Abstract
Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.
Highlights
Dengue is a mosquito-borne viral infectious disease that has rapidly spread across the world and places tropical countries under a huge socio-economic and disease burden
The primary dengue mosquito vectors breed in containers with sufficient water and nutrition
Aedes aegypti has adapted to human habitats and breeds primarily in artificial water containers such as jars, old tires, and flower pots, whereas Aedes albopictus tended to breed in natural containers such as tree stumps and coconut shells and to a lesser extent in artificial containers
Summary
Dengue is a mosquito-borne viral infectious disease that has rapidly spread across the world and places tropical countries under a huge socio-economic and disease burden. Two species of Aedes mosquitoes, Aedes aegypti and Aedes albopictus are the primary dengue vectors. Containers in the environment are routinely surveyed and container elimination is one of the most effective approaches to dengue control. While larval and container surveys can provide crucial information on mosquito vector populations to help in risk prediction and in targeting control efforts, the labor-intensive nature of the surveys limits their practical scope. Studies incorporating larval counts in risk prediction models have been limited in number [2] and scope and indirect proxies such as socioeconomic status and proximity to vector larval development sites are commonly used in risk prediction models [3]
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