Hosted by the University of Bristol's Population Health Science Institute, this webinar series will focus on the theme of "Interdisciplinary Perspectives on Population Health: Bridging the Gaps for a Healthier Future."

Abstract: In this presentation Ettie will share her experience of using two different infectious semi-mechanistic models (Hawkes Processes and Renewal models) to inform malaria and COVID-19  policy.  These type of models enable you can encode the shape of an infection mechanism but there is flexibility to easily infer the parameters from data with relatively inexpensive inference methods such as Maximum Likelihood and Bayesian inference.

Join Zoom Meeting: https://bristol-ac-uk.zoom.us/j/96154126995?pwd=R7ECnBVzBZ5kPCdpOeYjOYdT4mEpXB.1, Meeting ID: 961 5412 6995, Passcode: 224596

Bio: Ettie is a Lecturer in Statistical Science in the School of Mathematics at the University of Bristol. She is interested in developing and applying novel methods for infectious disease outbreak analysis to help inform policy makers in real time.  Ettie’s current research focuses on developing spatial temporal renewal based transmission models alongside estimating the number of children affected by COVID-19 and crises. Ettie is an active member of the Machine Learning and Global Health Network, which is a network of academics across the UK, Denmark, Germany and Singapore doing applied and methodological research in the field of Global Health.