在日本的2019年冠状病毒疾病期间,人类运动避免决策
Ryosuke Omori1, Koichi Ito2, Shunsuke Kanemitsu3
1Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan.
Journal of theoretical biology
|March 17, 2024
概括
人民的人民的人民.
科学领域:
- 流行病学 流行病学
- 行为科学 行为科学
- 数据科学数据科学数据科学
背景情况:
- 了解流行病期间宿主行为变化对于预测疾病动态至关重要.
- 预测行为转变需要实时流行病情况意识,移动数据具有挑战性.
- 人类移动模式是疫情应对措施的关键指标.
研究的目的:
- 分析使用适应数据对流行病情况的决策时间和行为反应.
- 为了量化在日本应对2019年冠状病毒疾病 (COVID-19) 的流动性回避.
- 了解人们如何感知和反应随着时间的推移流行病的强度.
主要方法:
- 利用了日本最大的旅游住宿预订数据集 (覆盖了50%的住宿).
- 使用时间序列预订和取消数据测量了拟议的流动性回避指数.
- 分析了日本COVID-19流行病的数据,直到2023年2月.
主要成果:
- 避免流动的峰值与报告的病例数量一致.
- 避免移动显示出与报告病例的对数有成比例的关系.
- 响应灵敏度在第一波中确立,随后的波显示响应减弱.
结论:
- 人们对流行病的强度的感知是对数的.
- 最初的流行病经历塑造了长期的行为反应.
- 随着时间的推移,对流行病的行为适应会导致敏感性降低.
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