The ‘lowhighcovid’ tool is intended to highlight the potential impact of different control strategies on the rate of spread of COVID-19. It is designed as an educational tool, and is not intended to be used as a COVID-19 disease management or forecasting tool.
“Our website is intended to demystify infectious disease modelling, and highlight the broad type of model behind government policies for the control of COVID-19,” said Nick Taylor, a PhD researcher in Theoretical and Computational Epidemiology in Cambridge’s Department of Plant Sciences who was involved in developing the tool.
Control measures, including social distancing and lockdown, affect the rate at which COVID-19 spreads through a population. The interactive model allows users to see the likely effects of different measures, depending on when they are started and the length of time they are in place.
There are a wide variety of approaches to modelling the spread of disease. Models used so far for COVID-19 range from detailed individual-based models, which are run many times for each set of parameters to give a range of predictions, to well-established deterministic models which divide the population into Susceptible, Infected and Resistant classes (referred to as an SIR model) resulting in a single prediction for one set of parameters. The new tool allows users to explore how a modified SIR model can be used to understand and manage infectious disease transmission.
Image Credit: Tim Mossholder on Unsplash
Reproduced courtesy of the University of Cambridge