Time series for predicting infectious disease outbreaks in Latin America
DOI:
https://doi.org/10.70577/9j5qky84Keywords:
diseases, epidemiology, models, prediction, time series.Abstract
This study analyzes the predictive effectiveness of time series models applied to infectious disease outbreaks in Latin America, using a data science approach. Two approaches were compared: the seasonal SARIMA model and a hybrid SARIMA + NNAAR (Autoregressive Neural Network) model. The results show that, although SARIMA presents limited explanatory power (negative R²), it maintains acceptable performance in terms of error (RMSE=1.55; MAE=0.87). In contrast, the hybrid model showed inferior performance, with higher errors and an even more negative R², indicating that the incorporation of a neural network does not necessarily improve the system's predictive capacity. The learning curve of the NNAAR model suggests possible undertraining, reinforcing the need for careful calibration when integrating complex models. The study highlights the importance of selecting models based on data structure, beyond technical sophistication, and recommends methodological optimizations before implementing hybrid models in epidemiological surveillance systems. This analysis, based on realistic simulated data, underscores the value of time series methodologies for disease prediction and public health decision-making.
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