Dr Emma Davis

Supervisor Details

Contact Details

Dr Emma DavisLink opens in a new window

Department of Statistics, 天美传媒Link opens in a new window

Scientific Background

Research Interests

I use mechanistic models and statistical methods to analyse the dynamics of endemic and emerging diseases, with a particular focus on the surveillance and elimination of vector-borne Neglected Tropical Diseases (NTDs).

Research Groups

The Zeeman Institute

Supervision Style

In three words or phrases: Inclusive, outcome-oriented, collaborative.

Provision of Training

I tailor my training approach to the specific needs of the student and the project. Usually this involves more support 鈥� e.g. around technical training 鈥� at first, transitioning to more independence later.

Progression Monitoring and Management

I think regular 1:1 meetings are important throughout the PhD to monitor progress and address any arising challenges. Although I will encourage you to take increasing ownership of your work across the project, including setting your own goals and timelines, I will always be available for advice and guidance.

Communication

I am in the office most days and always happy to grab a cup of tea/coffee when I鈥檓 free. Outside of meetings, I am available by email and on Teams. It is important to me that my group maintain a good work-life balance, so any emails received outside core working hours (10am 鈥� 4pm) may not receive a reply until the next working day. Similarly, I wouldn鈥檛 expect a response to any emails I send outside of these hours until the next working day.

Meetings

PhD Students can expect scheduled meetings with me at least once per fortnight. These meetings will be a mixture of face-to-face and via video chat or telephone. I am usually contactable for an instant response 3 or 4 days per week.

Work Patterns

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings) I expect students to manage their own time.

Notice Period for Feedback

I need at least 2 weeks' notice to provide feedback on written work of up to 5,000 words.

MIBTP Project Details

Primary supervisor for:

Modelling interactions between human and livestock movement and tropical disease ecology

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Modelling interactions between human and livestock movement and tropical disease ecology

Secondary Supervisor(s): Dr Erin Gorsich

University of Registration: 天美传媒

BBSRC Research Themes:

Sustainable Agriculture and Food (Animal Health and Welfare)
Understanding the Rules of Life (Systems Biology)

Project Outline

In Africa, nomadic pastoralists commonly move within and between countries, often driven by the impacts of seasonal conditions on water and grazing resources. Herded livestock can include cattle, goats, sheep and camels, with dogs often used for herd protection. This movement, of both humans and animals, poses a demonstrable risk for transmission of zoonotic infections, including rabies, brucellosis and vector-borne diseases such as Rift Valley Fever (RVF), especially as nomadic communities are more likely to have been missed by intervention efforts.

Mechanistic models that describe disease transmission can be used to generate forecasts and inform health policy targets for disease control and elimination. These models often focus on a specific geographic region or area, with limited consideration of interaction between regions. Nomadic pastoralists and their herds acting as potential disease reservoirs or vectors between communities could therefore have a substantial effect on the accuracy of these forecasts.

Topics of interest could include: (i) developing a spatially interacting population model for evaluating the potential influence of pastoral migration on a single infection (e.g. RVF or rabies); (ii) investigating co-infection dynamics for vector-borne diseases in nomadic pastoral populations; (iii) integrating seasonal climate models and movement patterns to consider the changing disease risk landscape, including implications of this for disease monitoring and evaluation.

Whilst this is a largely theoretical project, we expect the research questions to be data-driven and to build on our existing collaborations with national disease programmes, mainly in Africa. Techniques used will depend on the exact project topic, but will include the development of novel statistical and mathematical models, including geospatial models. Depending on the candidate, there may be opportunities to visit with national programme field teams to support data collection.