Some people are interpreting the below study as evidence that people who test positive without symptoms won't spread infection, but it's not quite that simple. A short thread on epidemic growth and timing of infections... 1/
If we assume most transmission comes from those who develop symptoms, there are 2 points where these people can test positive without having symptoms - early in their infection (before symptoms appear) & later, once symptoms resolved (curve below from: cmmid.github.io/topics/covid19…) 2/
So if people test positive without symptoms, are they more likely to be early in their infection or later? Well, it depends on the wider epidemic... 3/
If epidemic is growing, majority of infections will have occurred recently (because that's definition of growing). As a result, people more likely to be early in their infection than later. So +ve tests without symptoms are more likely to be on left hand side of this curve. 4/
This means having a lot of people testing positive without symptoms isn't particularly reassuring, because they may well transmit & become symptomatic in near future... 5/
In contrast, if epidemic declining, most people who test +ve will be later in their infection. So less likely to be infectious in near future. This is situation with above Wuhan study. Outbreak was over when study done, so +ve tests likely to be people infected a while ago. 5/
It's an important distinction to make, because it's the difference between saying '+ve tests without symptoms can't spread infection' and '+ve tests without symptoms may yet spread infection'. Given epidemics have been growing in many places recently, latter more likely. 6/
As a final note, this 'growing epidemic = people are earlier in infection' issue also has implications for screening measures, e.g. elifesciences.org/articles/05564.
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Why do COVID-19 modelling groups typically produce ‘scenarios’ rather than long-term forecasts when exploring possible epidemic dynamics? A short thread... 1/
Coverage of modelling is often framed as if epidemics were weather - you make a prediction and then it happens or it doesn’t. But COVID-19 isn’t a storm. Behaviour and policy can change its path... 2/
This means that long-term COVID forecasts don’t really make sense, because it’s equivalent of treating future policy & behaviour like something to be predicted from afar (more in this piece by @reichlab & @cmyeaton: washingtonpost.com/outlook/2020/0…). 3/
Data take time to appear on gov website, so deaths for 1st Nov now average over 320, not “just over 200” as claimed in this article. Either CEBM team aren't aware of delays in death reporting, or they are & for some reason chose to quote too low values. cebm.net/covid-19/the-i… 1/
Worth noting models above were preliminary scenarios, not forecasts. (Personally, I thought there were more than enough data/trends to be concerned about last month, regardless of results of one specific long-term modelling scenario from early Oct.)
Why do data delays matter? Because it's difference between simplistic narrative of 'all model values were too high' and realisation that despite model variation, median of quoted worst-case estimates (376 deaths) concerningly close to Nov 1st average (which will rise further). 3/
Because SARS-CoV-2 testing often happens after symptoms appear, it's been difficult to estimate detection probability early in infection. So great to collaborate with team at @TheCrick & @ucl to tackle this question, with @HellewellJoel & @timwrussell – cmmid.github.io/topics/covid19… 1/
We analysed data from London front-line healthcare workers who'd been regularly tested and reported whether they had symptoms at point of test (medrxiv.org/content/10.110…)... 2/
To estimate when people were likely infected and hence detection probability over time, we combined the HCW data with a model of unobserved infection times. 3/
As epidemic trajectories in Europe rise back towards their spring peaks, a thread on normalisation during a pandemic... 1/
In late Feb & early March, we modelled scenarios for what effect widespread social distancing measures might have in UK. Like others, we'd already modelled contact tracing (thelancet.com/journals/langl…) but characteristics of infection suggested additional measures would be needed. 2/
At the time we were doing this modelling, fewer than 3000 COVID deaths had been reported globally (below from 1st March). I remember doing media during this period and could tell many saw the potential impact of COVID-19 as rather abstract, given observed numbers to date... 3/
If the reproduction number drops below 1, an epidemic won't disappear immediately. But how many additional infections will there be before it declines to very low levels? Fortunately there's a neat piece of maths that can help... 1/
Let's start by considering a situation where one person is infectious. On average they'll infect R others, who in turn will infect R others, and so on. We'd therefore get the following equation for average outbreak growth: 2/
If R is above 1, the outbreak will continue to grow and grow until something changes (control measures, accumulated immunity, seasonal effects etc.) But if R is below 1, above transmission process will decline & converge to a fixed value. (Proof here: en.wikipedia.org/wiki/Geometric…) 3/
Comparison of SPI-M medium term projections for England hospitalisations/deaths and subsequent data has now been published (along with some model descriptions): assets.publishing.service.gov.uk/government/upl… 1/
Additional context on that early working analysis also now available (noting that unlike above, these were *scenarios* to illustrate a reasonable worst case without further interventions, not forecasts): assets.publishing.service.gov.uk/government/upl… 2/