Christopher J. Lynch|Ross Gore
Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study
Old Dominion University|Old Dominion University
forecasting methods rely trends averages prior observations forecast covid19 case counts covid19 forecasts received much media attention numerous platforms created inform public however forecasting effectiveness varies geographic scope affected changing assumptions behaviors preventative measures response pandemic due time requirements developing covid19 vaccine evidence needed inform shortterm forecasting method selection county health district state levelscovid19 forecasts keep public informed contribute public policy proper understanding forecasting purposes outcomes needed advance knowledge health statistics policy makers public using publicly available realtime data provided online aimed evaluate performance seven forecasting methods utilized forecast cumulative covid19 case counts forecasts evaluated based well forecast 1 3 7 days forward utilizing 1 3 7 priorday cumulative case counts early virus onset study provides objective evaluation forecasting methods identify forecasting model assumptions contribute lower error forecasting covid19 cumulative case growth information benefits professionals decision makers public relying data provided shortterm case count estimates varied geographic levelswe created 1 3 7day forecasts county health district state levels using 1 nave approach 2 holtwinters hw exponential smoothing 3 growth rate approach 4 moving average approach 5 autoregressive ar approach 6 autoregressive moving average arma approach 7 autoregressive integrated moving average arima approach forecasts relied virginias 3464 historical countylevel cumulative case counts march 7 april 22 2020 reported new york times statistically significant results identified using 95 cis median absolute error mdae median absolute percentage error mdape metrics resulting 216698 forecaststhe nextday forecast 3day lookback length obtained lowest mdae median 067 95 ci 049084 p001 statistically significantly differed 39 59 alternatives 66 53 59 alternatives 90 geographic level significance level 01 shortrange forecasting methods assuming stationary means prior days counts outperformed methods assumptions weak stationarity nonstationarity means mdape results revealed statistically significant differences across geographic levelsfor shortrange covid19 cumulative case count forecasting county health district state levels early onset following found 1 method effective forecasting 1 3 7day cumulative case counts 2 exponential growth best representation case growth early virus onset public aware virus 3 geographic resolution factor selection forecasting methods