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

Study areas.

<p>Map showing the three villages where the study was conducted (Kivukoni, Minepa and Mavimba) in rural Ulanga district, southeastern Tanzania.</p

Maps of community opinions in dry and wet seasons.

<p>Examples of gridded village maps showing wet season and dry season differences observed on the interpolated surfaces of community opinions on where mosquito densities are high, medium or low in Kivukoni, Minepa and Mavimba villages at different times during the study period.</p

Comparison of mosquito catches in areas classified by communities as having high, medium or low mosquito densities in dry season and wet season.

<p>Median nightly mosquito catches in areas marked by community members as having high mosquito densities, medium densities or low densities in all villages during wet season (upper panel), and dry season (lower panel). Data segregated by taxa, but combined over 12 months. The error bars in this graph represent the inter-quartile ranges, i.e. 25^th percentile and 75^th percentile on either side of the median nightly catch. Data for the wet season included months of December, January, February, March, April and May, while the dry season data included June, July, August, September, October and November.</p

Comparison of mosquito catches in areas classified by communities as having high, medium or low mosquito densities in different villages.

<p>Median nightly mosquito catches in areas marked by community members as having high, medium or low outdoor-biting mosquito densities in Kivukoni, Minepa and Mavimba villages. Data combined for all mosquito species over 12 months. The error bars in this graph represent the inter-quartile range, i.e. 25^th percentile and 75^th percentile on either side of the median nightly catch.</p

Monthly comparisons of mosquito catches in areas classified by communities as having high, medium or low mosquito densities.

<p>Month by month variations of nightly mosquito catches in areas marked by community members as having high mosquito densities, medium densities or low densities in the different months of collection, between July 2012 and June 2013. Data aggregated for all three study villages.</p

Main stages in the process of crowdsourcing vector surveillance.

<p>Illustration of the five main steps when crowdsourcing for community knowledge and experiences to predict or approximate densities and distribution of outdoor-biting mosquitoes.</p

Directly observed physical features predominant in locations marked by community members as having high, medium and low outdoor mosquito biting densites in the three study villages.

<p>Directly observed physical features predominant in locations marked by community members as having high, medium and low outdoor mosquito biting densites in the three study villages.</p

The M-Trap.

<p>Pictures of the odour-baited trap, the M-trap, used for comparative assessment of mosquito densities. Vertical envelope-shaped mosquito entry points are labelled. In our study, no human volunteer occupied the trap, and instead we relied on synthetic mosquito attractants complemented with carbon-dioxide gas.</p