Project Lead(s): Dickson Lwetoijera
Issue
Mosquito-borne diseases (such as malaria and arboviruses) are major challenges in Africa.
In Tanzania, these diseases affect 12 million and kill about 70,000 people annually.
While ongoing interventions are having significant impact, techniques for mapping intra-village variations that would allow targeting of hotspots of residual transmission are lacking.
Solution
The project aimed to create a geo-information system to map intra-village variations in the distribution and density of malaria vectors.
To accomplish the study objectives, the team surveyed and mapped the targeted study area – Morogoro region, southeastern Tanzania, Ulanga and Kilombero districts and Dar es Salaam on the eastern coast in Ilala district – using global positioning system (GPS) receivers.
The GPS data were then imported into ArcGIS Desktop 10 (ESRI Eastern Africa) and used to prepare maps of the study area, identifying active vector breeding habitats or significant vector population densities.
To validate the community map data, the team conducted entomological surveillance (i.e., adults and larval density surveillance, using odour-baited outdoor mosquito traps).
Significantly, more disease-transmitting mosquitoes were consistently caught in areas with a high density of environmental and anthropogenic variables, compared to medium- or low-density distributions of environmental and anthropogenic variables across all study villages.
Outcome
The team successfully identified the areas where mosquitoes were either more or less abundant within the villages through observational changes, and distribution of the environmental variables and anthropogenic features.
These changes were precisely and reliably recorded and updated by village community members, who can associate mosquito abundance and the environmental and anthropogenic variables in their locale.
The project team found relying on community knowledge to identify malaria hotspots was cost-effective and sustainable for guiding large-scale implementation of outdoor control devices to lure and kill disease-transmitting mosquitoes.
The team has developed a predictive model that incorporates the data collected from the home-based geo-information system to identify areas that should be targeted with complementary interventions.
Information about the project has been disseminated at conferences.
The project team has not yet decided whether to apply for Transition To Scale funding.