Expert System to Model and Forecast Time Series of Epidemiological Counts with Applications to COVID-19

Beatriz González-Pérez, Concepción Núñez, José L. Sánchez, Gabriel Valverde and José Manuel Velasco
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Beatriz González-Pérez: Department of Statistics and Operations Research, Complutense University of Madrid (UCM), 28040 Madrid, Spain
Concepción Núñez: Laboratory of Research in Genetics of Complex Diseases, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain
José L. Sánchez: Department of Statistics and Operations Research, Complutense University of Madrid (UCM), 28040 Madrid, Spain
Gabriel Valverde: Department of Statistics and Operations Research, Complutense University of Madrid (UCM), 28040 Madrid, Spain
José Manuel Velasco: Computer Architecture and Automation Department, Complutense University of Madrid (UCM), 28040 Madrid, Spain

Mathematics, 2021, vol. 9, issue 13, 1-34

Abstract: We developed two models for real-time monitoring and forecasting of the evolution of the COVID-19 pandemic: a non-linear regression model and an error correction model. Our strategy allows us to detect pandemic peaks and make short- and long-term forecasts of the number of infected, deaths and people requiring hospitalization and intensive care. The non-linear regression model is implemented in an expert system that automatically allows the user to fit and forecast through a graphical interface. This system is equipped with a control procedure to detect trend changes and define the end of one wave and the beginning of another. Moreover, it depends on only four parameters per series that are easy to interpret and monitor along time for each variable. This feature enables us to study the effect of interventions over time in order to advise how to proceed in future outbreaks. The error correction model developed works with cointegration between series and has a great forecast capacity. Our system is prepared to work in parallel in all the Autonomous Communities of Spain. Moreover, our models are compared with a SIR model extension (SCIR) and several models of artificial intelligence.

Keywords: artificial intelligence; machine learning; non-linear regression; error correction model; SIR (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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