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Short-term forecasting of the coronavirus pandemic
Jurgen A Doornik1,2, Jennifer L Castle3,2, David F Hendry1,2
1Nuffield College, Oxford, UK.
International Journal of Forecasting
|September 21, 2020
Summary
Real-time COVID-19 case and death forecasts were generated using statistical methods. These short-term predictions, based on past data trends, proved more accurate than some epidemiological models early in the pandemic.
Area of Science:
- Epidemiology
- Biostatistics
- Machine Learning
Background:
- Real-time forecasting of coronavirus disease 2019 (COVID-19) cases and deaths is crucial for public health.
- Existing epidemiological models have limitations in accurately predicting short-term disease spread.
Purpose of the Study:
- To develop and evaluate a novel forecasting method for COVID-19.
- To provide short-term statistical extrapolations complementary to traditional epidemiological models.
Main Methods:
- Utilized machine learning to extract trends from time-series data within defined windows.
- Applied constraints to flexible extracted trends for forecast computation.
- Focused on statistical extrapolation of past and current COVID-19 data.
Main Results:
- The developed method generated real-time forecasts of COVID-19 confirmed cases and deaths.
- These statistical forecasts demonstrated superior accuracy compared to some epidemiological models during the early pandemic stages.
- The approach assumes underlying trends are informative for short-term predictions without additional epidemiological assumptions.
Conclusions:
- Short-term statistical forecasts based on trend extraction using machine learning are effective for COVID-19.
- This method offers a valuable complement to traditional epidemiological modeling for disease surveillance.
- The approach proved robust and accurate, particularly in the initial phases of the pandemic.
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