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Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia
Journal article   Open access   Peer reviewed

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia

Michael R Desjardins, Matthew D Eastin, Rajib Paul, Irene Casas and Eric M Delmelle
The American journal of tropical medicine and hygiene, v 103(5), pp 2040-2053
01 Nov 2020
DOI: https://doi.org/10.4269/ajtmh.20-0080
PMID: 32876013
url
https://doi.org/10.4269/ajtmh.20-0080View
Published, Version of Record (VoR) Open

Abstract

Aedes - virology Animals Colombia - epidemiology Demography Dengue - epidemiology Dengue - transmission Humans Models, Biological Mosquito Vectors - virology Risk Factors Spatio-Temporal Analysis Weather
Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female and mosquitoes. Because both species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level-where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space-time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

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