Group | Mathematical Epidemiology

Contact Networks

The research group Mathematical Epidemiology focuses on the intersection between mathematics, epidemiology and ecology. We combine field and laboratory data with theoretical and computational models to improve understanding of population, infection and disease dynamics, focusing on malaria and neglected tropical diseases.

Selected Projects

Opisthorchis Modelling

By developing models of the transmission dynamics of food-borne trematodes, such as liver flukes, we elucidate the role of reservoirs hosts in maintaining transmission in rural areas of Lao PDR, and determine the impact of intervention strategies on reducing morbidity. Read more about the SNF project

NTD Modelling Consortium

As a member of the Neglected Tropical Diseases Modelling Consortium, we advise global bodies and national control programmes on the feasibility and ideal strategies for achieving human African trypanosomiasis elimination targets. More information about the consortium can be found here, as well as a study on Assessing Strategies against Gambiense Sleeping Sickness through Mathematical Modeling.

Latest Publications

Trevisin C et al. A spatially explicit model of the dynamics of Opisthorchis viverrini spread. Ecohydrology. 2025;18(2):e2754. DOI: 10.1002/eco.2754

Anyanwu U et al. A mathematical model of animal-human brucellosis transmission in Armenia: implications for prevention and control. CABI One Health. 2024;3(1). DOI: 10.1079/cabionehealth.2024.0020

Fairbanks E.L et al. Inference for entomological semi-field experiments: fitting a mathematical model assessing personal and community protection of vector-control interventions. Comput Biol Med. 2024;168:107716. DOI: 10.1016/j.compbiomed.2023.107716

Fairbanks E.L et al. Evaluating human landing catches as a measure of mosquito biting and the importance of considering additional modes of action. Sci Rep. 2024;14:11476. DOI: 10.1038/s41598-024-61116-0

Golumbeanu M et al. AnophelesModel: an R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact. PLoS Comput Biol. 2024;20(9):e1011609. DOI: 10.1371/journal.pcbi.1011609