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M Gabriela M Gomes Profile picture
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2 Jan, 4 tweets, 1 min read
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
medrxiv.org/content/10.110…
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!
Covid policy doesn't really concern me. I just don't have the personality to influence policies and I'm lucky to be individually resilient to them.

But I wouldn't hesitate to give my life to defend my research against irrational judgements.

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More from @mgmgomes1

M Gabriela M Gomes Profile picture
2 Jan
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
M Gabriela M Gomes Profile picture
20 Dec 21
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]

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/…
Here a wider perspective:
arxiv.org/abs/2009.01354
Read 25 tweets
M Gabriela M Gomes Profile picture
18 Dec 21
1/ Many times I have been asked why communication around herd immunity threshold (HIT) was so confusing in this pandemic. I have even been asked whether experts really understand it. Here is my answer:
2/ The concept is well understood among mathematical epidemiologists. In my view what went terribly wrong was the politicised way in which the HIT was used in this pandemic.
3/ The HIT is the percentage of the population that needs to be immune (prior to vaccination immunity was a natural outcome of recovery from infection) before the epidemic peaks and subsides.
Read 14 tweets
M Gabriela M Gomes Profile picture
17 Dec 21
Last year, prominent modelling groups dismissed our Covid work (known for incorporating individual variation in susceptibility and exposure, and estimating low herd immunity thresholds) by claiming our stylised contact-reduction profile (Rc) wasn't close to government NPIs..
England and Scotland (with our latest stylised Rc):
This week, I had a few moments to spare and implemented the same model and fittings with the stringency index that tracks government response. The results are almost identical...
Read 7 tweets
M Gabriela M Gomes Profile picture
16 Dec 21
No inicio da pandemia convidaram-me para integrar um daqueles grupos que fazem modelos Covid para o governo Portugues. Eu disse que nao porque queria testar um novo conceito de modelos e queria estar a vontade para fazer ciencia pura e comunicar a vontade.
Comecei a ser contatada por jornalistas que me faziam perguntas as quais eu respondia avisando sempre que os meus modelos eram diferentes dos clássicos.
Talvez nao seja surpreendente que os modelos demorem a ser aceites na especialidade embora eu tivesse alguma esperança que neste caso fosse mais rápido. Mas enfim, abordagens novas demoram a ser processadas.
Read 5 tweets
M Gabriela M Gomes Profile picture
28 Nov 21
Much is being said about Covid variants spreading sequentially faster. A couple of days ago I posted a comment on this in response to @jburnmurdoch
As we approach endemicity new variants are expected to outcompete others faster. Reason being recovered subpopulation (less immune to novel variants as long as there is some immune escape) grows as we approach endemicity increasing the benefit of novelty.
Illustration from model:
When replacement is by immune escape the second variant (B) outcompetes previous faster than first variant (A) had done:
Read 9 tweets

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