Analysis of COVID-19 in Japan with Extended SEIR model and ensemble Kalman filter

TL;DR

This paper presents an extended SEIR model with data assimilation using ensemble Kalman filter to analyze COVID-19 spread in Japan, accounting for asymptomatic cases and estimating the effective reproduction number more reliably.

Contribution

It introduces an extended SEIR model incorporating asymptomatic and pre-symptomatic cases with data assimilation, providing more stable estimates of the reproduction number in Japan.

Findings

Estimated reproduction numbers are more stable than previous methods.

Decreasing effectiveness of emergency measures observed regionally.

Sensitivity analysis highlights impact of infectivity parameters.

Abstract

We introduce an extended SEIR infectious disease model with data assimilation for the study of the spread of COVID-19. In this framework, undetected asymptomatic and pre-symptomatic cases are taken into account, and the impact of their uncertain proportion is fully investigated. The standard SEIR model does not consider these populations, while their role in the propagation of the disease is acknowledged. An ensemble Kalman filter is implemented to assimilate reliable observations of three compartments in the model. The system tracks the evolution of the effective reproduction number and estimates the unobservable subpopulations. The analysis is carried out for three main prefectures of Japan and for the entire population of Japan. For these four populations, our estimated effective reproduction numbers are more stable than the corresponding ones estimated by a different method (Toyokeizai). We also perform sensitivity tests for different values of some uncertain medical parameters, like the relative infectivity of symptomatic / asymptomatic cases. The regional analysis results suggest the decreasing efficiency of the states of emergency.