Graduate School of Medicine, Kyoto University, Yoshidakonoecho, Sakyo-ku, Kyoto 606-8501, Japan.
Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan.
Math Biosci Eng. 2023 Dec 5;20(12):21499-21513. doi: 10.3934/mbe.2023951.
Mobility restrictions were widely practiced to reduce contact with others and prevent the spatial spread of COVID-19 infection. Using inter-prefectural mobility and epidemiological data, a statistical model was devised to predict the number of imported cases in each Japanese prefecture. The number of imported cases crossing prefectural borders in 2020 was predicted using inter-prefectural mobility rates based on mobile phone data and prevalence estimates in the origin prefectures. The simplistic model was quantified using surveillance data of cases with an inter-prefectural travel history. Subsequently, simulations were carried out to understand how imported cases vary with the mobility rate and prevalence at the origin. Overall, the predicted number of imported cases qualitatively captured the observed number of imported cases over time. Although Hokkaido and Okinawa are the northernmost and the southernmost prefectures, respectively, they were sensitive to differing prevalence rate in Tokyo and Osaka and the mobility rate. Additionally, other prefectures were sensitive to mobility change, assuming that an increment in the mobility rate was seen in all prefectures. Our findings indicate the need to account for the weight of an inter-prefectural mobility network when implementing countermeasures to restrict human movement. If the mobility rates were maintained lower than the observed rates, then the number of imported cases could have been maintained at substantially lower levels than the observed, thus potentially preventing the unnecessary spatial spread of COVID-19 in late 2020.
为了减少与他人的接触并防止 COVID-19 感染的空间传播，广泛实施了行动限制。利用跨县际流动和流行病学数据，设计了一个统计模型来预测日本每个县的输入病例数。使用基于手机数据的县际流动率和起源县的流行率估计值，预测了 2020 年跨越县界的输入病例数。使用具有县际旅行史的病例的监测数据对简单模型进行了量化。随后，进行了模拟，以了解输入病例如何随起源地的流动率和流行率而变化。总体而言，预测的输入病例数定性上捕获了随时间推移观察到的输入病例数。尽管北海道和冲绳分别是最北部和最南部的县，但它们对东京和大阪以及流动率的不同流行率很敏感。此外，其他县也对流动变化敏感，假设所有县的流动率都有所增加。我们的研究结果表明，在实施限制人类流动的对策时，需要考虑县际流动网络的权重。如果保持的流动率低于观察到的水平，那么输入病例的数量可能会保持在明显低于观察水平的水平，从而有可能防止 2020 年末 COVID-19 的不必要的空间传播。
