How different would the global dynamics have been if COVID-19 had instead been a pandemic flu virus with similar fatality rate? A few thoughts... 1/
There are differences between pandemic flu and SARS-CoV-2, of course. In absence of control, serial interval for SARS-CoV-2 (science.sciencemag.org/content/369/65…) is longer than flu (ncbi.nlm.nih.gov/pmc/articles/P…), and evidence SARS-CoV-2 transmission more clustered (sciencedirect.com/science/articl…) 2/
The susceptibility profile may also be different. In flu pandemics, susceptibility is often concentrated in younger groups (pubmed.ncbi.nlm.nih.gov/20096450/) - for COVID-19, severity/susceptibility concentrated in older groups (e.g. nature.com/articles/s4159…). 3/
A few countries (e.g. South Korea & Japan) have used targeted approaches that account for the clustered dynamics of COVID-19, but many around the world have not (or at least not yet effectively):
4/
Many of measures currently being deployed - such as shutting down segments of society - come from the 1918 toolkit, which is why many of the debates are so familiar: 5/
Indeed, the approach of shutdown-type measures + strict border restrictions would likely work against pandemic flu - we know this because these measures have also suppressed the 2020 flu season in many places (e.g. academic.oup.com/cid/advance-ar…) 6/
It's tempting to put control measures in 'flu' boxes and 'coronavirus' boxes, but I suspect the global experience with COVID will also make many countries rethink their plans for a future severe flu pandemic. 7/7

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More from @AdamJKucharski

27 Nov
Some locations in Tier 3 had evidence of rising epidemics before November lockdown; others were declining. Same for Tier 1 & 2 – some were rising; some were declining. How come? There are three likely explanations... 1/
First, things like population demography, household structure, and nature of local industry will influence social interactions and hence transmission potential. As a result, baseline R may just be slightly lower in some locations. 2/

Second, high levels of infection will lead to some accumulation of immunity (in short-term, at least). Unlikely it's enough to go back to normal without outbreaks, but could be enough for control measures that would get R near 1 in spring to now get R below 1. (Data from ONS) 3/
Read 7 tweets
25 Nov
Relaxing UK COVID-19 control measures over the Christmas period will inevitably create more transmission risk. There are four main things that will influence just how risky it will be... 1/
We can think of as epidemic as a series of outbreaks within households, linked by transmission between households. This is particularly relevant over Christmas, given school holidays and some workplace closures. 2/
We can also think of R in terms of within and between household spread. If the average outbreak size in a household is H, and each infected person in household transmits to C other households on average, we can calculate the 'household' reproduction number as H x C. 3/
Read 8 tweets
22 Nov
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/
Read 8 tweets
12 Nov
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/
Read 9 tweets
11 Nov
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.)
2/
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/
Read 5 tweets
11 Nov
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 & @timwrussellcmmid.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/
Read 6 tweets

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