Temporally consistent predominance and distribution of secondary malaria vectors in the Anopheles community of the upper Zambezi floodplain.

Regional optimisation of malaria vector control approaches requires detailed understanding both of the species composition of Anopheles mosquito communities, and how they vary over spatial and temporal scales. Knowledge of vector community dynamics is particularly important in settings where ecohydrological conditions fluctuate seasonally and inter-annually, such as the Barotse floodplain of the upper Zambezi river. DNA barcoding of anopheline larvae sampled in the 2019 wet season revealed the predominance of secondary vector species, with An. coustani comprising > 80% of sampled larvae and distributed ubiquitously across all ecological zones. Extensive larval sampling, plus a smaller survey of adult mosquitoes, identified geographic clusters of primary vectors, but represented only 2% of anopheline larvae. Comparisons with larval surveys in 2017/2018 and a contemporaneous independent 5-year dataset from adult trapping corroborated this paucity of primary vectors across years, and the consistent numerical dominance of An. coustani and other secondary vectors in both dry and wet seasons, despite substantial inter-annual variation in hydrological conditions. This marked temporal consistency of spatial distribution and anopheline community composition presents an opportunity to target predominant secondary vectors outdoors. Larval source management should be considered, alongside prevalent indoor-based approaches, amongst a diversification of vector control approaches to more effectively combat residual malaria transmission.

Geographically extensive larval surveys reveal an unexpected scarcity of primary vector mosquitoes in a region of persistent malaria transmission in western Zambia.

BACKGROUND: The Barotse floodplains of the upper Zambezi River and its tributaries are a highly dynamic environment, with seasonal flooding and transhumance presenting a shifting mosaic of potential larval habitat and human and livestock blood meals for malaria vector mosquitoes. However, limited entomological surveillance has been undertaken to characterize the vector community in these floodplains and their environs. Such information is necessary as, despite substantial deployment of insecticide-treated nets (ITNs) and indoor residual spraying (IRS) against Anopheles vectors, malaria transmission persists across Barotseland in Zambia's Western Province. METHODS: Geographically extensive larval surveys were undertaken in two health districts along 102 km of transects, at fine spatial resolution, during a dry season and following the peak of the successive wet season. Larvae were sampled within typical Anopheles flight range of human settlements and identified through genetic sequencing of cytochrome c oxidase I and internal transcribed spacer two regions of mitochondrial and nuclear DNA. This facilitated detailed comparison of taxon-specific abundance patterns between ecological zones differentiated by hydrological controls. RESULTS: An unexpected paucity of primary vectors was revealed, with An. gambiae s.l. and An. funestus representing < 2% of 995 sequenced anophelines. Potential secondary vectors predominated in the vector community, primarily An. coustani group species and An. squamosus. While the distribution of An. gambiae s.l. in the study area was highly clustered, secondary vector species were ubiquitous across the landscape in both dry and wet seasons, with some taxon-specific relationships between abundance and ecological zones by season. CONCLUSIONS: The diversity of candidate vector species and their high relative abundance observed across diverse hydro-ecosystems indicate a highly adaptable transmission system, resilient to environmental variation and, potentially, interventions that target only part of the vector community. Larval survey results imply that residual transmission of malaria in Barotseland is being mediated predominantly by secondary vector species, whose known tendencies for crepuscular and outdoor biting renders them largely insensitive to prevalent vector control methods.

A cross-sectional study to identify the distribution and characteristics of licensed and unlicensed private drug shops in rural Eastern Uganda to inform an iCCM intervention to improve health outcomes for children under five years.

INTRODUCTION: Malaria, pneumonia and diarrhea are leading causes of death in young children in Uganda. Between 50-60% of sick children receive treatment from the private sector, especially drug shops. There is an urgent need to improve quality of care and regulation of private drug shops in Uganda. This study was conducted to determine the distribution, the licensing status and characteristics of drug shops in four sub-districts of Kamuli district. METHODS: This study was part of a pre-post cross sectional study that examined the implementation of an integrated Community Case Management (iCCM) intervention for common childhood illness in rural private drug shops in Kamuli District in Eastern Uganda. This mapping exercise used a snowball sampling technique to identify licensed and unlicensed drug shops and collect information about their characteristics. Data were collected using a questionnaire. GPS data were collected for all drug shops. ANALYSIS: Quantitative data were analyzed using SPSS for descriptive statistics. Open ended questions were entered into NVivo 10 and analyzed using thematic analysis strategies. RESULTS: In total, 215 drug shops in 284 villages were located. Of these, 123 (57%) were open and consented to an interview. Only 12 (10%) drug shops were licensed, 93 (76%) were unlicensed, and the licensing status of 18 (15%) was unknown. Most respondents were the owner of the drug shop (88%); most drug sellers reported their qualification as nursing assistants (70%). Drug sellers reported licensing fees and costs of contracting an "in-charge" as barriers to licensing. Nearly all drug shops sold drugs for malaria (91%) and antibiotics (79%).

Climate suitability for European ticks: assessing species distribution models against null models and projection under AR5 climate.

BACKGROUND: There is increasing evidence that the geographic distribution of tick species is changing. Whilst correlative Species Distribution Models (SDMs) have been used to predict areas that are potentially suitable for ticks, models have often been assessed without due consideration for spatial patterns in the data that may inflate the influence of predictor variables on species distributions. This study used null models to rigorously evaluate the role of climate and the potential for climate change to affect future climate suitability for eight European tick species, including several important disease vectors. METHODS: We undertook a comparative assessment of the performance of Maxent and Mahalanobis Distance SDMs based on observed data against those of null models based on null species distributions or null climate data. This enabled the identification of species whose distributions demonstrate a significant association with climate variables. Latest generation (AR5) climate projections were subsequently used to project future climate suitability under four Representative Concentration Pathways (RCPs). RESULTS: Seven out of eight tick species exhibited strong climatic signals within their observed distributions. Future projections intimate varying degrees of northward shift in climate suitability for these tick species, with the greatest shifts forecasted under the most extreme RCPs. Despite the high performance measure obtained for the observed model of Hyalomma lusitanicum, it did not perform significantly better than null models; this may result from the effects of non-climatic factors on its distribution. CONCLUSIONS: By comparing observed SDMs with null models, our results allow confidence that we have identified climate signals in tick distributions that are not simply a consequence of spatial patterns in the data. Observed climate-driven SDMs for seven out of eight species performed significantly better than null models, demonstrating the vulnerability of these tick species to the effects of climate change in the future.