AO_SCPLOWBSTRACTC_SCPLOWThe importance of pandemic forecast cannot be overemphasized . We propose an interpretable machine learning approach for forecasting pandemic transmission rates by utilizing local mobility statistics and government policies . A calibration step is introduced to deal with time-varying relationships between transmission rates and predictors . Experimental results demonstrate that our approach is able to make accurate two-week ahead predictions of the state-level COVID-19 infection trends in the US . Moreover, the models trained by our approach offer insights into the spread of COVID-19, such as the association between the baseline transmission rate and the state-level demographics, the effectiveness of local policies in reducing COVID-19 infections, and so on . This work provides a good understanding of COVID-19 evolution with respect to state-level characteristics and can potentially inform local policymakers in devising customized response strategies.