{"title":"\"What should be computed\" for supporting post-pandemic recovery policymaking? A life-oriented perspective.","authors":"Junyi Zhang, Tao Feng, Jing Kang, Shuangjin Li, Rui Liu, Shuang Ma, Baoxin Zhai, Runsen Zhang, Hongxiang Ding, Taoxing Zhu","doi":"10.1007/s43762-021-00025-8","DOIUrl":null,"url":null,"abstract":"<p><p>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 <i>Computational Urban Science</i> 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.</p>","PeriodicalId":72667,"journal":{"name":"Computational urban science","volume":"1 1","pages":"24"},"PeriodicalIF":2.6000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602982/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational urban science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s43762-021-00025-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/11/19 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
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.