I've built a model to show the concept of herd immunity.
It shows why we need to not leave hard-to-reach parts of the population unvaccinated.
A thread.
Herd immunity, also called population immunity, is the protection for the population that comes from when a proportion have been vaccinated.
With more vaccinations, we move towards this herd immunity threshold.
(We're not there yet, even though some say we are.)
Here's my model. Imagine a population in a country.
People are either susceptible (they may not be vaccinated or have had the virus). We colour these green.
People may have had the vaccine. These are blue.
And there's one (near the bottom left in purple) that is infectious
Now, in my model, they will infect anyone who is susceptible (green) within that little circle surrounding them. And those will infect people surrounding *them*
After a few generations, more and more are infected.
Eventually, almost all (although not all - some are fortunate) are infected.
That's bad. And it's because we haven't reached the herd immunity threshold.
So - what happens when we reach the herd immunity threshold (or get close to it)?
We have many more vaccinated (they're blue).
Let's see what happens when the infectious person (this time in the middle) infects others.
Well, in this case, there's a local infection, but the infection can't be sustained (that's good). Herd immunity.
But.
What happens when those vaccines are not spread out equally across the country?
Let's vaccinate *the same number of people* just in the bottom half of our population.
There we are - lots of vaccinated people in blue. That infection (right middle) doesn't stand a chance.
Now let's see what happens when that infectious person is in the top half among the unvaccinated population (remember, we have the same number of vaccinated people in the population as a whole).
Well, that infection spreads...
... and spreads ...
... and spreads ...
until huge numbers of people are infected *even though we have overall reached the herd immunity threshold*
And that is why we need to vaccinate evenly, not leaving pockets where infection can spread.
If anyone would like to work with me on this, do let me know. I've built the agent-based model and would be happy to work with others turning it into a paper. @chrischirp@AdamJKucharski@GrahamMedley
If anyone is interested in the seemingly different ways we can model spatial transmission, here's a paper I wrote pre-Covid showing the similarities between the different approaches of:
- population
- network
- agent-based
- cellular
models
"Prisons are highly prone to outbreaks of COVID-19 and consequent raised risks of hospitalisations and deaths (high confidence). Age-standardised mortality rates are significantly higher for prisoners than the general population. ...
" ... Incidence of disease, number and size of outbreaks, hospitalisation and mortality rates in prisons have increased markedly in wave 2 compared to wave 1 (high confidence). Prisons can act as amplifiers for community infection."
1. The vaccine deployment programme continues successfully 2. Evidence shows vaccines are sufficiently effective in reducing hospitalisations and deaths in those vaccinated
...
3. Infection rates do not risk a surge in hospitalisations which would put unsustainable pressure on the NHS 4. Our assessment of the risks is not fundamentally changed by new Variants of Concern
Public Health England @PHE_uk have published the update for variants of concern and variants under investigation. Note that these are cases detected (and that these may be detected as the result of surge testing).
Rises in VUI B.1.525 (+26 since Tuesday) & VUI B1.1.318 (+10)
Here is the data table (data from @PHE_uk). Note B.1.1.7 is excluded in the chart above. To emphasise, these are the result of non-uniform testing and sequencing.
They find that fewer than 1 in 5 students (17.8%) had antibodies at the end of the Autumn term (which imples that more than 80% were susceptible to Covid).
"SARS-CoV-2 seroprevalence in 2,905 university students was 17.8% (95%CI, 16.5-19.3) ..."
"... ranging between 7.6%-29.7% across the five universities" (the universities are not named - 'University A' to 'University E')
The Government's roadmap for reopening has been published.
I will add commentary as I read through.
From first glance, there are no numbers for thresholds, which does in some way question the notion of 'data not dates'.
There are however a lot of dates in the document.
The roadmap itself is only 15 pages long.
It sets out 'principles'
- whole of England rather than regional response
- "led by data not dates" (see above)
- five weeks between steps - 'no earlier than'
- face-to-face education a priority