{"title":"时间序列新冠肺炎政策结果分析工具，从群体本能的角度衡量人类行为。","authors":"Toshiki Miyagawa, Yoshiyasu Takefuji","doi":"10.1007/s12553-023-00759-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>There are 47 municipalities and prefectures in Japan that operate similar COVID-19 policies in a unified manner. There are significant differences regarding their policy outcomes. In order to investigate when the outcomes are different, we made a COVID-19 policy outcome analysis tool, jpcovid for evaluating time-series scores of individual prefectures, not a policy analysis tool.</p><p><strong>Methods: </strong>Scoring policies is based on a single population mortality metric: the number of COVID-19 deaths divided by the population in millions from a demographic perspective.</p><p><strong>Results: </strong>Although uniformed policies have been adopted by the 47 prefectures in Japan, there are significant differences in the calculated scores among the 47 prefectures. This difference can be caused by differences in the herding instincts of the community with COVID-19 variants. The herd instinct is an inherent tendency to associate with others and follow the group's behavior or a behavior wherein people tend to react to the actions of others without considering the reason. The snapshot scoring tool, jpscore showed that Niigata has the best score of 67.9 while Osaka has the worst score of 727.9. jpcovid allows users to identify when herd instincts made changes in time-series scores.</p><p><strong>Conclusions: </strong>This is the world's first large-scale measurement on the herd instinct of prefectures in Japan. The proposed method can be applied to other countries in general.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s12553-023-00759-x.</p>","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":" ","pages":"1-6"},"PeriodicalIF":3.1000,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252164/pdf/","citationCount":"1","resultStr":"{\"title\":\"A time-series COVID-19 policy outcome analysis tool to measure human behavior from a herd instinct perspective.\",\"authors\":\"Toshiki Miyagawa, Yoshiyasu Takefuji\",\"doi\":\"10.1007/s12553-023-00759-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>There are 47 municipalities and prefectures in Japan that operate similar COVID-19 policies in a unified manner. There are significant differences regarding their policy outcomes. In order to investigate when the outcomes are different, we made a COVID-19 policy outcome analysis tool, jpcovid for evaluating time-series scores of individual prefectures, not a policy analysis tool.</p><p><strong>Methods: </strong>Scoring policies is based on a single population mortality metric: the number of COVID-19 deaths divided by the population in millions from a demographic perspective.</p><p><strong>Results: </strong>Although uniformed policies have been adopted by the 47 prefectures in Japan, there are significant differences in the calculated scores among the 47 prefectures. This difference can be caused by differences in the herding instincts of the community with COVID-19 variants. The herd instinct is an inherent tendency to associate with others and follow the group's behavior or a behavior wherein people tend to react to the actions of others without considering the reason. The snapshot scoring tool, jpscore showed that Niigata has the best score of 67.9 while Osaka has the worst score of 727.9. jpcovid allows users to identify when herd instincts made changes in time-series scores.</p><p><strong>Conclusions: </strong>This is the world's first large-scale measurement on the herd instinct of prefectures in Japan. The proposed method can be applied to other countries in general.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s12553-023-00759-x.</p>\",\"PeriodicalId\":12941,\"journal\":{\"name\":\"Health and Technology\",\"volume\":\" \",\"pages\":\"1-6\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2023-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252164/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s12553-023-00759-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12553-023-00759-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
A time-series COVID-19 policy outcome analysis tool to measure human behavior from a herd instinct perspective.
Purpose: There are 47 municipalities and prefectures in Japan that operate similar COVID-19 policies in a unified manner. There are significant differences regarding their policy outcomes. In order to investigate when the outcomes are different, we made a COVID-19 policy outcome analysis tool, jpcovid for evaluating time-series scores of individual prefectures, not a policy analysis tool.
Methods: Scoring policies is based on a single population mortality metric: the number of COVID-19 deaths divided by the population in millions from a demographic perspective.
Results: Although uniformed policies have been adopted by the 47 prefectures in Japan, there are significant differences in the calculated scores among the 47 prefectures. This difference can be caused by differences in the herding instincts of the community with COVID-19 variants. The herd instinct is an inherent tendency to associate with others and follow the group's behavior or a behavior wherein people tend to react to the actions of others without considering the reason. The snapshot scoring tool, jpscore showed that Niigata has the best score of 67.9 while Osaka has the worst score of 727.9. jpcovid allows users to identify when herd instincts made changes in time-series scores.
Conclusions: This is the world's first large-scale measurement on the herd instinct of prefectures in Japan. The proposed method can be applied to other countries in general.
Supplementary information: The online version contains supplementary material available at 10.1007/s12553-023-00759-x.
期刊介绍：
Health and Technology is the first truly cross-disciplinary journal on issues related to health technologies addressing all professions relating to health, care and health technology.The journal constitutes an information platform connecting medical technology and informatics with the needs of care, health care professionals and patients. Thus, medical physicists and biomedical/clinical engineers are encouraged to write articles not only for their colleagues, but directed to all other groups of readers as well, and vice versa.By its nature, the journal presents and discusses hot subjects including but not limited to patient safety, patient empowerment, disease surveillance and management, e-health and issues concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. The journal also addresses the medical, financial, social, educational and safety aspects of health technologies as well as health technology assessment and management, including issues such security, efficacy, cost in comparison to the benefit, as well as social, legal and ethical implications.This journal is a communicative source for the health work force (physicians, nurses, medical physicists, clinical engineers, biomedical engineers, hospital engineers, etc.), the ministries of health, hospital management, self-employed doctors, health care providers and regulatory agencies, the medical technology industry, patients'' associations, universities (biomedical and clinical engineering, medical physics, medical informatics, biology, medicine and public health as well as health economics programs), research institutes and professional, scientific and technical organizations.Health and Technology is jointly published by Springer and the IUPESM (International Union for Physical and Engineering Sciences in Medicine) in cooperation with the World Health Organization.
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