There is a range of modelling methods, successively requiring more detailed data, from phenomenological (statistical and curve-fitting) methods, to those which seek increasingly to represent the mechanisms (hence “mechanistic” modelling) by which the virus might spread.
We see the difference between curve-fitting and the successively more complex models that build a model from assumed underlying interactions, and causations of infection spread, between parts of the population.
A couple of interesting articles on the Coronavirus pandemic came to my attention this week; a recent on in National Geographic on June 26, highlighting a starting comparison of the USA’s cases history and recent spike in case numbers with European data.
This article referred to an older National Geographic piece, from March, by Cathleen O’Grady, referencing a specific chart from Katy Armstrong of the Imperial College Covid-19 Response team.
I noticed, and was interested in that reference following a recent interaction I had with that team, regarding their influential March 16th paper.
Meanwhile, my own forecasting model is still tracking published data quite well, although over the last couple of weeks I don’t think the published rate of deaths is falling as quickly as before.
My model is currently fitting deaths data for the UK, on the originally modelled basis of Government published “all settings” deaths. I plan to compare results by looking at the Gompertz function and Sigmoid charts that Michael Levitt uses.
Owing to the serendipity of a contemporary and friend of mine at King’s College London, Andrew Ennis, wishing one of HIS contemporaries in Physics, Michael Levitt, a happy birthday on 9th May, and mentioning me and my Coronavirus modelling attempts in passing, I am benefiting from another perspective on Coronavirus from Michael Levitt.
The UK Government yesterday changed the reporting basis for Cononavirus numbers, retrospectively (since 6th March 2020) adding in deaths in the Care Home and and other settings, and also modifying the “Active Cases” to match, and so I have adjusted my model to match.
In the recent daily UK Government presentations, the R0 Reproductive Number has been mentioned a few times, and with good reason. Its value is as a commonly accepted measure of the propensity of an infectious disease outbreak to become an epidemic. It turns out to be a relatively simple number to define, although working backContinue reading “The SIR model and importance of the R0 Reproductive Number”