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

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.

How accurate are radiography and computed tomography in the diagnosis of COVID-19?—A Bayesian approach

“…1 From the experience of countries with large-scale community transmission, it has been learned that COVID-19 requires an unprecedented mobilization of health systems. [2][3][4][5] The demand on health care systems has been stressed, especially intensive care units for large numbers of people who become ill simultaneously. The ability of the health care system to respond will depend on available resources, including hospital beds and intensive care units.…”

“…The role of chest CT in patients with clinical suspicion of COVID-19 is evolving with scientific evidence, but there are substantial differences in opinion on when and how the technique should be used for clinical diagnosis. 3 While the American College of Radiology only recommends the use of CT to solve problems, the Fleishner society assigns it a role as an important tool to use if symptoms worsen or in a resource-limited setting for RT-PCR. 8,9 The Expert Consensus Statement on Reporting of the Radiological Society of North America (RSNA) 10 has proposed a standardized classification for CT: (a) typical appearance (Cov19Typ): characterized by peripheral, bilateral, ground glass opacities (GGO) with or without consolidation or intralobular lines (crazy paving), multifocal GGO and/or findings of organizing pneumonia; (b) indeterminate appearance (Cov19Ind): characterized by absence of typical pattern but with multifocal, diffuse, perihilar, unilateral or few very small GGO; (c) atypical appearance (Cov19Aty) characterized by signs of other pneumonias; and (d) negative for pneumonia (Cov19Neg).…”

“…The role of chest CT in patients with clinical suspicion of COVID-19 is evolving with scientific evidence, but there are substantial differences in opinion on when and how the technique should be used for clinical diagnosis. 3 While the American College of Radiology only recommends the use of CT to solve problems, the Fleishner society assigns it a role as an important tool to use if symptoms worsen or in a resource-limited setting for RT-PCR. 8 , 9 …”