Presented by Prof Dirk Pfeiffer (City University Hong Kong and Royal Veterinary College, UK), Dr Guillaume Fournié (Royal Veterinary College, UK) and Dr Petra Muellner (EPI-interactive NZ)
The Avian Influenza (AI) model implemented in the freely available app Epidemix is a stochastic compartmental model that simulates the spread of avian influenza viruses within a batch of birds. In this webinar we will illustrate how Epidemix can be used to investigate the impact of mortality thresholds for disease detection on disease dynamics. In addition, we will showcase new functionality that was recently added to the app; including the ability to upload custom networks and locations.
What is Epidemix?
Epidemix is a free web application that allows users from different backgrounds to improve their understanding of mathematical disease modelling. You can use Epidemix to explore key concepts of disease dynamics and control, and to explore how different types of models can be used to examine the spread of diseases in different populations. Epidemix was developed in recognition of the importance of effectively linking modelling with policy decision making. Our goal is to provide a teaching resource and to support improved communication between epidemiologists and decision makers by making models accessible without the need to learn complex mathematics or a specialist programming language. The goal of Epidemix is not to provide predictive outputs, but to help create an improved understanding of different spread scenarios and the assumptions and interactions behind them. The app is freely accessible via www.epidemix.app.
Participation in the event is free.
16 March 2022 | Auckland: 9 pm | Hong Kong: 4 pm | London: 8 am | New York 3 am | Los Angeles 12 pm
Please register in advance via this link:
https://cityu.zoom.us/webinar/register/WN_ovdRuQAATtaqQzla8euDHg
Epidemix is described in:
Muellner U, Fournié G, Muellner P, Ahlstrom C, Pfeiffer D. Epidemix - an Interactive Multi-Model Application for Teaching and Visualizing Infectious Disease Transmission. Epidemics, doi: 10.1016/j.epidem.2017.12.003, 2017.
