Publications by Statistical Genetics and Epidemiology

Our research spans areas of statistical genetics, in particular the development of powerful statistical approaches to analyse genetic data, as well as studying infectious diseases.

Cuomo-Dannenburg, G. et al. (2023) “Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review”, medRxiv.

McCabe, R. et al. (2023) “Alternative epidemic indicators for COVID-19: a model-based assessment of COVID-19 mortality ascertainment in three settings with incomplete death registration systems”, medRxiv.

Watson, L. et al. (2023) “Improving estimates of epidemiological quantities by combining reported cases with wastewater data: a statistical framework with applications to COVID-19 in Aotearoa New Zealand”, medRxiv.

Atchison, C. et al. (2023) “LONG-TERM PHYSICAL AND MENTAL HEALTH IMPACT OF COVID-19 ON ADULTS IN ENGLAND: FOLLOW UP OF A LARGE RANDOM COMMUNITY SAMPLE”, medRxiv.

Mitra, R. et al. (2023) “Learning from data with structured missingness”, Nature Machine Intelligence, 5(1), pp. 13–23.

Penn, M. et al. (2023) “The uncertainty of infectious disease outbreaks is underestimated”, Research Square.

Kont, M. et al. (2023) “Characterising the intensity of insecticide resistance: A novel framework for analysis of intensity bioassay data”, Current Research in Parasitology and Vector-Borne Diseases, 4.

Corbel, V. et al. (2023) “A new WHO bottle bioassay method to assess the susceptibility of mosquito vectors to public health insecticides: results from a WHO-coordinated multi-centre study.”, Parasites & vectors, 16(1), p. 21.

Deligiannidis, G. et al. (2023) “A Unified Framework for U-Net Design and Analysis”, in. Curran Associates, pp. 27745–27782.

Venkatesh, S. et al. (2023) “The genetic architecture of changes in adiposity during adulthood”, medRxiv.