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We introduce an extended SEIR infectious disease model with data assimilationfor the study of the spread of COVID-19. In this framework, undetectedasymptomatic and pre-symptomatic cases are taken into account, and the impactof their uncertain proportion is fully investigated. The standard SEIR modeldoes not consider these populations, while their role in the propagation of thedisease is acknowledged. An ensemble Kalman filter is implemented to assimilatereliable observations of three compartments in the model. The system tracks theevolution of the effective reproduction number and estimates the unobservablesubpopulations. The analysis is carried out for three main prefectures of Japanand for the entire population of Japan. For these four populations, ourestimated effective reproduction numbers are more stable than the correspondingones estimated by a different method (Toyokeizai). We also perform sensitivitytests for different values of some uncertain medical parameters, like therelative infectivity of symptomatic / asymptomatic cases. The regional analysisresults suggest the decreasing efficiency of the states of emergency.

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