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external supervisor

Dr Renaud Lambiotte, Department of Mathematics


An estimated 250 million people worldwide have schistosomiasis infections, of which 80% are in sub-Saharan Africa. Blood flukes (parasitic worms) cause schistosomiasis. Transmission to humans is via contact with freshwater sources that are contaminated through open defecation/urination and that harbour competent intermediate snail hosts. Treatment with the only available medicine (praziquantel) does not prevent reinfection; individuals living in high transmission areas are rapidly reinfected.

The location of residence, ecological conditions, and movement of individuals has been shown to influence schistosomiasis infection risk. Water depth, temperature, and vegetation amongst other factors determine the presence of competent intermediate snail hosts. Water contact by infected individuals influences whether a water body is a transmission site. The frequency of water site visits and number of people using the water site will modify infection likelihood from water contact. Despite these complex interactions, individual mobility patterns have yet to be examined against schistosomiasis infection risk. The focus has been on highly aggregated data, using administrative units such as villages or districts to identify areas with schistosome transmission. However, transmission sites can vary on the scale of meters. There remains a need to understand local spatial variation in human movement and water contact patterns.


This project will use data from ongoing studies in rural villages in Uganda where Schistosoma mansoni (intestinal schistosomiasis) is endemic. Ethics approval will be obtained prior to the start of this project.  Depending on restrictions due to the ongoing pandemic, it is expected that this data will be collected prior to the start of this DPhil project. Otherwise, this data will be collected within the first year of the DPhil project. The study designs are as follows. A cross-sectional survey of 600 individuals in 300 households from 10 villages will be completed. To measure infection status/intensity, one child (aged 5+ years) and one adult (aged 18+ years) will be sampled from each household. Household surveys will be conducted to gather socioeconomic, water, sanitation, and hygiene information. For the 600 study participants, GPS loggers will be worn for approximately 7-10 days. A waypoint will be recorded approximately every 1-3 minutes; recordings of waypoints are dependent on the frequency of individual movements. The DPhil candidate will have the opportunity to contribute to the existing fieldwork study designs through follow-up studies. The DPhil candidate is expected to lead the collection of secondary data related to satellite imagery to overlay with the GPS data.

The focus of analyses will be on data collected from the wearable GPS loggers. Additional spatial data to be collected will include waypoints of households, health centres, schools, and village water and sanitation infrastructure. The specific DPhil project is subject to further discussion and interests. Possible areas include:
  1. Assignment of individuals to water contact sites in order to classify transmission risk and composition of individuals associated to particular sites;
  2. Analysis of movement patterns, including order and frequency of water site visits, and regularity versus diversity of mobility, to understand the influence of individual trajectories or possible super spreaders/contaminators; 
  3. Assignment of individuals to school and health centre catchments in order to investigate new strategies for defining transmission areas (as opposed to using administrative units).

The student will gain skills in literature review, study design, primary data collection, schistosomiasis epidemiology, geostatistical analysis, statistical programming, data cleaning, and research presentation.


This project requires approximately 1-2 months of fieldwork in rural Uganda over the course of the DPhil project. The study is in close collaboration with the Uganda Ministry of Health.

Prospective candidate

Candidates ideally will have postgraduate training in epidemiology, statistics, or a related discipline. Experience handling geospatial data is desirable.