Author Archives: docbrs

SARS-Cov-2 modelling situation report

As we start September, the UK situation regarding Covid-19 cases and deaths has changed somewhat.

Since the UK Government re-assessed the way deaths data is collected and reported, the reported daily deaths resulting from Covid-19 infections have (thankfully) reduced to a very low level.

Cases, however, have started to rise again, although for a number of reasons the impact on deaths has been less than before. I have integrated the real world reported data with my model data to assess what is happening.

Model update following UK revision of Covid-19 deaths reporting

On August 12th, the UK Government revised their counting methodology and reporting of deaths from Covid-19, bringing Public Health England’s reporting into line with that from the other home countries, Wales, Northern Ireland and Scotland. I have re-calibrated and re-forecast my model to adapt to this new basis.

Model updates for UK lockdown easing points

As I reported in my previous post on 31st July, the model I use, originally authored by Prof. Alex de Visscher at Concordia University on Montreal, and described here, was to be updated to handle several phases of lockdown easing, and I’m glad to say that is now done. Alex has been kind enough already to adopt a method I had been considering, of introducing an array of dates and intervention effectiveness parameters, and I have been able to add the recent UK Government relaxation dates, and the estimated effectiveness of each into a new model code. I have run two sets of easing parameters as a sensitivity test.

The effect of lockdown easing in the UK

As reported in my previous post, there has been a gradual reduction in the rate of decline of cases and deaths in the UK relative to my model forecasts. This decline had already been noted, as I reported before, by The Office for National Statistics and their research partners, the University of Oxford, and reported on the ONS website.
I had adjusted the original lockdown effectiveness in my model (from 23rd March) to reflect this emerging change, but as the model had been predicting correct behaviour up until mid to late May, I will present here the original model forecasts compared to the current reported deaths trend.

Introduce Yourself (Example Post)

This is an example post, originally published as part of Blogging University. Enroll in one of our ten programs, and start your blog right. You’re going to publish a post today. Don’t worry about how your blog looks. Don’t worry if you haven’t given it a name yet, or you’re feeling overwhelmed. Just click theContinue reading “Introduce Yourself (Example Post)”

Introduce Yourself (Example Post)

This is an example post, originally published as part of Blogging University. Enroll in one of our ten programs, and start your blog right. You’re going to publish a post today. Don’t worry about how your blog looks. Don’t worry if you haven’t given it a name yet, or you’re feeling overwhelmed. Just click theContinue reading “Introduce Yourself (Example Post)”

Introduce Yourself (Example Post)

This is an example post, originally published as part of Blogging University. Enroll in one of our ten programs, and start your blog right. You’re going to publish a post today. Don’t worry about how your blog looks. Don’t worry if you haven’t given it a name yet, or you’re feeling overwhelmed. Just click theContinue reading “Introduce Yourself (Example Post)”

Mechanistic and curve-fitting UK modelling comparison

I thought it would be useful, at least for my understanding, to apply a curve-fitting approach to some of the UK reported data, and also to my model forecast based on that data.

Phenomenology & Coronavirus – modelling and curve-fitting

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.