To design effective policies against COVID-19, there is a need for more evidence-based research. However, associations between actual policies and temporal behavior changes have remained underexplored. To fill this important research gap, a nationwide retrospective life-oriented panel survey on individuals' behavior changes from April to September 2020 was implemented in Japan. Reliability of information sources, risk perceptions, and attitudes toward policymaking were also investigated. Valid data were collected from 2643 respondents residing in different parts of the country. Risks were reported about general infections and public transport use. Attitudes toward policymaking were mainly about policymaking capacity and PASS-LASTING based policy measures. A dynamic structural equation model (DSEM) was developed to quantify dynamic associations between individuals’ behavior changes over time and subjective assessments (i.e., attitudes) of policymaking. Survey results revealed that behavior changes are mostly characterized by avoidance behaviors. Modeling estimation results showed a statistically-significant sequential cause-effect relationship between accumulated behavior changes in the past, subjective factors, and the most recent behavior changes. The most recent behavior changes are mostly affected by accumulated behavior changes in the past. Effects of subjective assessments of policymaking on the most recent behavior changes are significant but moderate. Among attitudes toward policymaking, attitudes toward policymaking capacity are more influential than willingness to follow PASS-LASTING based policy measures. High risks of using public transport are found to significantly influence the most recent behavior changes, together with other risk perception factors. Insights into effective COVID-19 policymaking are summarized.

This study attempts to provide scientifically-sound evidence for designing more effective COVID-19 policies in the transport and public health sectors by comparing 418 policy measures (244 are transport measures) taken in different months of 2020 in Australia, Canada, Japan, New Zealand, the UK, and the US. The effectiveness of each policy is measured using nine indicators of infections and mobilities corresponding to three periods (i.e., one week, two weeks, and one month) before and after policy implementation. All policy measures are categorized based on the PASS approach (P: prepare-protect-provide; A: avoid-adjust; S: shift-share; S: substitute-stop). First, policy effectiveness is compared between policies, between countries, and over time. Second, a dynamic Bayesian multilevel generalized structural equation model is developed to represent dynamic cause-effect relationships between policymaking, its influencing factors and its consequences, within a unified research framework. Third, major policy measures in the six countries are compared. Finally, findings for policymakers are summarized and extensively discussed.

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The COVID-19 pandemic has caused various impacts on people’s lives, while changes in people’s lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people’s lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people’s lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about “what should be computed?” in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.

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