Generalized Pandemic Model with COVID-19 for Early-Stage Infection Forecasting

Mirna Patricia Ponce-Flores, Jesús David Terán-Villanueva (), Salvador Ibarra-Martínez and José Antonio Castán-Rocha
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Mirna Patricia Ponce-Flores: Departamento de Posgrado e Investigación, Facultad de Ingeniería de Tampico, Universidad Autónoma de Tamaulipas, Tampico 89336, Mexico
Jesús David Terán-Villanueva: Departamento de Posgrado e Investigación, Facultad de Ingeniería de Tampico, Universidad Autónoma de Tamaulipas, Tampico 89336, Mexico
Salvador Ibarra-Martínez: Departamento de Posgrado e Investigación, Facultad de Ingeniería de Tampico, Universidad Autónoma de Tamaulipas, Tampico 89336, Mexico
José Antonio Castán-Rocha: Departamento de Posgrado e Investigación, Facultad de Ingeniería de Tampico, Universidad Autónoma de Tamaulipas, Tampico 89336, Mexico

Mathematics, 2023, vol. 11, issue 18, 1-18

Abstract: In this paper, we tackle the problem of forecasting future pandemics by training models with a COVID-19 time series. We tested this approach by producing one model and using it to forecast a non-trained time series; however, we limited this paper to the eight states with the highest population density in Mexico. We propose a generalized pandemic forecasting framework that transforms the time series into a dataset via three different transformations using random forest and backward transformations. Additionally, we tested the impact of the horizon and dataset window sizes for the training phase. A Wilcoxon test showed that the best transformation technique statistically outperformed the other two transformations with 100% certainty. The best transformation included the accumulated efforts of the other two plus a normalization that helped rescale the non-trained time series, improving the sMAPE from the value of 25.48 attained for the second-best transformation to 13.53. The figures in the experimentation section show promising results regarding the possibility of forecasting the early stages of future pandemics with trained data from the COVID-19 time series.

Keywords: Mexico pandemic prediction; future pandemic forecasting; time series transformation to dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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