My research interests include developing mathematical and statistical models using phylogenetic and epidemiological data to understand disease transmission in the context of sustained and pandemic outbreaks. Over the past year, I completed an MRes in Biomedical Sciences while additionally conducting work in the phylodynamic COVID response team in the Department of Infectious Disease Epidemiology. We developed a phylodynamic model in an epidemiological framework to estimate useful epidemiological parameters, such as R and number infected over time, across all regions with sequence data available on GISAID.
While continuing to work in the response team, I have now started the Wellcome Trust PhD programme in Epidemiology, Evolution and Control of Infectious Diseases under the supervision of Dr. Erik Volz and Dr. Marc Baguelin. My PhD project focuses on developing novel phylodynamic modelling methods for forecasting hot-spots and transmission dynamics in RNA-virus outbreaks, including influenza, HIV and sars-cov-2.
Additionally, I am continuing previous work, under the supervision of Dr. Jeff Eaton, on estimation and modelling of real-time HIV incidence in Malawi, with a focus in adolescent girls and young women (AGYW), aiming to develop novel methods for extrapolating population incidence estimates from surveillance data collected at ante-natal care clinics. We are further developing this work to assess how increasing secondary school access to AGYW in Malawi impacts HIV and pregnancy incidence, in particular to inform the Secondary Education Expansion for Development (SEED) initiative being conducted by the CDC.
Prior to moving to Imperial College, I completed an MSc in Epidemiology at the London School of Hygiene and Tropical Medicine under the supervision of Dr. Marc Baguelin. During my time at LSHTM, I utilised epidemiological data and pathogen genome sequences collected by Public Health England during the A-H1N1 influenza pandemic outbreak in 2009 to observe how the integration of genetic data into mathematical modelling of an outbreak can provide a more reliable estimate of disease transmission, specifically within the context of pandemic outbreaks.
et al., 2021, The impact of viral mutations on recognition by SARS-CoV-2 specific T cells., Iscience, Vol:24, ISSN:2589-0042, Pages:103353-103353
et al., 2021, Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England, Nature, Vol:593, ISSN:0028-0836, Pages:266-269
et al., 2021, Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions, Nature Communications, Vol:12, ISSN:2041-1723, Pages:1-7
et al., 2021, Evaluating the effects of SARS-CoV-2 Spike mutation D614G on transmissibility and pathogenicity, Cell, Vol:184, ISSN:0092-8674, Pages:64-75.e11
et al., 2021, A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China, International Journal of Infectious Diseases, Vol:102, ISSN:1201-9712, Pages:463-471