Incidence moments: a simple method to study the memory and short term forecast of the COVID-19 incidence time-series

Abstract

Abstract Objectives The ability to predict COVID-19 dynamic has been very low, reflected in unexpected changes in the number of cases in different settings. Here the objective was to study the temporal memory of the reported daily incidence time series and propose a simple model for short-term forecast of the incidence. Methods We propose a new concept called incidence moments that allows exploring the memory of the reported incidence time series, based on successive products of the incidence and the reproductive number that allow a short term forecast of the future incidence. We studied the correlation between the predictions of and the reported incidence determining the best predictor. We compared the predictions and observed COVID-19 incidences with the mean arctangent absolute percentage error (MAAPE) analyses for the world, 43 countries and for Chile and its regions. Results The best predictor was the third moment of incidence, determining a short temporal prediction window of 15 days. After 15 days the absolute percentage error of the prediction increases significantly. The method perform better for larger populations and presents distortions in contexts of abrupt changes in incidence. Conclusions The epidemic dynamics of COVID 19 had a very short prediction window, probably associated with an intrinsic chaotic behavior of its dynamics. The incident moment modeling approach could be useful as a tool whose simplicity is appealing, since it allows rapid implementation in different settings, even with limited epidemiological technical capabilities and without requiring a large amount of computational data.