Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide

Abstract

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.

Keywords: COVID-19; forecasting; seasonal decomposition; trend estimation.

Fig. 1.

Green bars correspond to daily cases. Blue bars show the forecast for the next 7 d. The red line shows the estimated trend smoothed together with the forecast. (A) JHU daily cases for Spain with forecasts starting from September 10, 2020, with a negative observation in June 2020 (not shown in the plot), visible outliers, and seasonality patterns in reporting starting from July. Daily cases in the last 3 mo preceding December 3, 2021, for (B) Brazil, (C) Kansas, (D) China, and (E) Germany. Shown are the observed number of cases (green), estimated trend (red), and trend forecast for the following week (blue).

Fig. 2.

Illustration of the probabilistic forecast as a collection of nested intervals (red shaded regions) for the forecast of the number of cases in the United States.

Fig. 3.

Histograms for the average WIS (in x axis) of 1-wk-ahead forecasts for the 31 European countries.

Fig. 4.

(Left) Scatter plot of RMAE and RWIS on 1-wk-ahead forecast for the selected subset of 80 countries, with points in red corresponding to the 30 countries with either larger populations or larger population density included in (Right) the bar plot.

Fig. 5.

Snapshot of world risk map from March 25, 2022.