1/ Reducing transmission is not the only way of preventing deaths. From health records we can predict who is at risk of severe or fatal disease (medrxiv.org/content/10.110…, PLoS Medicine in press) and offer them shielding.
2/ Shielding was less than optimal in the first wave, because (at least in Scotland) it was implemented too late. But we are better prepared now.
3/ The policy option of shielding the vulnerable for a short time while allowing the epidemic to run to completion among the young and fit should not be ruled out (medrxiv.org/content/10.110…).
4/ If the herd immunity threshold is relatively low, and we are nearly there in most of the UK, this "stratify and shield" policy becomes more advantageous.

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More from @mgmgomes1

4 Oct
1/ Heterogeneous susceptibility and exposure to infection are under selection by the force of infection. Highly susceptible and highly connected individuals tend to be infected earlier and removed from the susceptible pool earlier.
2/ Mean susceptibility and mean connectivity in residual susceptible pool decrease over time lowering cumulative attack rate (CAR). Models that do not account for complete variation in those characteristics are biased towards overpredicting CAR and herd immunity threshold (HIT).
3/ Correcting models by accounting for observed factors is insufficient. To ensure that variation (observable and unobservable) is captured completely we build a distribution into the models and infer its variance by fitting to epidemic curves.
Read 4 tweets
29 Sep
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;
Read 4 tweets
28 Sep
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.
Read 5 tweets
19 Sep
@PieterTrapman @mlipsitch 1/ Unclear what you mean by these numbers, but a week later I finally had a couple of hours on a Saturday to have a look. Running the models in our July preprint I am now comparing the 70% lower-risk group with the whole population. Here is what I find (explanations follow).
@PieterTrapman @mlipsitch 2/ I run each of the 3 models until the pandemic is over (one year is sufficient for these models). Then I calculate the proportion of the 70% lower-risk group that has been infected and divide by the portion of the entire population that has been infected.
@PieterTrapman @mlipsitch 3/ This metric varies wildly across models even though all assumed a gamma distribution with mean=1 and coefficient of variation estimated by fitting to first wave of confirmed cases in specific countries. I calculate HIT for each case and find this to be conserved across models.
Read 9 tweets
13 Sep
Whether SARS-CoV-2 has a natural herd immunity threshold (nHIT) closer to 70% or 20% is a hugely important question whose answer impacts the life of every person on Earth.
I find it therefore normal that a random person forms and expresses opinions about nHIT and that some may even treat the subject just like they treat politics, religion or sports. But scientists are not random in this instance. The expectation on them is different!
In March I assembled an informal team to apply to COVID-19 a knowledge base that I and others developed over the last 10 years. We had studied how individual variation impacts population dynamics: epidemics but also ecology and evolution (nonheritable variation in this case).
Read 9 tweets
7 Sep
@joaocunhamarque @al_antdp @YouTube Tinha outros planos para este fim de dia mas troquei por assistir à sessão. Três horas bem empregues que me permitiram mais uma vez contrastar expectativas correntes com as de quem segue modelos que tratam das heterogeneidades na susceptibilidade e exposição ao virus.
@joaocunhamarque @al_antdp @YouTube Quando essas heterogeneidades são tratadas por completo os modelos de transmissão passam a prever o fim da pandemia na Europa antes do início da época dos virus respiratórios sazonais.
@joaocunhamarque @al_antdp @YouTube Fim da pandemia não significa fim do novo coronavirus, mas sim entrada num período pós-pandémico em que SARS-CoV-2 integrará o conjunto de virus causadores de infecções respiratórias sazonais.
Read 4 tweets

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