I am an Imperial College Research Fellow (ICRF) based in the MRC centre for Global Infectious Disease Analysis in the School of Public Health at Imperial College, London. I am interested in applying and developing novel methods for outbreak analysis to help inform policy makers in real time.
I am currently part of the Imperial College COVID-19 response team looking at real time modelling of Rt across Europe and the USA.
My fellowship focuses on using Hawkes Processes to model infectious disease in a new way. These semi-mechanistic models enable us to separate out the contribution to the burden of disease from both exogenous and endogenous events, which I believe will help give new insights to policy makers. I am currently applying these semi-mechanistic models to pre-elimination malaria data but believe they would be suitable to model a wide variety of different infectious disease outbreaks.
I am a numerical modeller by background and was previously funded through a Bill & Melinda Gates Foundation grant to use mathematical models of malaria endemic areas to answer policy related questions. I mainly focused on two projects: modelling the impact of pyrethroid resistance on the mass community effect of insecticide treated nets and identifying gaps in current malaria interventions where the RTS,S malaria vaccine could be targeted.
et al., 2021, Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study, The Lancet, ISSN:0140-6736
et al., 2021, Using Hawkes Processes to model imported and local malaria cases in near-elimination settings, PLOS Computational Biology, Vol:17, ISSN:1553-734X
et al., 2020, State-level tracking of COVID-19 in the United States, Nature Communications, Vol:11, ISSN:2041-1723, Pages:1-9
et al., 2020, Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, Vol:584, ISSN:0028-0836, Pages:257-261
et al., 2020, Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach, PLOS Computational Biology, Vol:16, ISSN:1553-734X, Pages:1-20