Prescriptive temporal modeling approach using climate variables to forecast dengue incidence in Córdoba, Colombia.

Abstract

We present a modeling strategy to forecast the incidence rate of dengue in the department of Córdoba, Colombia, thereby considering the effect of climate variables. A Seasonal Autoregressive Integrated Moving Average model with exogenous variables (SARIMAX) model is fitted under a cross-validation approach, and we examine the effect of the exogenous variables on the performance of the model. This study uses data of dengue cases, precipitation, and relative humidity reported from years 2007 to 2021. We consider three configurations of sizes training set-test set: 182-13,189-6, and 192-3. The results support the theory of the relationship between precipitation, relative humidity, and dengue incidence rate. We find that the performance of the models improves when the time series models are previously adjusted for each of the exogenous variables, and their forecasts are used to determine the future values of the dengue incidence rate. Additionally, we find that the configurations 189-6 and 192-3 present the most consistent results with regard to the model's performance in the training and test data sets.