A Time Series Exploration of Diarrheal Disease Incidence in Mexico: Predictive Modeling for Public Health

Abstract

Diarrhea remains a significant public health concern in Mexico, leading to substantial morbidity and mortality. In this study, we employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast the future incidence of diarrhea disease deaths in Mexico. The analysis included the evaluation of various time series diagnostics, including the Augmented Dickey-Fuller (ADF) test, autocorrelation function (ACF), partial autocorrelation function (PACF), and the Box-Jenkins model, to ensure the validity and accuracy of the forecasts. By implementing advanced time series techniques, we aimed to provide crucial insights into the trends and potential future trajectories of diarrhea-related mortality in Mexico, enabling better preparedness and resource allocation for public health interventions.