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To study the spatiotemporal COVID-19 spread, we use the Global Epidemic and Mobility Model (GLEAM), an individual-based, stochastic, and spatial epidemic model [1, 2, 3, 4]. GLEAM uses real-world data to perform in-silico simulations of the spatial spread of infectious diseases at the global level.  We use the model to analyze the spatiotemporal spread and magnitude of the COVID-19 epidemic in a set of countries and geographical locations of interest for vaccine trials. The list of countries analyzed might change according to data availability. The model generates an ensemble of possible epidemic projections described by the number of newly generated infections, times of disease arrival in different regions, and the number of traveling infection carriers. Approximate Bayesian Computation is used to estimate the posterior distribution of the basic parameters of the model. The calibration of the global model for COVID-19 is reported in Science.

For each country we report the following information:
•  Median weekly of new deaths. This is reported to show the goodness of fit on past data by comparing with the actual data reported from each location.
•   Weekly new infections with the 95%CI, IQR, and medians shown for the 6 weeks after the last data point used for the calibration.

Disclaimer: There are large uncertainties around the transmission of COVID-19, the effectiveness of different policies and the extent to which the population is compliant to social distancing measures. The presented material is based on modeling scenario assumptions informed by current knowledge of the disease and subject to change as more data become available.