In my most recent post on February 12th, I described modelling work I had done in support of Prof. Alex de Visscher’s paper, in conjunction with Dr. Tom Sutton, on “Second-wave Dynamics of COVID-19: Impact of Behavioral Changes, Immunity Loss, New Strains, and Vaccination” which has now been published for peer review as a pre-print on Springer’s site at https://www.researchsquare.com/article/rs-195879/v1. I have now added vaccination and multiple variants I had already added to our previous model into the new grouped population model, and this blog post reports on progress with that new model.
This paper reports some parametric Coronavirus model runs I have made that compare, in particular, how the UK vaccine programme allows some NPI relaxation compared with a case with no vaccination. The outcome is that the vaccine programme in the UK has the potential to reduce the imposition of NPIs on March 7th by about 15%, without costing lives, this being the next time we in the UK are due for a major NPI review, potentially involving the return of schools at around March 7th.
I present an analysis of the pandemic situation in the UK, with two Coronavirus variants present since December 16th, and sensitivities to different New Year 2021 Non Pharmaceutical Interventions (NPIs), but always with the background of vaccine dispensing, which started in the UK on December 8th.
Now that it seems clear that a vaccination programme in the UK might start as early as next week, I have re-run my Covid-19 vaccination model for the UK, updating the November 25th scenarios (which begin on January 1st 2021) to reflect some potential UK outcomes.
In my most recent post on November 18th, about updating my Coronavirus model to handle the impact of vaccines, I gave some examples of how case numbers, and more specifically death rates might be improved for the UK through a vaccination programme. Now that there seem to be several vaccines imminent, with efficacies ranging from 70% (Astra-Zeneca/Oxford) through 90% (A-Z/O via a different inoculation regime, and Pfizer), to 95% (Moderna) and several others in the mix, I explore some sensitivities in more detail, and also apply the model to the USA.
I look at the impact of a postulated change in the timing of further measures to control the Covid-19 epidemic in the USA, against a current background of rapidly increasing daily case numbers and deaths. I also show an updated projection for the UK, both compared with Worldometers forecasts
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.
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.
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.
I promised in an earlier blog post to present Prof. Michael Levitt’s analysis of Covid-19 data published on the EuroMoMo site for European health data over the last few years. His finding is that COVID19 is similar to flu only in total and in age range excess mortality. Flu is a different virus, has a safe vaccine & is much less a threat to heroic medical professionals.