1. Generalized Pandemic Model with COVID-19 for Early-Stage Infection Forecasting.
- Author
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Ponce-Flores, Mirna Patricia, Terán-Villanueva, Jesús David, Ibarra-Martínez, Salvador, and Castán-Rocha, José Antonio
- Subjects
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COVID-19 , *COVID-19 pandemic , *FORECASTING , *TIME series analysis , *RANDOM forest algorithms - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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