My research involves using mathematical models in order to better estimate the burden of malaria and to understand the likely impact of interventions upon this burden, including those that reduce transmission towards elimination.
A strong focus of my work involves collaborating with the World Health Organisation and the Malaria in Pregnancy consortium to better capture the contribution of the negative effects of malaria upon pregnant women and their babies to the wider global burden of malaria.
I also would like to better understand how data collected routinely by malaria programmes can be optimally integrated within decision-making. A current focus in this area involves antenatal care (ANC)-based surveillance: harnessing data collected from women attending ANC to more accurately capture malaria endemicity, burden and intervention uptake.
Recently, working within the Imperial Covid-19 response team, I have helped to lead work trying to better understand the global impact of the pandemic.
Before focusing my research on malaria, I completed my PhD, “Quantifying the effects of measures to control highly pathogenic avian influenza H5N1 in poultry in Southeast Asia”, under the supervision of Professor Azra Ghani and Dr Simon Cauchemez.
et al., 2023, Using mortuary and burial data to place COVID-19 in Lusaka, Zambia within a global context, Nature Communications, Vol:14, ISSN:2041-1723, Pages:1-15
et al., 2023, Alternative epidemic indicators for COVID-19 in three settings with incomplete death registration systems, Science Advances, Vol:23, ISSN:2375-2548, Pages:1-10
et al., 2023, Using antenatal care as a platform for malaria surveillance data collection: study protocol, Malaria Journal, Vol:22, ISSN:1475-2875, Pages:1-10
et al., 2023, Seasonal dynamics of Anopheles stephensi and its implications for mosquito detection and emergent malaria control in the Horn of Africa, Proceedings of the National Academy of Sciences of Usa, Vol:120, ISSN:0027-8424, Pages:1-9
et al., 2022, Considering equity in priority setting using transmission models: Recommendations and data needs, Epidemics: the Journal of Infectious Disease Dynamics, Vol:41, ISSN:1755-4365, Pages:1-8