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 the USA at the county level. The data are aggregated to provide the estimates at the State level. The model generates an ensemble of possible epidemic projections described by the number of newly generated infections and deaths. The model is calibrated on weekly deaths data from the Johns Hopkins Centers for Civic Impact by using an information theoretical approach.

For each state we report the following information:
•  Cumulative median infection attack rate (with 90% CI) since the start of the epidemic
•   Weekly median number of new infections with the 90%CI.

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.