I wrote recently about wet bulb temperatures (WBTs), and why we should be worried if they get too high. In that post, I mentioned that I had excluded data from before 1990, as I was concerned about the data quality. For the rest of that post, I assumed that the quality control of the HadISD dataset that the Met Office does would be adequate and that the episodes of extreme WBTs that I found in the dataset were real.

I’ve been thinking about that some more, and I think I probably need to be a bit more careful about data quality. Although the Met Office’s quality control procedures are very thorough, the observations that I’ve been looking at are by definition outliers. So even if 99.99% of the observations in the dataset are genuine (and I don’t know if that’s the right figure), the extreme observations I was interested in are far more likely to be in that remaining 0.01% than some randomly selected observation.

I’ve had some discussions about this with Dr Kate Willett from the Met Office, who has been supremely helpful and has given me some great ideas about how to look in more detail at the data quality. I’m very grateful to Dr Willett for her support, and much of the deep dive into data quality in the rest of this blog post uses her ideas. Any errors in my interpretation of the data below are mine.

So, for today I’d like to look at some of the extreme WBT episodes I found and look in more detail about whether they appear to be genuine or some artefact of faulty instruments or similar.

I gave a list of extreme WBT episodes in my last post, and I’m going to use a slightly different one here, though several of the episodes are common to both lists. Today I’m looking at all episodes where the WBT was recorded as being at least 35°C for at least 4 h, and I’m including all data at any time in the dataset (last time I excluded any observations before 1990). This gives us the following list of 26 episodes:

Weather station	Date of observation	Hours WBT ≥ 35	Max WBT
723870-03160, DESERT ROCK AIRPORT, Nye County, Nevada, United States (36.621, -116.028)	08 May 1979	6	38.8
	13 May 1980	4	39
	24 May 1980	5	36.7
	25 May 1980	12	38.6
	29 May 1980	5	39.4
	19 Apr 1981	6	39
	20 Apr 1981	11	39.8
	21 Apr 1981	4	38.8
	20 May 1981	9	39.8
	09 May 1982	4	39.1
944490-99999, LAVERTON AERO, Shire Of Laverton, Western Australia, 6440, Australia (-28.617, 122.417)	22 Jan 1995	6	36.2
952050-99999, DERBY AERO, Shire Of Derby-West Kimberley, Western Australia, Australia (-17.367, 123.667)	03 Feb 2001	4	36.8
404160-99999, KING ABDULAZIZ AB, Dammam Governorate, Eastern Province, Saudi Arabia (26.265, 50.152)	08 Jul 2003	5	36.5
417150-99999, SHAHBAZ AB, Jacobabad District, Larkana Division, Sindh, 79000, Pakistan (28.284, 68.45)	06 Jun 2005	6	37.4
954820-99999, BIRDSVILLE, Diamantina Shire, Queensland, Australia (-25.898, 139.348)	30 Dec 2006	4	36.6
952050-99999, DERBY AERO, Shire Of Derby-West Kimberley, Western Australia, Australia (-17.367, 123.667)	16 Apr 2009	4	36.5
941310-99999, TINDAL, Town of Katherine, Northern Territory, 0850, Australia (-14.521, 132.378)	04 Nov 2011	4	36.5
	06 Nov 2011	5	36.9
942170-99999, ARGYLE AERODROME, Shire Of Wyndham-East Kimberley, Western Australia, Australia (-16.633, 128.45)	13 Jan 2013	6	36.9
946590-99999, WOOMERA, Pastoral Unincorporated Area, South Australia, Australia (-31.144, 136.817)	11 Mar 2013	6	36.6
943120-99999, PORT HEDLAND INTL, Town Of Port Hedland, Western Australia, Australia (-20.378, 118.626)	24 Mar 2013	4	36.9
952050-99999, DERBY AERO, Shire Of Derby-West Kimberley, Western Australia, Australia (-17.367, 123.667)	29 Sep 2013	4	35.8
760400-99999, EJIDO NUEVO LEON BC., Municipio de Mexicali, Baja California, Mexico (32.4, -115.183)	23 Jul 2018	6	38.4
948170-99999, COONAWARRA, Wattle Range Council, South Australia, Australia (-37.3, 140.817)	10 Jan 2021	4	36.5
760400-99999, EJIDO NUEVO LEON BC., Municipio de Mexicali, Baja California, Mexico (32.4, -115.183)	21 Jul 2021	6	37.5
	16 Aug 2021	6	37.2

For each of those episodes, I have drawn a panel of 6 graphs which I hope is going to give us a clue about whether the data look reliable. I have plotted the dry bulb temperature (what we would normally call just “temperature”) on the left and the humidity on the right. WBT is calculated from dry bulb temperature and humidity, so if either of those variables looks wrong, then the calculated WBT is likely to be wrong as well.

