Space-time conditional autoregressive modeling to estimate neighborhood-level risks for dengue fever in Cali, Colombia

Abstract

Vector-borne diseases (VBDs) affect more than 1 billion people a year worldwide, cause over 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 Aedes aegypti and Aedes albopictus mosquitoes. Since both Aedes 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 2015-2016; 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 Aedes 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.

A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

“…More than half of the studies (n = 98, 53.6%) included temperature as a covariate while around 40% of studies had rainfall (n = 79, 43.2%). Temperature and rainfall were better fit when lagged one or two months rather than unlagged [38][39][40][41]. Temperature and rainfall were considered as significant factors in most studies, but some studies showed that meteorological factors alone are not sufficient to explain spatial heterogeneity in disease transmission, which may be associated more with non-climatic factors [42][43][44].…”

“…For the 33 studies that both checked the multicollinearity of covariates and performed variable selection, we summarised the retention rate of different groups of covariates in the final models (Fig 6A) [27,32,[38][39][40][41][42][43][44][46][47][48]54,59,60,72,78,79,86,93,[99][100][101][102][103][104][105][106][107][108][109][110][111][112]. Of 33 studies, 25 studies (96.2%) retained climatic variables when tested.…”

“…Ecological data (7 tested and 2 rejected) and spatiotemporal incidence (5 tested and 1 rejected) were the least tested and included (Fig 6A). The most common combinations of retained categories were climatic, environmental, demography, and socio-economic (n = 4) [41,44,103,108]. For climatic variables, different measures of temperature and rainfall were tested in reviewed studies.…”