqi
Zheng, Yanqiao
Liang, Han
Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis
title Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis
title_full Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis
title_fullStr Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis
title_full_unstemmed Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis
title_short Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis
title_sort re-thinking the role of government information intervention in the covid-19 pandemic: an agent-based modeling analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795931/
https://www.ncbi.nlm.nih.gov/pubmed/33379205
http://dx.doi.org/10.3390/ijerph18010147
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