Many countries, including the UK, are experiencing a resurgence of Covid-19 cases recently, although, thankfully, with a much lower death rate. I have run several iterations of my model in the meantime, introducing several lockdown adjustment points, since my last blog post, as the situation has developed. The key feature is the sharp rise cases, and to a lesser extent, deaths, around the time of the lockdown easing in the summer. I have applied a 10% increase in current intervention effectiveness on October 19th (although there are some differences in the half-term dates across the UK), followed by a partial relaxation after 2 weeks, -5%, reducing the circuit-breaker measure by half – so not back to the level we are at currently. The effect of that change is shown in the final chart in the blog post.
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.
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.
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.
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.
I have now reforecast my model with a slightly lower intervention effectiveness (83% instead of 83.5%), and, while still slightly below reported numbers, it is nearly on track
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.