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. 2022 Apr-Jun;38(2):453-466.
doi: 10.1016/j.ijforecast.2020.09.003. Epub 2020 Sep 12.

Short-term forecasting of the coronavirus pandemic

Jurgen A Doornik  1   2 , Jennifer L Castle  3   2 , David F Hendry  1   2
Affiliations

Short-term forecasting of the coronavirus pandemic

Jurgen A Doornik et al. Int J Forecast. 2022 Apr-Jun.

Abstract

We have been publishing real-time forecasts of confirmed cases and deaths from coronavirus disease 2019 (COVID-19) since mid-March 2020 (published at www.doornik.com/COVID-19). These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative regarding short-term developments but without requiring other assumptions about how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is spreading, or whether preventative policies are effective. Thus, they are complementary to the forecasts obtained from epidemiological models. The forecasts are based on extracting trends from windows of data using machine learning and then computing the forecasts by applying some constraints to the flexible extracted trend. These methods have been applied previously to various other time series data and they performed well. They have also proved effective in the COVID-19 setting where they provided better forecasts than some epidemiological models in the earlier stages of the pandemic.

Keywords: Automatic forecasting; COVID-19; Epidemiology; Forecast averaging; Forecasting; Machine learning; Smoothing; Time series; Trend indicator saturation.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Forecasts of confirmed cases of COVID-19 in UK from 2020-03-21 (left panel), together with forecasts from 2020-03-16 and 2020-03-8 (right panel). The seven thin grey lines and the red forecast (marked with a circle and prefixed with F:) were used together for the average forecasts (solid black line with + symbols). The thick line labeled UK Confirmed represents the observed counts.
Fig. 2
Fig. 2
Forecasts of confirmed cases of COVID-19 for UK, EU-27, USA, and China. JH/CSSE data collected and forecast on 2020-03-17. Model specifications are shown in Table 1.
Fig. 3
Fig. 3
Forecasts of confirmed cases of COVID-19 for mainland Denmark and the Netherlands. Data from 2020-03-17; model specifications in Table 1.
Fig. 4
Fig. 4
Forecasts of deaths from COVID-19 for UK, EU-27, US, and Italy. Data and forecasts from 2020-03-17 and subsequently updated with later data (dotted line). Model specifications are shown in Table 1.
Fig. 5
Fig. 5
Forecast accuracy over time. MAPE for each target date for one, two, and four step-ahead forecasts. Confirmed cases are shown at the top and deaths at the bottom.
Fig. 6
Fig. 6
Forecasts of confirmed cases of COVID-19 in UK from 2020-03-20 to 2020-04-05. Average forecasts and SIR forecasts using nonlinear least squares.
See this image and copyright information in PMC

References

    1. Batista M. 2020. Estimation of the final size of the Coronavirus Epidemic by the SIR Model. Online paper ResearchGate.
    1. Bhatia S., et al. MRC Centre for Global Infectious Disease Analysis; 2020. Short-term forecasts of COVID-19 deaths in multiple countries: Online reports 2020-04-15 and 2020-04-08. mrc-ide.github.io/covid19-short-term-forecasts/index.html.
    1. Castle J.L., Clements M.P., Hendry D.F. Robust approaches to forecasting. International Journal of Forecasting. 2015;31:99–112.
    1. Castle J.L., Doornik J.A., Hendry D.F. Some forecasting principles from the M4 competition. Economics Discussion. 2019 Paper 2019-W01 Nuffield College, University of Oxford.
    1. Castle J.L., Doornik J.A., Hendry D.F. In progress Nuffield College, University of Oxford; 2020. Medium-term forecasting of the Coronavirus pandemic.

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