'Herd immunity' has been reached during previous epidemics of influenza, measles and seasonal coronaviruses. But it's subsequently been lost (and then regained). What are some of the reasons for this? 1/
Here we're using technical definition of 'herd immunity', i.e. sufficient immunity within a population to push R below 1 in absence of other control measures. But reaching this point doesn't mean R will stay below 1 forever. Here four things to be aware of... 2/
A: Population turnover. Over time, new births mean an increase in % of population susceptible. This will eventually lead to R>1 and new (but smaller) outbreaks - the more transmissible the infection, the sooner this recurrence will happen. More: 3/
B: Waning. For seasonal coronaviruses, immunity can also wane (e.g. nature.com/articles/s4159… & nature.com/articles/s4146…). Again, this will result in increase in susceptibility over time, and hence potential for R>1 in future. 4/
However, it's worth noting that subsequent infections for seasonal coronaviruses can come with reduced severity, so although transmission still occurs, burden can be lower during subsequent epidemics: 5/
C: Evolution. In the case of flu, antigenic evolution can mean immunity built against recent strains doesn't fully protect against new ones (nature.com/articles/nrmic…). Note: there's no evidence this is influencing SARS-CoV-2 dynamics so far (theconversation.com/coronavirus-mu…). 6/
D: Population migration. The immunity profile of a population may change over time as susceptible individuals arrive/leave. E.g. in 2014/15 there was mass cholera vaccination in IDP camp in South Sudan; by 2016, over 80% were susceptible (journals.plos.org/plosntds/artic…) 7/
The COVID-19 pandemic will eventually end because of accumulated immunity, either following infection or - ideally - from future vaccine. But even if this means R<1 initially, the above shows there are number of long-term possibilities to consider. 8/8

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More from @AdamJKucharski

7 Oct
How would a 'protect the vulnerable and let everyone else go back to normal' approach to COVID play out? I see three main scenarios, each with important consequences to consider... 1/
Scenario A: Let's suppose it's possible to identify who's at high risk of acute/chronic COVID-19. Then somehow find way to isolate these people away from rest of society for period it would take to build immunity in low risk groups and get R below 1 & infections low... 2/
This would mean isolating at least 20% of UK population (if use over 65 as age cutoff) and this period of isolation could be several months (or longer if rest of population continues to be cautious, reducing the overall rate of infection and hence accumulation of immunity). 3/
Read 6 tweets
6 Oct
If COVID cases/hospitalisations/deaths are rising - as they are in many European countries - there are only two ways the trend will reverse.... 1/
A. Enough change in control measures and/or behaviour to push R below 1. The extent of restrictions required will depend on population structure/household composition etc. But given existing measures are disruptive and R is above 1, could take a lot of effort to get R down. 2/
B. Accumulation of sufficient immunity to push R below 1. However, evidence from Spain (e.g. bbc.co.uk/news/world-eur…) suggests ICUs will start hitting capacity before this point, so to avoid them being overwhelmed, would likely end up cycling between epidemics and (A) above. 3/3
Read 4 tweets
4 Oct
I often see the misconception that control measures directly scale COVID case numbers (e.g. “hospitalisations are low so measures should be relaxed”). But in reality, measures scale *transmission* and transmission in turn influences cases. Why is this distinction important? 1/
If discussions are framed around the assumption of a simple inverse relationship between control and cases, it can lead to erroneous claims that if cases/hospitalisations are low, control measures can be relaxed and case counts will simply plateau at some higher level. 2/
But of course, this isn’t how infectious diseases work. If control measures are relaxed so that R is above 1, we’d expect cases - and hospitalisations - to continue to grow and grow until something changes (e.g. control reintroduced, behaviour shifts, immunity accumulated). 3/
Read 6 tweets
24 Sep
COVID app launches today. Would encourage everyone to download & use - we need every tool we can get to tackle this pandemic, and effectiveness will increase dramatically with number of users. Plus there are couple of features I hope could be particularly powerful...1/
First, streamlining venue registration could make it much easier to link people to settings of common exposure, helping notify people associated with superspreading events: 2/
Second, the app could help pick up contacts that would otherwise be hard to trace (i.e. causal contacts outside home/work/school) - we estimated that these are the contacts that can really hinder effectiveness of test & trace: thelancet.com/journals/lanin… 3/
Read 4 tweets
21 Sep
As well as limits on gathering size in recent months ( ), another feature of Sweden that stands out is household size - it's smallest average in Europe, with majority single occupant (ec.europa.eu/eurostat/web/p…). What effect could this have on transmission? 1/
The estimated risk of transmission per contact is higher within household than outside household (thelancet.com/journals/lanin…). This means it can be helpful to think of an epidemic as a series of within-household outbreaks, linked by between household transmission... 2/
If the average size of an outbreak in a household is H, and each infected person within a household spreads infection to C other households in community on average, then we can think of the 'household' reproduction number as equivalent to H x C. 3/
Read 6 tweets
20 Sep
I'm seeing more and more suggestions that groups at low risk of COVID-19 should go back to normal while high risk groups are protected. What would the logical implications of this be? 1/
First, let's pick an example definition of risk. If we use infection fatality risk alone for simplicity (which of course isn't only measure of severity), there is a clear age pattern, which rises above ~0.1% around age 50 and above ~1% around age 70 (medrxiv.org/content/10.110…) 2/
Suppose hypothetically we define the over 65 age group as 'high risk'. That's about 18% of the UK population, and doesn't include others with health conditions that put them at more at risk of severe COVID. 3/
Read 13 tweets

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