Malaria is a vector-borne disease caused by parasites of the genus Plasmodium, with 5 species being able to infect humans. Even though it is preventable and curable, malaria is considered a neglected tropical disease as it still represents a major economic, social, and health burden in low and middle income countries (LMIC). In 2022, 249 million cases were registered in 85 endemic countries and areas, resulting in an increase of 5 million cases compared to the previous year. In many of these endemic areas, the increased malaria incidence in the last few years can be associated with the disruption of services caused by the COVID-19 pandemic and other humanitarian and health emergencies. It is important to note that only 29 countries account for 95% of the global malaria incidence, most of them being part of the WHO African Region.

Mathematical models have historically been used to understand malaria transmission dynamics, slowly evolving along with our understanding of its complex host-parasite biology. Knowledge of disease dynamics can be combined with socio-economic considerations to produce models robust enough to simulate and compare control strategies, thus becoming useful tools to inform health policy making. Throughout the years, a few comprehensive epidemiological-economic modelling endeavours have addressed control and elimination challenges in sub-Saharan Africa, South-East Asia and Latin America, and have been able to offer context-informed recommendations and communicate them to key stakeholders. With newly approved vaccines and robust antimalarials offering renewed hope, mathematical models still represent a key tool to design effective prevention, control and elimination strategies.

The aim of this project is to develop robust multi-species malaria models, implement the effect of key baseline interventions and use these models to evaluate a series of context-informed elimination strategies. These models will be adapted and applied across different populations, taking into account each region’s geographic, demographic and socio-economic particularities.

The student will be hosted by the Aberdeen Centre for Health Data Science (ACHDS), a multi-disciplinary group of modellers, data scientists and clinicians working with the aim to improve health using data science and modelling tools. There will also be opportunities to engage with international collaborators in the Modelling and Simulation Hub, Africa (MASHA), a research group based in the University of Cape Town focused on the development of mathematical models aimed to inform health policy making. The supervisory team includes Dr Caroline Franco (Physicist, Lecturer in the ACHDS and mathematical modeller of infectious diseases), Dr Dimitra Blana (Biomedical Engineer, Deputy Director of the ACHDS and Lecturer in ACHDS with extensive experience in modelling of dynamic systems), and Assoc Prof Sheetal Silal (head of MASHA and experienced mathematical modeller of tropical infectious diseases, with particular focus on malaria control and elimination strategies).

Candidate background:

The student should be a highly motivated individual with strong problem solving skills and interest in tackling global health problems. Previous experience in R or a similar programming language and a strong technical background are recommended.

Candidates should hold (or expect to achieve) a minimum of a First Class Honours degree in Physics, Mathematics, Data Science, or another relevant subject. Applicants with a minimum of a 2.1 Honours degree may be considered provided they have a Distinction at Master's level.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.

Informal enquiries are encouraged, please contact Dr Caroline Franco (caroline.franco@abdn.ac.uk) for further information.

Interested candidates must contact the lead supervisor to discuss the project in advance of applying, as supervisors will be expected to provide a letter of support for suitable applicants.

APPLICATION PROCEDURE:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
You should apply for Applied Health sciences (PhD) to ensure your application is passed to the correct team for processing.
Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.
Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your degree certificates and transcripts.
Please note: you DO NOT need to provide a research proposal with this application
Please ensure all required documents are provided as incomplete applications will not be considered.
If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at pgrs-admissions@abdn.ac.uk
Please note: International applicants are eligible to apply for this studentship, but due to funding limitations will be required to find additional funding to cover the difference between overseas and home fees (approximately £17,000 per annum). An international applicant receiving an offer will be required to provide evidence they have these funds available.

Funding Notes

This four-year research project is competition funded. It is in competition for funding with other projects advertised by the University of Aberdeen. The project receiving the most suitable applicant will be awarded project funding.
Funding is provided by the School of Medicine, Medical Sciences & Nutrition and includes tuition fees at the UK/Home rate, research costs, and an annual doctoral stipend for living costs (£17,668 for the 2023/2024 academic year)
Overseas candidates may apply for this studentship but will have to find additional funding to cover the difference between overseas and home fees (~£17,000 per annum).

References

• World malaria report 2023. Geneva: World Health Organization; 2023. Licence: CC BY-NC-SA 3.0 IGO.
• Mandal, S., Sarkar, R.R. & Sinha, S. Mathematical models of malaria - a review. Malar J 10, 202 (2011). https://doi.org/10.1186/1475-2875-10-202
• Silal SP, Shretta R, Celhay OJ et al. Malaria elimination transmission and costing in the Asia-Pacific: a multi-species dynamic transmission model [version 2; peer review: 1 approved, 1 approved with reservations, 2 not approved]. Wellcome Open Res 2019, 4:62 (https://doi.org/10.12688/wellcomeopenres.14771.2)
• Aguas R, White L, Hupert N, Shretta R, Pan-Ngum W, Celhay O, Moldokmatova A, Arifi F, Mirzazadeh A, Sharifi H, Adib K, Sahak MN, Franco C, Coutinho R; CoMo Consortium. Modelling the COVID-19 pandemic in context: an international participatory approach. BMJ Glob Health. 2020 Dec;5(12):e003126. doi: 10.1136/bmjgh-2020-003126