Dr David Hodgson
BSc MSci MRes PhD
I completed my undergraduate studies in Mathematics at University College London (UCL) in 2013. After completing a Masters in Biophysical Sciences at Durham University, I started research into Mathemtical Biology at the Center of Mathematics and Physics in Life sciences and Experimental Biology (CoMPLEX) at UCL. During my time at CoMPLEX, I worked on various infectious disease modelling projects which led me to pursue a PhD in Mathematical modelling and cost-effectivess of future RSV intervention strategies between 2015-2020. During this time I also worked at Public Health England as a Senior Mathematical Modeller, where I used mathematical models to evaluate the effectiveness of existing Influenza vaccination programmes in England and Wales.
I joined The London School of Hygeine and Tropical Medicine as a Research Fellow in 2020.
I am a tutor on two courses at LSHTM, EPM202: Statistical methods in epidemiology, and EPM302: Modelling and the dynamics of infectious diseases.
My main research uses mathematical and statistical models to better inform vaccination resource allocation against respiratory viruses. My work on RSV evaluates the impact of future vaccine candidates and determines cost-effective ways to roll-out potential vaccination programmes. My work on Influenza has previously explored ways to re-allocate the existing seasonal vaccination programme to generate a greater level of herd immunity. My current research is still focused on Influenza, but I am now looking at host-level Influenza antibody dynamics to try and answer questions such as i) how does influenza immunity influence the severity of disease? and ii) how does influenza immunity affect the effectiveness of vaccination strategies?
From a statistical and mathematical perspective, I’m interested in research into Bayesian parameter estimation though Markov chain Monte Carlo (MCMC) methods. In particular, I like exploring contemporary MCMC algorithms and evaluating which are optimised to solving the types of complex non-linear models that arise in infectious disease modelling.