Imperial College London

Dr Alexandra Hogan

Faculty of MedicineSchool of Public Health

Research Associate







Norfolk PlaceSt Mary's Campus





I work on mathematical models to guide and evaluate malaria control and elimination programmes in malaria endemic areas, in particular in the African setting. We aim to identify areas in which current tools and scale-up will successfully result in malaria elimination, and determine the most efficient and effective ways to reduce transmission. This project is funded by the Bill and Melinda Gates Foundation. 

Before joining the Department of Infectious Disease Epidemiology, I completed my PhD program at the Research School of Population Health at the Australian National University, under the supervision of Associate Professor Kathryn Glass. My PhD research involved developing and analysing mathematical models for respiratory syncytial virus (RSV) in young children, to better understand RSV transmission dynamics and seasonality, and inform rollout strategies for a maternal RSV vaccine that is currently undergoing phase three trials.

A full list of publications can be found here:



Smith RJ, Hogan AB, Mercer GN, et al., 2017, Unexpected Infection Spikes in a Model of Respiratory Syncytial Virus Vaccination, Vaccines, Vol:5, ISSN:2076-393X, Pages:12-12

Hogan AB, Anderssen RS, Davis S, et al., 2016, Time series analysis of RSV and bronchiolitis seasonality in temperate and tropical Western Australia, Epidemics, Vol:16, ISSN:1755-4365, Pages:49-55

Hogan AB, Glass K, Moore HC, et al., 2016, Exploring the dynamics of respiratory syncytial virus (RSV) transmission in children, Theoretical Population Biology, Vol:110, ISSN:0040-5809, Pages:78-85

Moore HC, Jacoby P, Hogan AB, et al., 2014, Modelling the Seasonal Epidemics of Respiratory Syncytial Virus in Young Children, Plos One, Vol:9, ISSN:1932-6203, Pages:e100422-e100422


Hogan AB, Mercer GN, Glass K, et al., Modelling the seasonality of respiratory syncytial virus in young children, 20th International Congress on Modelling and Simulation (MODSIM), ISSN:1932-6203

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