The objective of the SolarMal project was to test an innovative method to reduce malaria transmission by mass mosquito trapping. They did this by installing solar-powered odour-baited mosquito trapping systems (SMoTS) on Rusinga Island, Lake Victoria, Kenya. In a total of two years the most of all households on Rusinga Island where equipped with a SMoTS. Before, during and after the rollout of this intervention method they monitored the prevalence of malaria mosquitoes on the island. The project concludes amongst others that the presence of SMoTS result in a decrease of malaria prevalence. However, there are more striking and promising results from this project that actually raises new questions. There is a need for more analysis and continuations of this project. So far, for example, the effect of the SMoTS installed by the SolarMal project is not yet fully examined in the spatial dimension incorporating environmental geodata. Also, the entomological monitoring is to a limited extent analysed in a combination of the spatial ánd temporal dimension.
For this thesis topic most of the SolarMal project data is available. Objective of this thesis topic is to perform GIS analysis on this project data and continue the study by mainly adding the spatial dimension into the analysis. Questions that need to be tackled are amongst others, and not limited to:
- How is malaria and its vector mosquito distributed in space and time?
- Can the space-time distribution of malaria and its vector mosquito be related to environmental information (for example water bodies, presence of humans, rainfall, wind speed&direction)?
- Can the space-time distribution of malaria and its vector mosquito be related to the typical spatial rollout of the SMoTS intervention?
- Is it possible to identify (spatial) community protective effects and how do these relate to the household level effect of the SMoTS intervention?
- Analyse the intervention rollout in the space-time dimension and examine its effect on the distribution of malaria and its vector mosquitoes.
- Visualize the entomological data in space and time and relate this with potentially related environmental and/or socio-economic data
Theme(s): Modelling & visualisation