Hybrid Geostatistical and Machine Learning Methods for Predicting Onchocerciasis Prevalence

Onchocerciasis, also known as river blindness, is a parasitic disease caused by Onchocerca volvulus and transmitted through the bites of black flies. It primarily affects rural communities in sub-Saharan Africa, causing severe itching, skin changes, and in some cases, irreversible blindness. Efforts to eliminate onchocerciasis have focused on mass drug administration (MDA) of ivermectin to affected communities. However, the success of these programs relies on precise mapping of disease prevalence to target interventions effectively. With the global push toward onchocerciasis elimination, there is a pressing need for improved predictive models that can identify areas requiring intensified interventions and ensure no community is left behind.

This PhD project will focus on developing advanced predictive models to estimate the prevalence of onchocerciasis at high spatial resolution. The project aims to combine multiple datasets from diverse sources, including environmental variables, and disease prevalence surveys, to enhance prediction accuracy. By using a hybrid approach combining geostatistical and machine learning methods, the project will identify the key environmental drivers and improve disease mapping capabilities. This research has the potential to inform more effective targeting of disease elimination interventions and public health strategies.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in mathematics or statistics.

Funding

At The University of Manchester, we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for. Please contact the supervisor to discuss funding options.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing [Email Address Removed].

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).