Expert system to model and forecast time series of epidemiological counts with applications to COVID-19

B. González-Pérez, C. Núñez, J.L. Sánchez, G. Valverde, J.M. Velasco

Mathematics Vol. 9, nº. 13, pp. 1485-1 - 1485-34

Resumen:

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.

Palabras Clave: artificial intelligence; machine learning; non-linear regression; error correction model; SIR

Índice de impacto JCR y cuartil WoS: 2,592 - Q1 (2021); 2,300 - Q1 (2023)

Referencia DOI: https://doi.org/10.3390/math9131485

Publicado en papel: Julio 2021.

Publicado on-line: Junio 2021.

Cita:
B. González-Pérez, C. Núñez, J.L. Sánchez, G. Valverde, J.M. Velasco, Expert system to model and forecast time series of epidemiological counts with applications to COVID-19. Mathematics. Vol. 9, nº. 13, pp. 1485-1 - 1485-34, Julio 2021. [Online: Junio 2021]