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”
My title for this post is drawn from a slide I have shown before, from the 17th April Cambridge Conversation webinar, which I reported in my April 17th blog post, and also in my April 22nd blog post on model refinement, illustrating the cyclical behavior of the Covid-19 epidemic in the absence of pharmaceutical interventions, with control of cases and deaths achieved, only to some extent, by Non-Pharmaceutical Interventions (NPIs).
The UK Government has just announced some reversals of the current lockdown easing, and so before I model the additional interventions announced today, I want to illustrate quickly the behaviour of the model in response to changing the effectiveness of current interventions, refecting the easings that have already been made, and also to highlight the sensitivity of the forecasts of case and death rates to the influence of lockdown effectiveness.
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 then before. I have integrated the real world reported data with my model data to assess what is happening.
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.
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.