Sean Cavany is a mathematical modeller based in Ben Cooper’s DRIaDD (Drug-resistant infections and disease dynamics) group in the NDM Centre for Global Health Research.
His current focus is on developing models to help understand the impact of substandard and falsified medicines on the emergence and spread of antimicrobial resistance. This will involve using data-driven within-host models of the emergence and selection of resistance following treatment with substandard and falsified medicines. Results from these within-host models will then inform epidemiological models to quantify the potential population-level impacts. Sean works closely on this project with members of the Medicine Quality group.
He has broad interests within the field of infectious disease dynamics. He completed his PhD in Infectious Disease Epidemiology at the London School of Hygiene and Tropical Medicine, where he analyzed the effectiveness of contact tracing for tuberculosis. Prior to coming to Oxford he was a postdoc at the University of Notre Dame, where he worked on modelling the transmission of SARS-CoV-2 and of dengue virus. He is also interested in the potential of using wastewater for infectious disease surveillance. He has an undergraduate degree in mathematics and is also a qualified secondary school maths teacher.
Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.
Jung S-M. et al, (2023), medRxiv
The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance.
Cavany S. et al, (2023), Nature communications, 14
Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
Cavany S. et al, (2023), BMJ Global Health, 8, e012169 - e012169
Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub.
Howerton E. et al, (2023), medRxiv
Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
Cavany SM. et al, (2023), PLOS Computational Biology, 19, e1010424 - e1010424