Michael Levitt – A rambling mind

Pandemic modelling review

Charts 21/22/23/24 with all 4 age groups for scenario UK023 with vaccinations. 17th May 1.8% ,19th July 1% and 1st Sep 2% mean NPI relaxations. v3 transmission k11 = 1, var_eff = 70/85%, fss x1 and fmort x1. Model 800 day outcomes to April 12th 2022 to November 6th. Active, all infected and serious modelled cases

Vaccination has somewhat stabilised the SARS-Cov-2 pandemic in the UK. I summarise the capabilities that I have found necessary and useful in modelling the behaviour of the pandemic; successive variants, different population age-groups, the effect of Government NPIs, and vaccinations.

Mechanistic and curve-fitting UK modelling comparison

Cumulative, daily and 7-day average model UK deaths, 83%, 6th March - 30th Sep, with the daily ratio of cumulative deaths, and the H(t) function, the natural logarithm of that ratio, and the negative natural log of H(t)

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.

Phenomenology & Coronavirus – modelling and curve-fitting

This chart was put up by Michael Levitt on July 8th to illustrate curve fitting methodology using the Gompertz function. See https://twitter.com/MLevitt_NP2013/status/1280926862299082754

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.

Coronavirus model tracking, lockdown and lessons

Model chart June-11th, showing UK deaths data daily and cumulatively, both modeeled and reported, to September 30th 2020

A brief update post to confirm that my Coronavirus model is still tracking the daily reported UK data well, and doesn’t currently need any parameter changes. I go on to highlight some important aspects of emphasis in the Daily Downing St. Update on June 10th, as well as the response to Prof. Neil Ferguson’s comments to the Parliamentary Select Committee for Science and Technology about the impact of an earlier lockdown date, a scenario I have modelled and discussed before.

Current Coronavirus model forecast, and next steps

Reported Deaths vs. Modelled deaths, both cumulative and daily,

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.

Michael Levitt’s analysis of European Covid-19 data

Deaths graphs from EurMoMo allow the calculation of Excess deaths

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.

Cambridge Conversation 14th May 2020, and Michael Levitt’s analysis of Euro data

UnHerd interview with Prof. Michael Levitt

I covered the May 14th Cambridge Conversation in my blog post last week, and promised to make available the YouTube link for it when uploaded. It is now on the University of Cambridge channel.

Coronavirus – possible trajectories

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”