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Incidence Moments: A Simple and Accurate Method to Study the Memory and Short Term Forecast of the COVID-19 Incidence Time-Series

31 Pages Posted: 1 Jun 2021

See all articles by Mauricio Lambarri Canals

Mauricio Lambarri Canals

University of Chile - Environmental Health Program

Andrea Canals

University of Chile - Programa de Bioestadística

Cristóbal Cuadrado Nahum

University of Chile - School of Public Health

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Abstract

Summary: The ability to predict COVID-19 dynamic has been very low, because of unexpected changes in the number of cases. Here we 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 the first five incidence moments and the reported incidence determining the best predictor. We compared the predictions and observed COVID incidences with correlation analysis and Bland-Altman diagrams for the world, for Chile and its regions, and for 43 other countries.

Findings: We find that the best predictor is the third moment of incidence, determining a temporal prediction window of incidence of 15 days. After 15 days the correlation between the observed and expected values ​​decrease under r = 0.8. The incidence series of the world, Chile and its regions and the rest of the countries show a high correlation between observed-expected values, with 8 countries with very high correlations. The method is better when the population size is high and presents distortions when there are abrupt changes in incidence.

Interpretation: This study shows that the epidemic dynamics of COVID 19 has a very short prediction window, probably associated with an intrinsic chaotic behavior of its dynamics. The present analyzes also show that the incidence moments modeling approach could be useful as a simple tool for decision makers to short-term monitor the course of an epidemic. The simplicity of the proposed method is appealing since it allows a rapid implementation in different settings, even with limited technical epidemiological capacities and without requiring extensive computational.

Funding Information: ANID COVID 0960 Grant “Impact of COVID-19 in Chile. A transdisciplinary evaluation of the response to the pandemic and its consequences”.

Declaration of Interests: The authors have completed the ICMJE declaration of conflicts of interest form, and declare that they have not received funding to prepare the report; not have financial relationships with organizations that may have an interest in the published article, in the last three years; and not having other relationships or activities that could influence the published article. The forms can be requested by contacting the responsible author or the editorial direction of the Journal.

Ethics Approval Statement: Our study is based on official secondary data reported by the Ministry of Health of Chile; therefore, it did not require approval from the Faculty of Medicine ethics committee.

Keywords: COVID 19; epidemiology; forecast

Suggested Citation

Canals, Mauricio Lambarri and Canals, Andrea and Cuadrado Nahum, Cristóbal, Incidence Moments: A Simple and Accurate Method to Study the Memory and Short Term Forecast of the COVID-19 Incidence Time-Series. Available at SSRN: https://ssrn.com/abstract=3857599 or http://dx.doi.org/10.2139/ssrn.3857599

Mauricio Lambarri Canals (Contact Author)

University of Chile - Environmental Health Program ( email )

Chile

Andrea Canals

University of Chile - Programa de Bioestadística ( email )

Av. Independencia 1027
Santiago, 7520421
Chile

Cristóbal Cuadrado Nahum

University of Chile - School of Public Health ( email )

Chile

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