Understanding polynomial distributed lag models: truncation lag implications for a mosquito-borne disease risk model in Brazil

Nidhi Parikh

Abstract: Using data for the states of Brazil, we construct a polynomial distributed lag model under different truncation lag criteria to predict reported dengue cases. Accurately predicting dengue cases provides the framework to develop forecasting models, which would provide public health professionals time to create targeted interventions for areas at high risk of dengue outbreaks. Others have shown that variables of interest such as temperature and vegetation can be used to predict dengue cases. These models did not detail how truncation lag criteria was chosen for their respective models when polynomial distributed lag was used. We explore current truncation lag selection methods used widely in the literature (marginal and minimized AIC) and determine which of these methods works best for our given data set. While minimized AIC truncation lag selection produced the best fit to our data, this method used substantially more data to inform its prediction compared to the marginal truncation lag selection method. Finally, the following variables were found to be significant predictors of dengue in this region: normalized difference vegetation index (NDVI), green-based normalized difference water index (NDWI), normalized burn ratio (NBR), and temperature. These best predictors were derived from multispectral remote sensing imagery as well as temperature data.