Despite reductions in S. mansoni intensity and hookworm prevalence, intensive MDA had no effect on atopy, allergy-related disease or helminth-related pathology. This could be due to sustained low-intensity infections, thus a causal link between helminths and allergy outcomes cannot be discounted. Intensive community-based MDA has limited impact in high-schistosomiasis-transmission fishing communities, in the absence of other interventions.

Background Spatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach. Methods Drone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence. Results Imagery covering an area of 8.9 km2 across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence. Conclusions This study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control.

IntroductionMolecular xenomonitoring (MX)—pathogen detection in the mosquito rather than human—is a promising tool for lymphatic filariasis (LF) surveillance. In the Recife Metropolitan Region (RMR), the last LF focus in Brazil, Culex quinquefasciatus mosquitoes have been implicated in transmitting Wuchereria bancrofti parasites. This paper presents findings on the ideal mosquito collection method, mosquito dispersion, W. bancrofti infection in mosquitoes and W. bancrofti antigen in humans to aid MX development.MethodsExperiments occurred within two densely populated urban areas of Olinda, RMR, in July and August 2015. U.S. Centers for Disease Control and Prevention (CDC) light traps were compared to battery-powered aspirators as collection methods, and mosquito dispersion was measured by mosquito mark release recapture (MMRR). Female Cx. quinquefasciatus were tested by PCR for W. bancrofti infection, and study area residents were screened by rapid tests for W. bancrofti antigen.ResultsAspirators caught 2.6 times more total Cx. quinquefasciatus, including 38 times more blood-fed and 5 times more gravid stages, than CDC light traps. They also collected 123 times more Aedes aegypti. Of the 9,644 marked mosquitoes released, only ten (0.01%) were recaptured, nine of which were < 50m (34.8m median, 85.4m maximum) from the release point. Of 9,169 unmarked mosquitoes captured in the MMR, 38.3% were unfed, 48.8% blood-fed, 5.5% semi-gravid, and 7.3% gravid. PCR on 182 pools (1,556 mosquitoes) found no evidence of W. bancrofti infection in Cx. quinquefasciatus. Rapid tests on 110 of 111 eligible residents were all negative for W. bancrofti antigen.ConclusionsAspirators were more effective than CDC light traps at capturing Ae. aegypti and all but unfed stages of Cx. quinquefasciatus. Female Cx. quinquefasciatus traveled short (< 86m) distances in this urban area. Lack of evidence for W. bancrofti infection in mosquitoes and antigen in humans in these fine-scale studies does not indicate that LF transmission has ceased in the RMR. A MX surveillance system should consider vector-specific collection methods, mosquito dispersion, and spatial scale but also local context, environmental factors such as sanitation, and host factors such as infection prevalence and treatment history.

Spatial and temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by removing habitat and/or reduce the likelihood of larvae developing into adults, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, we use our experiences of drone-led larval habitat identification in Malawi to assess the accuracy and practicalities of this approach.Drone imagery and larval surveys were conducted in Kasungu district, Malawi between 2018-2020. Water bodies and aquatic vegetation were identified in the imagery using both manual methods and geographical object-based image analysis (GeoOBIA) and the performance of the classifications were compared. Larval sampling sites were characterised by biotic factors visible in drone imagery (e.g. vegetation coverage, type), and generalised linear mixed models were used to determine their association with larval presence.Imagery covering an area of 8.9km2 across eight sites was captured. Characteristics associated with rural larval habitat were successfully identified using GeoOBIA (e.g. median accuracy = 0.98, median kappa = 0.96 using a standard RGB camera), with a median of 18.3% being classed as surface water, compared to 20.1% using manual identification. The GeoOBIA approach, however, required greater processing time and technical skills. Larval samples were captured from 326 sites, and a relationship was identified between larval presence and vegetation (log-OR=1.44, p=0.01). Vegetation type was also a significant factor when considering late stage anopheline larvae only.Our study demonstrates the potential for drone-acquired imagery as a tool to support the identification of mosquito larval habitat in rural areas where malaria is endemic. There are, however, technical challenges to overcome before it can be smoothly integrated into malaria control activities. Further consultations between experts and stakeholders in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited.