1. The evidence favouring the heterogeneity model over the homogeneity model is overwhelming, whether we base this on fit penalized by complexity (DIC or similar criteria), forward predictive performance, or the Bayes factor.
2. We show that decreasing mean IFR to 0.3%, consistent with recent estimate for England, does not change fit of the model or inference that slowing and reversal of epidemic was largely attributable to build-up of herd immunity but gives a more plausible value of 15% for the HIT.
3. If you believe that the IFR during this period was 1.1%, and you don't believe the estimate of 4% for the HIT that is obtained by specifying this IFR value, you have to diagnose something else that is wrong with the model;
you can't simply revert to the homogeneity model when it has been decisively rejected.

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More from @mgmgomes1

May 16
#epitwitter Just heard from JTB that our #COVID19 paper was the most downloaded in last 90 days. I plan to spend summer developing projects that build on same concepts and methods. Happy to accommodate collaborations with those interested. Just send note.
Broader scope viewpoints article outlining effects and inferences of unobserved variation in disease dynamics just updated on arXiv: arxiv.org/abs/2009.01354
Selection on individual variation in susceptibility or exposure to causative factors of disease (contagious or not) introduces biases when not considered in analysis.

Common errors include the following:
Read 7 tweets
Feb 18
1/14 Dear #epitwitter,
Our first peer reviewed #COVID modelling paper has just been made available online by the grand classic Journal of Theoretical Biology 🙏authors.elsevier.com/sd/article/S00…
Others will follow shortly I believe.
2/14 Joined this platform in June 2019 to say some forms of individual variation impact epidemic dynamics hugely. Not all variation matters and explanation isn’t "nonlinearities this and that". Key process can be described linearly, intuitively, and quantifiably by inference.
3/14 When covid-19 emerged I couldn’t but apply concepts and new inference procedures to the pandemic. Also couldn’t but communicate results through this and other platforms.
This post summarises concepts, results, how they were received, and how mission has been accomplished.
Read 16 tweets
Feb 15
Almost 2 years since our low-HIT (herd immunity threshold) preprint was released the paper has been peer reviewed and accepted for publication in a scientific journal (specific details soon).

The accepted version features #COVID19 in England/Scotland: medrxiv.org/content/10.110…
Following the initial preprint submitted to medRxiv in April 2020 the theory that individual variation in susceptibility and exposure (frailty variation) to infection lowers HIT and epidemic final size was featured in many news and science outlets, eg:
Read 8 tweets
Jan 2
Case fatality rate em PT e UK desde que PT atingiu 85% da população vacinada com 2 doses (UK tinha 65%):
Percentagem com 2 doses em PT e UK:
Percentagem com dose de reforço em PT e UK:
Read 5 tweets
Jan 2
New Year's resolution for me: No more thinking about COVID-19. Research objectives fulfilled, time for new topics.

Still polishing final writings but worthwhile thinking has been completed

Early conclusions (first released 27 April 2020) remain unchanged
Two years ago had privilege to be: offered/accepting Global Talent Research Professorship at Strathclyde University @StrathMathStat; awarded Habilitation from Porto University. Both in recognition for research/teaching variation/selection in epidemiology/ecology/evolution.
Then pandemic emerged and took all time/attention. A lot has been sacrificed but I believe for good causes: research-wise immediately; policy-wise may take longer.

Happy to finally move on!
Read 4 tweets
Dec 20, 2021
Dreaming about death; got up to make coffee

Then decided to write thread about what's killing me. Not depressing (on contrary). It's my duty to make the world understand this +ve thing before I die [not that I think I'll die soon; that was just in dream]

For more than 10 years I've been researching with collaborators (including @mlipsitch @GrahamMedley) why epidemic models tend to exaggerate epidemic sizes and overestimate intervention impacts (particularly vaccines but also NPIs):
Here a wider perspective:
Read 25 tweets

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