Affiliation:
1. Departamento de Posgrado e Investigación, Facultad de Ingeniería de Tampico, Universidad Autónoma de Tamaulipas, Tampico 89336, Mexico
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.
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference35 articles.
1. Successive waves of COVID-19: Confinement effects on virus-prevalence with a mathematical model;Abdalla;Eur. J. Med. Res.,2021
2. Modeling the Waves of COVID-19;Cherednik;Acta Biotheor.,2022
3. Cruz-Nájera, M.A., Treviño-Berrones, M.G., Ponce-Flores, M.P., Terán-Villanueva, J.D., Castán-Rocha, J.A., Ibarra-Martínez, S., Santiago, A., and Laria-Menchaca, J. (2022). Short Time Series Forecasting: Recommended Methods and Techniques. Symmetry, 14.
4. The spreading of COVID-19 in Mexico: A diffusional approach;Results Phys.,2021
5. Mathematical model of Boltzmann’s sigmoidal equation applicable to the spreading of the coronavirus (COVID-19) waves;Guettari;Environ. Sci. Pollut. Res.,2021