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Over 250 million people are estimated to be infected with the parasitic worms that cause schistosomiasis, and over 700 million people live in areas with high infection risks. Schistosomiasis is transmitted via human contact with contaminated freshwater sources. Due to the ecological conditions required to sustain transmission, more than 80% of infected or at-risk individuals are located in rural areas of sub-Saharan Africa where access to safe sanitation and potable water is limited. Importantly, the proximity to contaminated freshwater sources is a key determinant of infection risk. In Uganda, Lake Victoria is a key source of transmission for schistosomiasis. However, it is unknown how the distance of a household to Lake Victoria affects infection risk. There are few studies with such granular data and challenges exist in identifying how best to assign a household to a water contact site (or single point to a Lake site). The objective of this project is to explore data-driven techniques for assigning households to water contact sites and to examine the correlation of those different assignment metrics to the infection status of individuals living in rural poor villages surrounding Lake Victoria. 

This project will take 8 weeks to complete.

Selection Criteria

  • Final year undergraduate, including modules on statistics 
  • Experience with Python 
  • Experience of writing scripts for analysis 
  • Has worked with spatial data (desirable 

 

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