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

Jessica Conrad; Amanda Ziemann; Randall Refeld; Nidhi Parikh; Amir Siraj; Nicholas Generous; Sara Del Valle; Geoffrey Fairchild; Carrie Manore

doi:10.1117/12.2536369

14 May 2019 Understanding polynomial distributed lag models: truncation lag implications for a mosquito-borne disease risk model in Brazil

Jessica Conrad, Amanda Ziemann, Randall Refeld, Nidhi Parikh, Amir Siraj, Nicholas Generous, Sara Del Valle, Geoffrey Fairchild, Carrie Manore

Author Affiliations +

Proceedings Volume 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV; 109860N (2019) https://doi.org/10.1117/12.2536369
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Jessica Conrad, Amanda Ziemann, Randall Refeld, Nidhi Parikh, Amir Siraj, Nicholas Generous, Sara Del Valle, Geoffrey Fairchild, and Carrie Manore "Understanding polynomial distributed lag models: truncation lag implications for a mosquito-borne disease risk model in Brazil", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109860N (14 May 2019); https://doi.org/10.1117/12.2536369

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Vegetation

Humidity

Landsat

Statistical analysis

Data acquisition

Remote sensing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years