Assessment of the Impact of COVID-19 Infections Considering Risk of Infected People Inflow to the Region

EasyChair Preprint 5085

Abstract

In this paper, we propose a new SEIR model for predicting the transmission of new coronavirus infections using mobile statistical information and machine learning, taking into account the risk of influx. The model can predict the number of infected persons in a region with high accuracy, and the estimation results for Sapporo and Tokyo show a high prediction accuracy of 1.2 persons/day. Using this model, we analyze the impact of the risk of influx to Sapporo City and show that the spread of infection in November could have been reduced to less than half if the number of influxes had been limited after the summer. We also examine the preventive measures called for in the emergency declaration in the Tokyo metropolitan area. We use the effective reproduction reduction rate of infection control measures obtained from the individual-based model for the urban area around Tokyo. We predict one month later using the SEIR model and show that comprehensive measures such as thorough droplet control, telework, and event restrictions are more effective than shortened hours at restaurants.

Keyphrases: COVID-19, SEIRモデル, 個体ベースモデル, 流入リスク, 逆シミュレーション最適化

@booklet{EasyChair:5085,
  author    = {Setsuya Kurahashi and Haruki Yokomaku and Kohei Yashima and Hideyuki Nagai and Masatoshi Yukishima and Sho Toyooka},
  title     = {Assessment of the Impact of COVID-19 Infections Considering Risk of Infected People Inflow to the Region},
  howpublished = {EasyChair Preprint 5085},
  year      = {EasyChair, 2021}}