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Currently submitted to: Journal of Medical Internet Research

Date Submitted: Oct 10, 2020
Open Peer Review Period: Oct 10, 2020 - Dec 5, 2020
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Short-range forecasting of coronavirus disease 2019 (COVID-19) during early onset at county, health district, and state geographic levels: Comparative forecasting approach using seven forecasting methods

  • Christopher J Lynch; 
  • Ross Gore; 



Modeling approaches have utilized variations on susceptible, infected, and recovered (SIR), susceptible, exposed, infected, and recovered (SEIR), and machine learning models to estimate the spread of coronavirus disease 2019 (COVID-19) based on the identified virus characteristics. Forecasting methods rely on real-time numbers of confirmed case and death counts to create forecasts based on the characteristics of the trends and averages of prior data. COVID-19 forecasting studies have varied in geographic scales from global, country, and state levels. These studies support the need to implement mitigation strategies to slow the spread, flatten the peak, inform policy, and indicate medical capacity burdens.


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. Using publicly available real-time data provided online, we evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts are evaluated based on how well they can forecast one-. three-, and seven-days forward when utilizing one-, three-, seven-, or all-prior days’ cumulative case counts during early onset of case spreading. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels.


One-, three-, and seven-days forecasts are created at the county, health district, and state levels using: (1) a naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Forecasts rely on 3,463 observations from Virginia’s county-level cumulative case counts as reported by The New York Times. 95% confidence of Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics of the resulting 216,698 forecasts are used to identify statistically significant differences.


Single-day MA forecast with three-day lookback obtained the lowest MdAE and statistically significantly differs from 39 (66.1%) to 53 (89.8%) of alternatives at each geographic level using P value equal to 0.05. Methods assuming stationary means of prior days’ counts outperform methods with assumptions of weak- or non-stationarity means. MdAPE results reveal statistically significant differences across geographic levels.


For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset: (1) MA is effective for forecasting one-, three-, and seven-days’ cumulative case counts; (2) assumptions of stationarity of means in prior-observations are more effective than assumptions of weak- or non-stationarity means; and (3) geographic resolution is a factor in forecasting method selection. (This work received no external funding.)


Please cite as:

Lynch CJ, Gore R

Short-range forecasting of coronavirus disease 2019 (COVID-19) during early onset at county, health district, and state geographic levels: Comparative forecasting approach using seven forecasting methods

JMIR Preprints. 10/10/2020:24925

DOI: 10.2196/preprints.24925


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