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

TL;DR

This paper introduces a global COVID-19 trend estimation and short-term forecasting method using robust seasonal decomposition, integrated into a twice-daily updated dashboard for over 200 countries, aiding decision-making.

Contribution

It presents a novel, globally applicable forecasting approach that accounts for reporting delays and irregularities, improving short-term COVID-19 trend predictions.

Findings

Effective trend estimation using seasonal decomposition techniques

Accurate seven-day forecasts of cases and deaths

Application to global and regional risk mapping

Abstract

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor the evolution of the pandemic, inform the public, and assist governments in decision making. Our goal is to develop a globally applicable method, integrated in a twice 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 a seven-day forecast. One of the significant difficulties to manage 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.