Graduate School of Economics, Osaka Metropolitan University, Sakai, Osaka, Japan.
PLoS One. 2024 Jul 30;19(7):e0306456. doi: 10.1371/journal.pone.0306456. eCollection 2024.
This study examines people's habituation to COVID-19-related information over almost three years. Using publicly available data from 47 Japanese prefectures, I analyse how human mobility responded to COVID-19-related information, such as the number of COVID-19-infected cases, the declaration of a state of emergency (DSE), and several doses of vaccine using an interactive effects model, which is a type of panel data regression. The results show that Japanese citizens were generally fearful and cautious during the first wave of the unknown infection. As such, a 1% week-on-week increase in the number of infected cases results in a decrease in human mobility by 1.09-percentage-point (pp) week-on-week. However, they gradually became habituated to similar infection information during the subsequent waves, which is reflected in 0.71 pp and 0.29 pp decreases in human mobility in the second and third waves. Nevertheless, the level of habituation decreased in response to the different types of the infection, such as new variants in the fourth wave, with 0.50 pp decrease. By contrast, regarding the DSE, it is more plausible to consider that human mobility responds to varying requests rather than habituate them. Whereas a rapid vaccination program could alleviate people's concerns. I also find spatial spillovers of infection information on human mobility using a spatial weight matrix included in the regression model. However, there is no evidence of DSE or vaccination spatial spillovers, likely because both are valid only in one's own prefecture. The implementation of flexible human mobility control policies by closely monitoring human mobility can prevent excessive or insufficient mobility control requests. Such a flexible policy can efficiently suppress infection spread and prevent economic activity reduction more than necessary. These implications are useful for evidence-based policymaking during future pandemics.
这项研究考察了人们在将近三年的时间里对 COVID-19 相关信息的习惯化程度。利用来自日本 47 个都道府县的公开数据，我使用交互式效应模型（一种面板数据回归类型）分析了人类流动性对 COVID-19 相关信息的反应，如 COVID-19 感染病例数、宣布紧急状态（DSE）以及几剂疫苗。结果表明，日本公民在未知感染的第一波中普遍感到恐惧和谨慎。因此，每周 COVID-19 感染病例数增加 1%，人类流动性每周减少 1.09 个百分点。然而，随着随后几波感染信息的出现，他们逐渐习惯了类似的感染信息，这反映在第二波和第三波中人类流动性每周分别减少了 0.71 个百分点和 0.29 个百分点。然而，针对不同类型的感染，如第四波的新变种，习惯化程度的降低，人类流动性每周减少 0.50 个百分点。相比之下，对于 DSE，更有可能认为人类流动性是对不同请求的反应，而不是习惯它们。而快速疫苗接种计划可以缓解人们的担忧。我还通过在回归模型中包含空间权重矩阵来发现感染信息对人类流动性的空间溢出效应。然而，没有证据表明 DSE 或疫苗接种存在空间溢出效应，这可能是因为它们只在自己的都道府县有效。通过密切监测人类流动性，实施灵活的人类流动性控制政策，可以防止过度或不足的流动性控制请求。这种灵活的政策可以比必要的更有效地抑制感染的传播并防止经济活动的减少。这些启示对于未来大流行期间基于证据的决策制定是有用的。
