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Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand.

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.

SUBMITTER: Su Yin M

PROVIDER: S-EPMC7971869 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recent years have seen the greatest ecological disturbances of our times, with global human expansion, species and habitat loss, climate change, and the emergence of new and previously-known infectious diseases. Biodiversity loss affects infectious disease risk by disrupting normal relationships between hosts and pathogens. Mosquito-borne pathogens respond to changing dynamics on multiple transmission levels and appear to increase in disturbed systems, yet current knowledge of mosquito diversity and the relative abundance of vectors as a function of habitat change is limited. We characterize mosquito communities across habitats with differing levels of anthropogenic ecological disturbance in central Thailand. During the 2008 rainy season, adult mosquito collections from 24 sites, representing 6 habitat types ranging from forest to urban, yielded 62,126 intact female mosquitoes (83,325 total mosquitoes) that were assigned to 109 taxa. Female mosquito abundance was highest in rice fields and lowest in forests. Diversity indices and rarefied species richness estimates indicate the mosquito fauna was more diverse in rural and less diverse in rice field habitats, while extrapolated estimates of true richness (Chao1 and ACE) indicated higher diversity in the forest and fragmented forest habitats and lower diversity in the urban. Culex sp. (Vishnui subgroup) was the most common taxon found overall and the most frequent in fragmented forest, rice field, rural, and suburban habitats. The distributions of species of medical importance differed significantly across habitat types and were always lowest in the intact, forest habitat. The relative abundance of key vector species, Aedes aegypti and Culex quinquefasciatus, was negatively correlated with diversity, suggesting that direct species interactions and/or habitat-mediated factors differentially affecting invasive disease vectors may be important mechanisms linking biodiversity loss to human health. Our results are an important first step for understanding the dynamics of mosquito vector distributions under changing environmental features across landscapes of Thailand.

Dataset Information

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand.

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