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Short-Range Forecasting Of Covid-19 During Early Onset At County, Health District, And State Geographic Levels Using Seven Methods: Comparative Forecasting Study, Christopher Lynch, Ross Gore

VMASC Publications

BACKGROUND:

Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels.

OBJECTIVE:

COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes …

Application Of One-, Three-, And Seven-Day Forecasts During Early Onset On The Covid-19 Epidemic Dataset Using Moving Average, Autoregressive, Autoregressive Moving Average, Autoregressive Integrated Moving Average, And Naïve Forecasting Methods, Christopher J. Lynch, Ross Gore

VMASC Publications

The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic's cumulative case counts at the county, health district, and state geographic levels for the state of Virginia. Forecasts are created over the first 46 days of reported COVID-19 cases using the cumulative case count data provided by The New York Times as of April 22, 2020. From this historical data, one-, three-, seven, and all-days prior to the forecast start date are used to generate the forecasts. Forecasts are created using: …