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EXTERNAL Supervisor

Professor Deirdre Hollingsworth, Nuffield Department of Medicine


Worldwide, an estimated 241 million people have malaria and 250 million have schistosomiasis, of these cases over 90% are in sub-Saharan Africa. The transmission cycles for malaria and schistosomiasis vary markedly. The protozoan pathogens that cause malaria are transmitted by mosquito and schistosomiasis is caused by a blood fluke that is transmitted via contact with contaminated water where competent intermediate hosts (snails) are present. Yet, shared factors related to socioeconomic status and the local ecology can affect infection risk for both pathogens. There also is evidence that certain antimalarials, e.g. coartem, can inadvertently treat schistosome infections. Conditions independently associated with malaria and schistosomiasis can become co-dependent within an individual exacerbating disease conditions such as splenomegaly (enlarged spleens) and anaemia (usually iron-deficiency or red blood cell sequestration). Both schistosomiasis and malaria use methods of community-based distribution programmes for medicine and rely on diagnosis in primary health care facilities (if any diagnosis is used at all). Despite schistosomiasis and malaria interacting across the dimensions of their risk factors, transmission dynamics, treatment spillovers/complications, and synergistic/antagonistic conditions, no predictive models have been constructed to understand this complexity.


This project will use data from an ongoing cohort study (SchistoTrack) in rural villages in Uganda where S. mansoni is endemic. Ethical approvals have been obtained. The baseline and two annual follow-ups will be completed prior to the start of this DPhil project. Three more years of follow-up will occur during the timeframe of this DPhil. A random sample of approximately 2400 households from 52 villages (~4000 individuals aged 5+ years) will be available for analysis. Both rapid diagnostics and microscopy data are available for both infections. Detailed information will be available on demographics, socioeconomic status, and water, sanitation, and hygiene access/behaviours, ecology, household location, and individual mobility (GPS trackers). Remote sensing data also will be available.


  1. Establish the epidemiology of malaria infection status, intensity, and mixed species infections in the study population.
  2. Identify shared pathways between malaria and schistosomiasis using Bayesian networks comparing differences in using expert opinion, machine learned networks, or a combination thereof.
  3. Develop co-distribution models for malaria and schistosomiasis, examining also mixing patterns among uninfected, singularly infected, and coinfected individuals.
  4. Understand the type and relevance of co-infection interactions by age and the influence on splenic pathologies.

The student will gain skills in literature review, primary data collection, clinical epidemiological data analysis, statistical programming, data cleaning, and research presentation.


This DPhil project requires one to two months of fieldwork in rural Uganda. Experienced field teams from the Uganda Ministry of Health will co-lead the primary data collection with the primary supervisor.


The ideal candidate will have a Master’s degree in statistics/epidemiology/public health or a related quantitative discipline in computer science, mathematics, engineering, or economics. This post is particularly suited to someone from a strong computational background.