Abstract
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.
Highlights
Mapping distributions of disease-transmitting mosquitoes and monitoring their biting activity can help community members and public health authorities to plan and implement appropriate interventions [1,2,3]
In the first study village (Kivukoni), during the different discussion rounds, opinions of community members suggested that most mosquitoes were found towards the northern part of the village, while southern part of the village had the smallest densities of outdoor-biting doi:10.1371/journal.pone.0156388.g005
This study approach was meant to increase our level of community engagement, and to ensure that community members in these study villages were more aware of the research work that we conducted in their communities
Summary
Mapping distributions of disease-transmitting mosquitoes and monitoring their biting activity can help community members and public health authorities to plan and implement appropriate interventions [1,2,3]. Internet based platforms allow communities of users to pull their knowledge, experiences and resources in ways far beyond any single individual, a technique often referred to us Crowdsourcing [15,16] While such online crowdsourcing approaches increase the speed and capabilities with which we can create solutions, they provide large sets of data, which could be mined to create essential new knowledge and address problems in a highly scalable way [17,18,19]. Such capabilities are only starting to be exploited against major global health problems [18]
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