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

机译:重新思考政府信息干预在Covid-19大流行中的作用:基于代理的建模分析

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摘要

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.
机译:Covid-19大流行对各国政府在干预的能力产生了新的挑战,并降低了感染爆发的风险。为了揭示政府干预决定背后的复杂性,我们为介入的信息疾病动态构建了一个双层网络扩散模型,并开展了全部空间模拟,以说明各国信息披露和阻塞之间面临的权衡。仿真结果表明,当对病毒的高级别医学识别和高公共卫生意识存在高级别的医学识别时,政府在披露速度上优先考虑所公开的信息的准确性,而对于相反的情况,优选更严格的信息阻塞。此外,一个不负责任的政府倾向于延迟披露,风险厌恶政府更加倾向于完全阻止,政府信誉低,将折扣披露和加剧情况的信息效果。这些发现表明,信息干预对于含有传染病的爆发是不可或缺的,但其有效性取决于外部社会/疫情因素和政府内部偏好和治理能力的复杂方式,在此期间需要更彻底的调查未来。

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