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Mathematical models to understand the epidemiological consequences of heterogeneous biting among disease vectors

Title of PhD project

Mathematical models to understand the epidemiological consequences of heterogeneous biting among disease vectors

Supervisory team

LSHTM

Lead: Laith Yakob (Faculty of Infectious and Tropical Diseases, Laith.yakob@lshtm.ac.uk

Nagasaki University

Brief description of project

Some hosts are more attractive to disease vectors and thus are typically bitten more often than others. This can have dramatic influence on the transmission of vector borne diseases [1]. Vectors can also differ in their biting behaviour in terms of the rate at which they bite hosts [2] and their preference for alternative host species [3]. The combined impact of these numerous routes of heterogeneity is unknown. Mathematical models that realistically capture the nuanced biting behaviour of vectors will be constructed to assess their influence on disease outbreak risk. General principles will then be complemented with case studies of specific vector species, such as Aedes albopictus – an arbovirus vector of concern to both Japan [4] and the UK [5]. These models will help inform risk of spread of vector borne diseases into regions that are not currently endemic.

[1] Guelbéogo et al. (2018) Variation in natural exposure to anopheles mosquitoes and its effects on malaria transmission. elife 7:e32625

[2] Sunahara (2018) Simulation Study of the Effects of Host Availability on Bite Rate of Aedes albopictus (Skuse) (Diptera: Culicidae) and Risk of Dengue Outbreaks in Non-Endemic Areas. Japanese Journal of Infectious Diseases 71(1):28-32

[3] Orsborne et al. (2020) Evidence of extrinsic factors dominating intrinsic blood host preferences of major African malaria vectors. Scientific Reports 10(1):741

[4] Yang et al. (2021) Searching for a sign of exotic Aedes albopictus (Culicidae) introduction in major international seaports on Kyushu Island, Japan. PLoS NTDs 15(10): e0009827

[5] Medlock et al. (2017) Detection of the invasive mosquito species Aedes albopictus in southern England. Lancet Infectious Diseases 17(2):140

The role of LSHTM and NU in this collaborative project

Laith Yakob is an epidemiological modeller based at LSHTM and  Toshihiko Sunhara is an entomologist and modeller based at NU. Both have developed models of vector borne diseases but from distinct and complementary viewpoints: work of TS is mostly based around the ecology and medical entomology of systems whilst work of LY has been more epidemiologically focussed. The PhD student will benefit from being co-supervised by researchers with such complementary backgrounds and interests.

Whether the student is based in Japan or the UK, the collaboration will involve weekly meetings with the primary supervisor and at least fortnightly meetings with the secondary supervisor. Once a month for the first 6 months, there will be meetings jointly with both supervisors, with joint meetings occurring approximately bimonthly thereafter. This frequency has proven suitable to LY’s previous PhD supervisions. However, it is recognised that all students differ in their requirements and both supervisors will bear this in mind and try their best to accommodate the student’s needs.

Strategic meetings will be held between the supervisors at least bi-annually to discuss the progress and directions of the student and to highlight any opportunities arising in either LSHTM or NU which might benefit the student or which might provide further ways by which the co-supervisors can collaborate.

Particular prior educational requirements for a student undertaking this project

An MSc degree involving mathematical/computational modelling. Demonstrable knowledge of infectious disease epidemiology.

Skills we expect a student to develop/acquire whilst pursuing this project

• Cutting-edge skills in mathematical modelling using industry leading computing languages (e.g. Python, R).
• An ability to effectively communicate results, both orally and in their scientific writing.
• Advanced ability to manage projects.
• Advanced skill in developing biologically realistic simulation models and applying them to real-life contexts.