TWIMC: I've been Mathematician and Mother for ~30 years (now also Grandmother) and this is what matters. I spent the last 10 years studying individual variation on characteristics that are under selection but that have no heritability repercussions in the time scale under study.
In these studies I have "used" (please make a note of this word) primarily host-pathogen systems but my curiosity for completely different systems was particularly vivid last year and I was happily moving away from infectious diseases when the pandemic started.
Early in the Covid pandemic I started being approached by reporters who wanted to understand what was happening and what to expect over times to come. To respond to them I started studying the data and interpreting it in light of my models.
We were in March-April and kept hearing herd immunity thresholds around 60-80%. So as a matter of honesty I had to explain to my reporters that under the models I was using (which accounted for individual variation as that's what I do) HITs were much lower (10-20% in Europe).
In matter of days my life turned 180 degrees! All sorts of craziness from people challenging those numbers to going all the way to challenge me as a person and a suite of models that have nothing to oppose. When in fact neither me nor the models really care about what HITs are.
But I had to respond to the new challenges. I hoped that estimating those damn HITs would put things to rest and let me go back to my new research. To speed things and try to get this over with asap I contacted potential collaborators and we made a plan.
We released our results at the end of April and I was really hoping to return to my other research soon after. But we were challenged to do more which we could do as more data became available. This confirmed those low HITs that for some obscure reason so many seem to hate.
Things went a bit quiet over the northern summer, only to light-up again when the second waves started in Europe. I absolutely have to make clear that I have seen nothing in these second waves in Europe that disproves those low HITs. Not that I mind but that's what analyses say.
I also don't think that HIT is a useful concept to be discussing so much in isolation (as said above what made me even talk about it in the first place was to make reporters appreciate how different my model was from others that were in use back in March-April).
Hypothetically: 1) estimating HIT=20% in Europe does not contradict HIT=30% in Brazil; 2) estimating HIT=20% in summer does not contact HIT=30% in winter; 3) estimating HIT=30% does not preclude HIT=50% if a much more sensitive Ab test is used to calibrate the model.
Can we be constructive please. Many of us just want out of this mess and get on with more interesting research.

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More from @mgmgomes1

4 Oct
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 (…, 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 (…).
Read 4 tweets
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…
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

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