The Forecast of COVID-19 Spread Risk at The County Level

Abstract

\n The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people s lives and restart the economy quickly and safely. People’s social behavior as captured by their mobility data plays a role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreak in the United States. The daily data are fed to a deep model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p=0.005)) between the model prediction and the actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. Lower correlation was reported for the counties with a total cases of <1,000 during the test interval. The average mean absolute error (MAE) was 605.4, and it was decreasing with the decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread where average daily cases decrease with the decrease in retires percentage, and increase with the increase in young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also could also help with early and effective management of possible future pandemics.