Latest on individual variation in susceptibility or exposure to #SARSCoV2 and #COVID19 with Marco Colombo, Joe Mellor, Helen Colhoun and Paul McKeigue:
Trajectory of COVID-19 epidemic in Europe medrxiv.org/content/10.110…
We show that relaxing the assumption of homogeneity in the modelling code released by Flaxman et al (Nature) to allow for individual variation in susceptibility or connectivity gives a model that has better fit to the data and more accurate 14-day forward prediction of mortality.
Allowing for heterogeneity in 11 European countries reduces estimate of "counterfactual" deaths that would have occurred if there had been no interventions from 3.2 million to 262,000, explaining most of the slowing and reversal of COVID-19 mortality by build-up of herd immunity.
The estimate of the herd immunity threshold depends on the value specified for the infection fatality ratio (IFR): a value of 0.3% for the IFR gives 15% for the average herd immunity threshold over the 11 European study countries.
The release of the modelling code and dataset used by Flaxman et al is a valuable contribution to transparent evaluation of infectious disease modelling.
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#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. journals.elsevier.com/journal-of-the…
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.
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.
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).
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:
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.
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]
PLEASE READ IF YOU CAN!
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): journals.plos.org/plospathogens/…