Professor Ben Lambert

Associate Professor of Statistics

Biographical Sketch

I am a mathematician and a statistician with a strong interest in biological systems, and I develop computational methods that help to uncover biological and epidemiological knowledge.

I direct Oxford's Schmidt AI in Science programme — a postdoctoral Fellowship programme where Fellows apply methods from AI to advance scientific knowledge.

I am a passionate communicator of statistical ideas (for example, through my YouTube videos), and I develop open-source materials which allow other researchers to understand cutting-edge methods.

Research Interests

Bayesian statistics, mathematical biology, epidemiology, Bayesian nonparametrics, research software

Publications

Bajaj, S., Thompson, R. and Lambert, B. (2024) “A renewal-equation approach to estimating Rt and infectious disease case counts in the presence of reporting delays”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences [Preprint].

Kamau, E. et al. (2024) “Enterovirus A71 and coxsackievirus A6 circulation in England, UK, 2006–2017: A mathematical modelling study using cross-sectional seroprevalence data”, PLoS Pathogens, 20(11).

Bajaj, S. et al. (2024) “COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys.”, The Lancet. Digital health, 6(11), pp. e778 - e790.

Gallagher, K. et al. (2024) “Ten simple rules for training scientists to make better software.”, PLoS computational biology, 20(9), p. e1012410.

Bajaj, S. et al. (2024) “Understanding COVID-19 testing behaviour in England through a sociodemographic lens : a population based study”, The Lancet Digital Health [Preprint].

Johnson, R. et al. (2023) “Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics.”, bioRxiv [Preprint].

Parag, K., Cowling, B. and Lambert, B. (2023) “Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying.”, Proceedings. Biological sciences, 290(2007), p. 20231664.

Creswell, R. et al. (2023) “Understanding the impact of numerical solvers on inference for differential equation models”, arXiv.

Tsui, J.-H. et al. (2023) “Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1.”, Science (New York, N.Y.), 381(6655), pp. 336–343.

Pons-Salort, M. et al. (2023) “Changes in transmission of Enterovirus D68 (EV-D68) in England inferred from seroprevalence data.”, eLife, 12, p. e76609.