Kuniya Toshikazu
Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan.
J Clin Med. 2020 Mar 13;9(3):789. doi: 10.3390/jcm9030789.
The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2 . 6 ( 95 % CI, 2 . 4 - 2 . 8 ) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.
2020年1月15日，日本报告了首例2019冠状病毒病（COVID-19）病例，此后报告病例数逐日增加。本研究旨在利用2020年1月15日至2月29日的实时数据，对日本COVID-19的疫情高峰进行预测。考虑到由于感染人群识别不完全而产生的不确定性，我们应用著名的SEIR分区模型进行预测。通过使用基于最小二乘法并带有泊松噪声的方法，我们估计日本该疫情的基本再生数为R0 = 2.6（95%置信区间，2.4 - 2.8），疫情高峰可能会在夏初至仲夏期间到来。此外，我们还获得了以下流行病学见解：（1）基本疫情规模不太可能受到实际感染人群识别率的影响；（2）干预措施对疫情高峰的延迟有积极作用；（3）需要进行较长时间的干预才能有效降低最终疫情规模。
