Is the NHS overwhelmed?

The government’s  mantra to justify tough lockdowns has been the need to save the NHS from being overwhelmed with Covid cases. The actual number of COVID hospital cases is tracked on the government’s dashboard, and the last update was from 26th November and states

Patients admitted Daily: 1369  Last 7 Days: 10415

I had assumed that these figures were members of the public who had contracted COVID in their everyday lives  and then become so sick that they had to be hospitalised, like Boris Johnson. So I was surprised to see this tweet:

I decided to look into this. Digging down on the dashboard under “Health Care” we find there are actually 2 categories of hospital patients. 1. Patients admitted to hospital and 2. Patients already in hospital. This is confusing because at first sight 2. seems to be the total number of COVID patients within Hospitals. However these figures are all based on a very large spreadsheet reporting data from each NHS trust. In that spreadsheet it is explicitly clear that 2. is “Total number of inpatients diagnosed with COVID (Last 24h)”. So that means these patients were admitted to hospital for other reasons (stroke, cancer, injury etc.) and who have then contracted COVID in hospital !

I took the Total England NHS data from this spreadsheet and plotted it:

I get exactly the same result as @StatisticsGuy. This means that by far the majority of patients contracted “COVID” in hospital after being admitted for something else! The number of community infections leading to hospital admission is much less! Hospitals seems to be a risky place for patients getting infected by COVID.

Here is the ratio of the two groups of cases with time

Note how the ratio varies inversely to the infection rate.

The conclusion is that when infection rates in the community are low as during the summer, then cases are mostly community infections. However when the community rates are high then hospital infections dominate. That means that infections are probably being spread to inpatients within hospitals by staff, cleaners & visitors because they get infected, perhaps asymptomatically, within the community. Hospitals are often too warm with little or no ventilation and many patients are held together on a ward.  It is easy to imagine how easily infections can spread in such an environment.

What about death statistics? Nearly all “COVID” deaths occur within hospitals. These are defined by PHE as deaths which occur within 28 days (4 weeks) of a positive test. So the question is : How many of these “hospital” cases would have died anyway from the underlying condition that put them in hospital in the first place?  These are then so-called deaths with COVID. If we assume that half of them were deaths with COVID then that still has a dramatic effect on the overall death rate. It reduces it by ~40% during the first peak and by 33% during the second peak.

COVID Deaths in England by date of death. Last week still uncertain. Corrected is scaled as described.

This then implies that  the number of deaths caused directly by COVID should be nearer 33,000 rather than the official figure of 51718 as of  1st December.

It also reduces the estimate for the Infection Fatality Rate (IFR) by 33%

 

Posted in Covid-19, Public Health | 10 Comments

Covid infection rates falling

The UK weekly infection rate has been falling since the 18th November and is now about the same as that in Germany (150/100,000). Note that infection rates had mostly stabilised already by early November.

Weekly Infection rates comparison (derived from ECDC data).

The fall in rates for France has been even more dramatic from a peak of 570/100,000 on 8th November now down to below that of Germany’s in just 4 weeks. Italy too is well past the second peak and falling fast. Germany however is now suffering worse during the second wave than it did during the first wave.

Mass vaccinations will naturally bring infections rates down as the pool of susceptible people reduce. If we could perhaps  vaccinate >20 million people by Easter then the epidemic would essentially be over and normal life can probably resume. It can only end with “herd immunity”.

Modelling Coronavirus

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Global Temperature falls 0.15C in October

The global average temperature anomaly for October was 0.706C, which is a fall of 0.15C from September. This reduces the annual average so far to 0.9C  leaving 2020 still slightly higher than 2016. However the uncorrected data (without pair-wise homogenisation) leave 2020 just below 2016. My calculation of the global temperature anomaly is based on GHCN V4 and HadSST3 using a 3D spherical triangulation method and a baseline of 1961-1990.

Global average temperatures (anomalies) where 2020 is averaged over 10 months. The green points are the uncorrected temperature data

The monthly data below shows a large drop in temperatures  for October

Monthly average temperatures V4U is the uncorrected GHCN V4 data and V4C the corrected version using pair-wise homogenisation.

The spatial distribution below shows lower than average temperatures across North America., Central Asia and Western Europe. Blue colours show temperatures below the 1961-1990 average for October.

Spatial temperatures in the Northern Hemisphere

There were also cooler ocean temperatures in the Southern Hemisphere.

I have noticed an interesting effect of the pair-wise homogenisation process. Recent months seems to show a large divergence between the corrected and uncorrected GHCN V4 results. However this difference slowly decays with time so that past differences reduce. This is because the corrected data from previous months and years also slowly change as the homogenisation algorithm is rerun each month. This seems to produce a self correcting process tending to reduce strong discrepancies. over time.

Posted in AGW, Climate Change, climate science | Tagged , | 8 Comments