Predicting the Shape of the COVID-19 Mortality Curve: A US Modeling Analysis
Author(s)
ABSTRACT WITHDRAWN
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. The mortality curve follows that of new cases, with in some instances, an even longer tail. This study was designed to model the post-peak decline in SARS-CoV-2 mortality 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 fatality counts 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. Social distancing scores were categorized in this study as: <1.9 (reference), 2-2.9 (lower), 3-3.9 (medium), 4-5 (high). Two forecasting models, an exponential smoothing and auto-regressive integrated moving average (ARIMA) model, were built on data from New York Queens and New York Kings counties. Root mean square error (RMSE) and Akaike information criterion (AIC) were evaluate for both model types. RESULTS: The forecast for mortality counts post-peak using the exponential smoothing method produced models with AIC and RMSE of 242.38 and 4.25 for New York/Kings and 287.93 and 4.63 for New York/Queens, respectively. ARIMA models for both areas resulted in AIC and RMSE values of: 349.00 and 2.69 for New York/Kings and 361.22 and 2.91 for New York/Queens, respectively. The calculated R squared value was greater for the exponential smoothing model (New York Kings model: R squared = 0.73, New York/Queens model: R squared = 0.55) CONCLUSIONS: The exponential smoothing method was more reliable than the ARIMA method for predicting the downwards trend of mortality cases following the COVID-19 peak
Conference/Value in Health Info
2020-11, ISPOR Europe 2020, Milan, Italy
Value in Health, Volume 23, Issue S2 (December 2020)
Code
PIN80
Topic
Epidemiology & Public Health
Topic Subcategory
Public Health
Disease
Infectious Disease (non-vaccine)