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Understanding polynomial distributed lag models: truncation lag implications for a mosquito-borne disease risk model in Brazil
Author(s): Jessica Conrad; Amanda Ziemann; Randall Refeld; Nidhi Parikh; Amir Siraj; Nicholas Generous; Sara Del Valle; Geoffrey Fairchild; Carrie Manore
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Paper 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.

Paper Details

Date Published: 14 May 2019
PDF: 11 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109860N (14 May 2019); doi: 10.1117/12.2536369
Show Author Affiliations
Jessica Conrad, Los Alamos National Lab. (United States)
Amanda Ziemann, Los Alamos National Lab. (United States)
Randall Refeld, Tulane Univ. (United States)
Nidhi Parikh, Los Alamos National Lab. (United States)
Amir Siraj, Univ. of Notre Dame (United States)
Nicholas Generous, Los Alamos National Lab. (United States)
Sara Del Valle, Los Alamos National Lab. (United States)
Geoffrey Fairchild, Los Alamos National Lab. (United States)
Carrie Manore, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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