著者
Kenji Karako Peipei Song Yu Chen Wei Tang
出版者
International Research and Cooperation Association for Bio & Socio-Sciences Advancement
雑誌
BioScience Trends (ISSN:18817815)
巻号頁・発行日
vol.14, no.2, pp.134-138, 2020-04-30 (Released:2020-05-21)
参考文献数
21
被引用文献数
8 10

To assess the effectiveness of response strategies of avoiding large gatherings or crowded areas and to predict the spread of COVID-19 infections in Japan, we developed a stochastic transmission model by extending the Susceptible-Infected-Removed (SIR) epidemiological model with an additional modeling of the individual action on whether to stay away from the crowded areas. The population were divided into three compartments: Susceptible, Infected, Removed. Susceptible transitions to Infected every hour with a probability determined by the ratio of Infected and the congestion of area. The total area consists of three zones crowded zone, mid zone and uncrowded zone, with different infection probabilities characterized by the number of people gathered there. The time for each people to spend in the crowded zone is curtailed by 0, 2, 4, 6, 7, and 8 hours, and the time spent in mid zone is extended accordingly. This simulation showed that the number of Infected and Removed will increase rapidly if there is no reduction of the time spent in crowded zone. On the other hand, the stagnant growth of Infected can be observed when the time spent in the crowded zone is reduced to 4 hours, and the growth number of Infected will decrease and the spread of the infection will subside gradually if the time spent in the crowded zone is further cut to 2 hours. In conclusions The infection spread in Japan will be gradually contained by reducing the time spent in the crowded zone to less than 4 hours.

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J-STAGE Articles - Analysis of COVID-19 infection spread in Japan based on stochastic transition model https://t.co/9G4F5LEMnq
COVID-19の感染広がりを防ぐため日本政府が2月下旬に提唱したテレワーク及び学校の休校措置による3密状態を避ける政策の効果を検証及び評価するために感染症数理モデルであるSusceptible-Infected-Removedモデルをベースとして人々が密状態の場所を避ける行動をベースに確率的 https://t.co/xqcqAplFAW

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