An examination of the co-distribution of malaria and schistosomiasis in rural Uganda

BACKGROUND

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.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

This project will use data from an ongoing cohort study (SchistoTrack/EpiMM-Schisto) in rural villages in Uganda where S. mansoni is endemic. Ethical approvals have been obtained. The baseline and first annual follow-up will be collected prior to the start of this DPhil project. Three more years of follow-up will occur during the timeframe of this DPhil. This analysis will focus on a random sample of approximately 1800 households from 45 villages. To measure infection status/intensity, one child (aged 5+ years) and one adult (aged 18+ years) will be sampled from each study household (~3600 individuals). 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.

Aims:

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, building on schistosomiasis models already available in the group.
4. Understand the type and relevance of co-infection interactions by age.

There is scope to tailor the project, especially the modelling/machine learning methods, to the student’s interest. The student will gain skills in literature review, primary data collection, clinical epidemiological data analysis, statistical programming, data cleaning, and research presentation.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

This DPhil project requires 1-2 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.

PROSPECTIVE STUDENT

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.