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Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks.
Ito, Koichi; Kanemitsu, Shunsuke; Kimura, Ryusuke; Omori, Ryosuke.
  • Ito K; Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido, 001-0020, Japan.
  • Kanemitsu S; Data Solution Unit 2 (Marriage and Family/Automobile Business/Travel), Data Management and Planning Office, Product Development Management Office, Recruit Co., Ltd, Chiyoda-ku, Tokyo, 100-6640, Japan.
  • Kimura R; SaaS Data Solution Unit, Data Management & Planning Office, Product Development Management Office, Recruit Co., Ltd, Chiyoda-ku, Tokyo, 100-6640, Japan.
  • Omori R; Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido, 001-0020, Japan. omori@czc.hokudai.ac.jp.
Sci Rep ; 12(1): 19780, 2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2119290
ABSTRACT
Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-24323-1

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-24323-1