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.
This is a brief update to my model predictions in the light of a week’s published data regarding Covid-19 cases and deaths in all settings – hospitals, care homes and the community.
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.
Even having explored Prof. Alex Visscher’s published MatLab code for a week or two now, with UK data, even I am surprised at how well the model is matching up to published UK data so far (on April 18th 2020). I have reported my previous work a couple of times, once relating to the modellingContinue reading “UK Coronavirus Modelling – match to data”
This is an extract from the Government daily Coronavirus briefing on 2nd April 2020, led by Matt Hancock, with Professor Stephen Powis, Medical Director of the NHS in England. In this clip, Prof. Powis states that he thinks there is “early academic evidence” that the R0 “Reproduction Number”, (what he calls the transmission rate) reflectingContinue reading “The Coronavirus briefing 2nd April 2020”
This article by the BBC’s Rachel Schraer explores the modelling for the progression of the Coronavirus Covid-19. In the article we see some graphs showing epidemic growth rates, and in particular this one showing infection rate dependency on how many one individual infects in a given period. https://www.bbc.co.uk/news/health-52056111 This chart led me to look intoContinue reading “Coronavirus modelling work reported by the BBC”
I guess the UK line in the Johns Hopkins chart, reported earlier, might well flatten at some point soon, as some other countries’ lines have. But if we continue at 3 days for doubling of cases, according to my spreadsheet experiment, we will see over 1m cases after 40 days. See:https://docs.google.com/spreadsheets/d/1kE_pNRlVaFBeY5DxknPgeK5wmXNeBuyslizpvJmoQDY/edit?usp=sharingand the example outputs attachedContinue reading “Coronavirus – possible trajectories”
A few people might have see the Johns Hopkins University Medical School chart on Covid-19 infection rates in different countries. This particular chart (they have produced many different outputs, some of them interactive world incidence models – see https://coronavirus.jhu.edu/map.html for more) usefully compares some various national growth rates with straight lines representing different periods overContinue reading “Coronavirus – forecasting numbers”
Here’s the kind of stuff that the Covid-19 modellers will be doing. https://www.nature.com/articles/srep46076.pdf I have downloaded GleamViz, http://www.gleamviz.org/simulator/client/, and it is quite complicated to set up (I used to have a little Windows app called Wildfire that just needed a few numbers to get a pictorial progression of life/recovery/death from the disease, depending on infectivity,Continue reading “Coronavirus modelling – GLEAMviz15”
There has been increasing concern recently about SARS-Cov-2 variants that might escape vaccines to some extent, as well as having different transmission rates (as the Kent variant does), and causing different severity of illness with higher mortality. I have added a vaccination efficacy modifier, var_eff, by variant, as a multiplier to the standard vaccination efficacy, vac_eff, to help model such potential variants that have a partial or total capability to escape vaccines, and this post shows examples of how that works, using a third variant introduced to the model on January 1st 2021. In addition, I have completed adding fSS (the fraction of people becoming seriously sick from each variant) and fmort (fatality of the variant) by Covid variant.