The COVID-19 pandemic has shown power of open data and analytics in research, but these activities often aren't recognised in traditional academic metrics. New perspective piece with @rozeggo & @sbfnk: journals.plos.org/plosbiology/ar…. I'd also like to highlight some examples... 1/
There are many, many other examples out there. COVID-19 has shown academics are highly motivated to produce work with immediate impact, even if it's beyond scope of traditional metrics. Now is the time to change the incentive structure to recognise these efforts. 9/9
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A short thread about a dead salmon and implausible claims based on epidemic curves... 1/
A few years ago, some researchers famously put an Atlantic salmon in an fMRI machine and showed it some photographs. When they analysed the raw data, it looked like there was evidence of brain activity... wired.com/2009/09/fmrisa… 2/
Now of course there wasn’t really any activity. It was a dead salmon. But it showed that analysing the data with simplistic methods could flag up an effect that wasn’t really there. Which leads us to COVID-19... 3/
'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:
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/
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
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/
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:
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/