Variations in household microclimate affect outdoor-biting behaviour of malaria vectors

Background: Mosquito behaviours including the degree to which they bite inside houses or outside is a crucial determinant of human exposure to malaria. Whilst seasonality in mosquito vector abundance is well documented, much less is known about the impact of climate on mosquito behaviour. We investigated how variations in household microclimate affect outdoor-biting by malaria vectors, Anopheles arabiensis and Anopheles funestus. Methods: Mosquitoes were sampled indoors and outdoors weekly using human landing catches at eight households in four villages in south-eastern Tanzania, resulting in 616 trap-nights over 12 months. Daily temperature, relative humidity and rainfall were recorded. Generalized additive mixed models (GAMMs) were used to test associations between mosquito abundance and the microclimatic conditions. Generalized linear mixed models (GLMMs) were used to investigate the influence of microclimatic conditions on the tendency of vectors to bite outdoors (proportion of outdoor biting). Results:  An. arabiensis abundance peaked during high rainfall months (February-May), whilst An. funestus density remained stable into the dry season (May-August) . Across the range of observed household temperatures, a rise of 1 ºC marginally increased nightly An. arabiensis abundance (~11%), but more prominently increased An. funestus abundance (~66%). The abundance of An. arabiensis and An. funestus showed strong positive associations with time-lagged rainfall (2-3 and 3-4 weeks before sampling). The degree of outdoor biting in An. arabiensis was significantly associated with the relative temperature difference between indoor and outdoor environments, with exophily increasing as temperature inside houses became relatively warmer. The exophily of An. funestus did not vary with temperature differences.   Conclusions: This study demonstrates that malaria vector An. arabiensis shifts the location of its biting from indoors to outdoors in association with relative differences in microclimatic conditions. These environmental impacts could give rise to seasonal variation in mosquito biting behaviour and degree of protection provided by indoor-based vector control strategies.

Crowdsourcing Vector Surveillance: Using Community Knowledge and Experiences to Predict Densities and Distribution of Outdoor-Biting Mosquitoes in Rural Tanzania

Lack of reliable techniques for large-scale monitoring of disease-transmitting mosquitoes is a major public health challenge, especially where advanced geo-information systems are not regularly applicable. We tested an innovative crowd-sourcing approach, which relies simply on knowledge and experiences of residents to rapidly predict areas where disease-transmitting mosquitoes are most abundant. Guided by community-based resource persons, we mapped boundaries and major physical features in three rural Tanzanian villages. We then selected 60 community members, taught them basic map-reading skills, and offered them gridded maps of their own villages (grid size: 200m×200m) so they could identify locations where they believed mosquitoes were most abundant, by ranking the grids from one (highest density) to five (lowest density). The ranks were interpolated in ArcGIS-10 (ESRI-USA) using inverse distance weighting (IDW) method, and re-classified to depict areas people believed had high, medium and low mosquito densities. Finally, we used odor-baited mosquito traps to compare and verify actual outdoor mosquito densities in the same areas. We repeated this process for 12 months, each time with a different group of 60 residents. All entomological surveys depicted similar geographical stratification of mosquito densities in areas classified by community members as having high, medium and low vector abundance. These similarities were observed when all mosquito species were combined, and also when only malaria vectors were considered. Of the 12,412 mosquitoes caught, 60.9% (7,555) were from areas considered by community members as having high mosquito densities, 28% (3,470) from medium density areas, and 11.2% (1,387) from low density areas. This study provides evidence that we can rely on community knowledge and experiences to identify areas where mosquitoes are most abundant or least abundant, even without entomological surveys. This crowd-sourcing method could be further refined and validated to improve community-based planning of mosquito control operations at low-cost.