Short-term forecasting of the coronavirus pandemic

Abstract

We have been publishing real-time forecasts of confirmed cases and deaths for COVID-19 from mid-March 2020 onwards, 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 of short term developments, without requiring other assumptions of how the SARS-CoV-2 virus is spreading, or whether preventative policies are effective. As such they are complementary to forecasts from epidemiological models. The forecasts are based on extracting trends from windows of the data, applying machine learning, and then computing forecasts by applying some constraints to this flexible extracted trend. The methods have previously been applied to various other time series data and have performed well. They are also effective in this setting, providing better forecasts in the earlier stages than some epidemiological models.

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

“…For instance, an application of exponential smoothing with multiplicative trend to forecasting the number of COVID-19 deaths for the United States results in an average error of 4.4%, 8.1% and 16.3% for one, two and four-steps-ahead respectively when we focus the evaluation for the rounds 6 to 9 of our application (2020-03-21 to 2020-04-30). Doornik et al (2020) report average errors of 4.9%, 8.8% and 14.0% for the same lead times and a similar timeframe (2020-03-24 to 2020-04-25, see their Table 2 ). Doornik et al (2020) also mention that their approach offers superior performance against two epidemiological models.…”

“… Doornik et al (2020) report average errors of 4.9%, 8.8% and 14.0% for the same lead times and a similar timeframe (2020-03-24 to 2020-04-25, see their Table 2 ). Doornik et al (2020) also mention that their approach offers superior performance against two epidemiological models. This suggests that our simple approach performs well in terms of accuracy against other published forecasts.…”

“…The grand majority of the individual models in this ensemble have been epidemiological models ( Centers for Disease Control and Prevention, 2020 ). We believe that the inclusion of statistical models, like our simple model or that of Doornik et al (2020) , as well as judgmental methods, would further increase the accuracy of such ensembles.…”

“…According to the authors, both models were effective in predicting the epidemic peak and size but no uncertainty measures were provided. Doornik et al (2020) compared the performance of epidemiological models prepared from the Medical Research Council Centre for Global Infectious Disease Analysis at Imperial College London to their own statistical forecasts and discussed the large forecast errors of epidemiological models in the first months of the pandemic.…”

The Value of Robust Statistical Forecasts in the Covid-19 Pandemic

“…In Section 2, we present a statistical forecasting device (Cardt; see Castle et al, 2021) that is highly adaptive and has been shown to work well in forecasting the 100,000 time series of varying frequency and sample length in the M4 competition (see Makridakis et al, 2020). Section 3 applies that forecasting device to short-term forecasts of Covid-19 confirmed cases and deaths (see also Doornik et al, 2020bDoornik et al, , 2020c, evaluating these forecasts against published forecasts from epidemiological models. Section 4 forecasts UK aggregate unemployment, comparing our statistical forecasts to those from a more structural congruent econometric model, before Section 5 concludes.…”