Summary
Carolin is a computational biologist at the Department of Infectious Disease Epidemiology at Imperial College London. Her main interests are stochastic simulation models of complex systems and clinical trial simulations. Currently, she is working with Roy Anderson on modelling control and elimination strategies for neglected tropical diseases. Previously she has worked on the evolution of antimicrobial resistance and evaluating strategies to reduce the risk of resistance development to novel antimicrobials. She has worked on diverse topics including projects on clinical trial simulators for novel immunotherapies for influenza and Alzheimer’s disease, environmental risk mapping of infectious diseases, modelling to improve resourcing of obstetric care facilities, screening strategies for latent tuberculosis infection in people living with HIV, identifying biomarkers for type II diabetes, vaccine development for pneumococcal diseases, molecular diagnostics for antimicrobial resistance and the eco-evoluationary dynamics of anthelminthic resistance in humans and animals.
Carolin holds a PhD in Biological Anthropology from the University of Cambridge. Her research interests include quantitative modelling of cultural evolution and the cognitive and social processes involved in innovation.
Publications
Journals
Baggaley R, Gon G, Ali S, et al., 2024, Do current maternal health staffing and bed occupancy benchmarks work in practice? Results from a simulation model, Bmj Public Health, Vol:2, ISSN:2753-4294
Anderson RM, Cano J, Hollinsworth TD, et al., 2023, Responding to the cuts in UK AID to neglected tropical diseases control programmes in Africa, Transactions of the Royal Society of Tropical Medicine and Hygiene, Vol:117, ISSN:0035-9203, Pages:237-239
Borlase A, Le Rutte EA, Castano S, et al., 2022, Evaluating and mitigating the potential indirect effect of COVID-19 on control programmes for seven neglected tropical diseases: a modelling study, Lancet Global Health, Vol:10, ISSN:2214-109X, Pages:E1600-E1611
Vegvari C, Abbott S, Ball F, et al., 2022, Commentary on the use of the reproduction number <i>R</i> during the COVID-19 pandemic, Statistical Methods in Medical Research, Vol:31, ISSN:0962-2802, Pages:1675-1685
Anderson RM, Vegvari C, Hollingsworth TD, et al., 2021, The SARS-CoV-2 pandemic: remaining uncertainties in our understanding of the epidemiology and transmission dynamics of the virus, and challenges to be overcome, Interface Focus, Vol:11, ISSN:2042-8898