OBJECTIVES:

The SARS-CoV-2 pandemic has been characterized by sharp, rapid increases in disease incidence, following by a relative long and slow decrease in new cases. This unusual curve was particularly surprising as governments instituted drastic measures to stop the disease progression. This study was designed to model the post-peak decline in SARS-CoV-2 infection cases as a function of social distancing scores, daily tests, population density and average family sizes in select US counties.

METHODS:

Data for SARS-CoV-2 cases and daily testing counts was obtained from The COVID Tracking Project. The family sizes and population density were obtained from the US census. Social distancing scores were purchased from UnaCast. Family size, population density and social distancing scores were categorized by quartile, with lowest quartiles used as reference in the models. Two forecasting models, an exponential smoothing and auto-regressive integrated moving average (ARIMA) model, were built on data from New York Queens, New York Kings and Illinois Cook counties. Root mean square error (RMSE) and Akaike information criterion (AIC) were evaluate for both model types.

RESULTS:

The forecast for infection rates post-peak using the exponential smoothing method produced models with AIC and RMSE of 340.7 and 12.6 for New York/Queens, 184.7 and 6.45 for Illinois Cook and 243.9 and 14.3 for New York/Kings, respectively. ARIMA models for all three areas resulted in AIC and RMSE values of: 242.12 and 7.31 for New York/Queens, 138.45 and 5.75 for Illinois Cook and 476.8 and 4.63 for New York/Kings, respectively. The calculated R squared value was greater for the exponential smoothing model versus the ARIMA for all counties and ranged from 0.45 (Illinois Cook) to 0.56 (New York Kings).

CONCLUSIONS:

The exponential smoothing method was more reliable than the ARIMA method for predicting the downwards trend following a COVID-19 peak, despite relative low R squared values.