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Assimilating the latest epidemic data can improve the predictions of epidemic dynamics compared with those using only dynamic models. However, capturing the nonlinear spatiotemporal heterogeneity remains challenging. We propose a data assimilation method to simultaneously update the parameters and states with respect to their spatiotemporal variation intervals by (1) developing a susceptible-infected-removed-vaccinated model by considering vaccination strategy and quarantine periods and (2) assimilating real-time epidemic data using an ensemble Kalman filter for daily updates of the state variables and Metropolis–Hastings sampling for weekly parameter estimation. Synthetic experiments and a WebGIS-based global prediction system demonstrate the sufficient nowcasting accuracy of this method. An analysis of the system outcomes shows that modeling vaccination details, embedding reasonable …