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).
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.
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.
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.