Stuart Gilmour Profile picture
Dec 31, 2021 12 tweets 4 min read Read on X
Will the UK soon see a tidal wave of COVID-related hospital admissions? Is Omicron really causing a drop in hospitalizations? Let’s look at the data. Image
I’m spending the afternoon watching the year end mixed martial arts, #rizin33, so it’s as good a time as any to examine the new surge in cases in the UK. There’s a lot of talk that omicron is less severe and hospitalizations won’t rise, but I’m not convinced. Let’s look. Image
The Guardian reports 12000 people in hospital in the UK, with past peak of ~35,000. Early reports of a UK government study found a lower risk of hospitalization and suggestions “it won’t be as bad as last time” even as cases are sky-rocketing. ImageImage
I’m suspicious about the idea that the hospitalization rate is going down with omicron. Let’s look at new cases and new hospitalizations. This figure shows the number of new cases, along with new hospitalizations (scaled by a factor of 10) since January 2021. Notice anything? Image
It looks like hospitalizations decoupled from cases in ~July, and as cases rose after “Freedom day” daily hospitalizations remained stable. I checked this by plotting the rate of admissions on day X per 1000 cases on day X-7 [assuming ~7 day delay from case report to admission] Image
This is the effect of vaccination and the surge in cases in children (who have lower risk of hospitalization) under the UK govt’s reckless back to school policy. You can see the clear shift in increased hospitalization rate after freedom day followed by the downward trend. Image
We only have a few weeks’ data on omicron, but it looks like hospitalization rates were dropping anyway. So I built a model of hospitalization as a function of past cases, with a term for omicron from 20th December, when it became dominant.
This model found an ~0.5% a day reduction in hospitalization rate since September, and an ~30% reduction in hospitalizations after omicron became established in mid-December. There was no significant effect of omicron on the downward trend. Image
Using this model I calculated predicted cases for the rest of the year based on the number of cases by specimen date. Here is the whole prediction since September. It’s not perfect but the trend is clear – daily admissions will rise to 2000 a day in the new year. Image
It looks like the UK will reach its past hospitalization peak by mid-January. If omicron hospital stays are shorter then the growth in total cases in hospital will be slower than the last peak, but if cases don’t drop rapidly the pressures on the NHS are unavoidable.
Some caveats: we don’t have much data on the omicron period (only a month); hospitalizations depend a lot on the age structure of infections, and I don’t have this info; most of my predictions have been “not even wrong” and I did this on my NYE break while watching MMA.
Nonetheless, the trend is worrying, and even more worrying is the ridiculous predisposition of UK politicians *and* many of its leading public health and medical establishment for breezy optimism in the face of repeated failures to handle a lethal infectious disease.

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More from @drStuartGilmour

Oct 15, 2022
Follow up on my tweets from yesterday complaining about the new economist report on how “autocratic” countries over-state their GDP. I will analyze the paper this article references, and show a range of sleight-of-hand and maths errors in this work.
This is an excellent and egregious example of how economists don’t understand and/or misuse statistical tools. I will be referencing this version of the work the Economist is discussing – there are many versions, this one is the most recent (2021). bfi.uchicago.edu/wp-content/upl…
The first sleight of hand is the confusion of levels and rates of growth. The author builds a theory of the relationship between *growth* in night time light (NTL) and *growth* in GDP (first two pics). But their final model (eqn 6) analyzes ln(GDP) – a level not a rate! ImageImageImage
Read 15 tweets
Oct 14, 2022
It’s so exhausting dealing with this torrent of bad-faith data “analysis” from the Economist. The latest is an analysis of satellite data on night time lights and gdp growth that suggests “autocratic” states fiddle their numbers on gdp growth. economist.com/graphic-detail…
For starters it’s obvious bad faith. This figure from the report it references shows the gdp growth and night time light growth for “free” and not-“free” countries. The assumption this is dishonest rather than just a different growth relationship is so condescending.
Anyone who has been to a rapidly growing low- or middle-income country knows they don’t have the same lighting as rich countries. This is “partly free” (?!) Dhaka. Bangladesh isn’t prioritizing street lights and has a different urban landscape to Tokyo (which is “free”).
Read 7 tweets
May 16, 2022
@dakekang and @huizhong_wu your reporting on prison rates in your latest article about Xinjiang is wrong and misleading. Assuming your linked list is true, the imprisonment rate is not “the highest anywhere in the world” and your numbers are just wrong.apnews.com/article/religi…
First, you report that US prison rates are 364 per 100000. This is not correct. The number is actually 537, but you didn’t include US Jails in your figure. Please correct it. We don’t need more articles understating the USA’s incarceration epidemic.
Second, you say that the Konasheher country rate (3789) is “the highest known imprisonment rate in the world”. This is false, because you compare a county with countries. There are *many* counties in the USA with higher rates. See e.g. Indiana.
Read 6 tweets
Mar 29, 2022
Hong Kong has experienced a wave of #covid19 cases and deaths, and some media are blaming this on Chinese COVID vaccines, saying they don’t work. Let’s talk about whether this is true, and the implications for global vaccine equity of vaccine misinformation.
A recent presentation by Hong Kong University (HKU) professors has been used by the usual China “experts” and journalists to argue that reliance on China’s vaccine, Sinovac, compared to BioNTech’s mRNA vaccine was a bad idea, with tweets like this.
Let’s look at this slide in detail. 2-dose Sinovac gives 77% protection against death in over 60s, but 3-dose Sinovac gives 98% protection. This is weird, and it suggests that there’s more to this story than a weak vaccine: Risk profile and timing. Let’s look at these.
Read 16 tweets
Feb 14, 2022
Remember in 2020 there was a map showing how well-prepared different countries were, which received widespread derision for its terrible accuracy? I analyzed the underlying data to see how poorly it predicted pandemic outcomes.
The map is based on the Global Health Security Index, a numerical measure of pandemic preparedness compiled by Economist Impact in collaboration with Johns Hopkins and others. There is a published report, with a clear methodology.
ghsindex.org
The data is available from their website, giving 195 countries an overall score and also scoring them on six sub-domains which measure things like anti-microbial resistance (AMR) preparedness, adherence to international health regulations, and so on.
Read 21 tweets
Feb 12, 2022
This week 10 years ago I first visited Minamisoma, and began a five year long collaboration with the local community studying nuclear, tsunami and earrthquake disaster response and recovery. A thread about my research and what we learned. Image
When the Great East Japan Earthquake and Tsunami hit I was a brand new assistant prof, living in Tokyo for two weeks. Almost as soon as it happened all the Japanese students from the dept I worked in headed north to help with recovery. [I took these photos in Feb 2012] Image
They all went to a small town called Minamisoma, very close to the Dai-ichi Nuclear plant, that was partially evacuated after the incident. First they did health checks but soon were asked to help with other things. [Map source: Morita et al, PLOS ONE, 2018] Image
Read 24 tweets

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