AbstractAedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.
Recently, the World Health Organization recognized that efforts to interrupt schistosomiasis transmission through mass drug administration have been ineffective in some regions; one of their new recommended strategies for global schistosomiasis control emphasizes targeting the freshwater snails that transmit schistosome parasites. We sought to identify robust indicators that would enable precision targeting of these snails. At the site of the world's largest recorded schistosomiasis epidemic-the Lower Senegal River Basin in Senegal-intensive sampling revealed positive relationships between intermediate host snails (abundance, density, and prevalence) and human urogenital schistosomiasis reinfection (prevalence and intensity in schoolchildren after drug administration). However, we also found that snail distributions were so patchy in space and time that obtaining useful data required effort that exceeds what is feasible in standard monitoring and control campaigns. Instead, we identified several environmental proxies that were more effective than snail variables for predicting human infection: the area covered by suitable snail habitat (i.e., floating, nonemergent vegetation), the percent cover by suitable snail habitat, and size of the water contact area. Unlike snail surveys, which require hundreds of person-hours per site to conduct, habitat coverage and site area can be quickly estimated with drone or satellite imagery. This, in turn, makes possible large-scale, high-resolution estimation of human urogenital schistosomiasis risk to support targeting of both mass drug administration and snail control efforts. ; Michigan Society of Fellows at the University of Michigan; Sloan Research Fellowship from the Alfred P. Sloan Foundation; Wellcome TrustWellcome Trust [104958/Z/14/Z]; Bill and Melinda Gates FoundationGates Foundation [OPP1114050]; Stanford University Woods Institute for the Environment; Stanford Institute for Innovation in Developing Econ-omies Global Development and Poverty Initiative grant from the Freeman Spogli Institute at Stanford University; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01TW010286]; National Science FoundationNational Science Foundation (NSF) [1414102] ; We are grateful to the laboratory and field technicians who contributed to data collection, including Pape Gueye, Assane Fall, Alassane Ndiaye, Souleymane Sow, Cheikh Thiam, Momar Guindo, and Achile Diatta. C.L.W. was supported by the Michigan Society of Fellows at the University of Michigan and by a Sloan Research Fellowship from the Alfred P. Sloan Foundation. F.A. and M.R. were supported by Wellcome Trust (Schistosomiasis Collections at the Natural History Museum Project 104958/Z/14/Z). G.A.D.L., S.H.S., M.J., I.J.J., A.J.C., and A.J.L. were supported by a grant from the Bill and Melinda Gates Foundation (OPP1114050), a 2018 Environmental Venture Program grant from the Stanford University Woods Institute for the Environment, a Stanford Institute for Innovation in Developing Econ-omies Global Development and Poverty Initiative grant from the Freeman Spogli Institute at Stanford University, a grant from the National Institutes of Health (R01TW010286), and a grant from the National Science Foundation (1414102). Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply endorsement by the US Government. ; Public domain authored by a U.S. government employee