The top graph shows the evolution over a 4 year period centred on the extreme episode. This lets us see if we’re following something approximating to normal seasonal variation for the location or if there has been some kind of spike. It’s a bit much to plot individual hourly values over that timescale, so I have calculated summary statistics for each week: the median, 90th centile, and 98th centile. If there’s a sudden increase in the gap between the median and higher centiles, that suggests that we may have weird outliers.

The middle graph shows a 6 day period centred on the maximum of WBT, this time plotting individual values, and shows nearby stations for comparison. I have included up to 5 other stations within 200 miles of the index station. There may be fewer than 5 other stations if there weren’t 5 stations within 200 miles. This lets us see whether the values are following a reasonable diurnal variation and whether they are obvious outliers compared with nearby stations.

The bottom graph compares the values with other datasets. I have used Visual Crossing and ERA5. Visual Crossing is a commercial weather service, and ERA5 is a publicly available dataset from the European Union’s Copernicus programme (part of the EU space programme). Often the Visual Crossing data are identical to the index station (I’ve added a tiny amount of random noise to the graphs here just so that you can still see both data series without one sitting on top of the other and hiding it), as Visual Crossing and the HadISD data that I used as my primary source both come from the same underlying dataset (the NOAA Integrated Surface Database). But Visual Crossing use different QC procedures to the ones the Met Office use, so where they do differ, that does suggest that something is up. The Visual Crossing data extend for only 3 days rather than the whole 6 days, as I’m a cheapskate and only subscribe to their free service and downloading much more data than that would have exceeded my download limits. ERA5 is a reanalysis dataset using data from various sources, and so is a more independent source of data.

The x axis for the 2 lower graphs is titled “Local time”, but I should point out that this isn’t necessarily exactly local time. Rather than go to the trouble of trying to look up local time zones for each location and date, I simply assumed that the world was divided into 24 equal sized time zones and that daylight savings time didn’t exist, for ease of calculation, and then just calculated the local time from the UTC time and the station longitude. So this may be an hour or two out from the actual local time, but should be close enough that we can tell if the diurnal variation seems reasonable. So if you’re keen enough to look up specific observations and find the times don’t quite match, that’s why.

As in my last post on this subject, I do need to emphasise that I am a medical statistician and not a climate scientist, and maybe I’ve overlooked something important or erred in my interpretation of the data. So please don’t assume that everything I write below is absolutely bullet-proof.

So, bearing that caveat in mind, let’s take a look at some graphs.

Here is the graph for the first episode:

There are some really obvious problems here. The temperature data simply don’t look remotely plausible. I’d previously discussed this observation station with Dr Willett, and she thought maybe someone had mixed up Fahrenheit and Celsius in the temperature observations. The data do seem consistent with that: if you assume that some of those implausibly high values are actually figures in Fahrenheit, then they match up quite well with the other datasets.

Here are the graphs for the next 2 episodes, at the same station. They are very similar. I won’t bore you with the graphs for the remaining episodes at that station, but they are also similar. It is clear that the episodes from this station are not trustworthy.

The next episode, episode 11, comes from Western Australia. The temperature looks perfectly plausible, but something looks wrong with the humidity, with an obvious spike around the time of the episode and a large deviation from the ERA5 dataset as well as the 2 nearest stations. This also doesn’t seem to be a genuine high WBT episode.

Episode 12, also from Western Australia, is similar, in that the temperature looks plausible but the humidity looks unreasonably high and seems more likely to be some kind of instrument malfunction than real extreme humidity.

Episode 13, from Saudi Arabia, is probably genuine. The temperature looks perfectly reasonable as judged by the seasonal average, the diurnal variation, and some nearby weather stations, though it is a little higher than the ERA5 dataset. The humidity doesn’t have the obvious red flags of the episodes 11 and 12, though it does seem to go up a bit within 24 h of the episode peak compared with a couple of days on each side. However, this web page from NOAA mentions that the highest dew point ever recorded was on 8 July 2003 at Dharhan, Saudia Arabia, which is more or less exactly the location of the weather station, and does suggest that some extreme weather was happening about that time.

Episode 14, from Pakistan, looks like it could be genuine, though again, it’s hard to be sure. The temperature certainly seems plausible, but the humidity, while broadly in line with nearby stations and not showing any obvious signs of a spike, is higher than the the ERA5 dataset. I haven’t been able to find a media report specifically about this location and date, but this article says that more than 500 people died from heat in May and June in Pakistan, India, and Bangladesh, which does seem consistent with this episode being genuine.

Episodes 15 to 22 are all from Australia. I don’t think any of them is genuine. The temperature looks plausible in all cases, but the humidity looks wrong: out of whack with the nearby stations and very different from the ERA5 data.

Episode 23 is from Mexico. Again, the temperature looks plausible, but the humidity is very different to both the ERA5 and the Visual Crossing data, as well as being considerably higher than several nearby weather stations. I don’t think this is genuine.

Episode 24 is from Australia again, and the humidity looks wrong again. I don’t think this is genuine either.

Episodes 25 and 26 are from the same weather station in Mexico as episode 23, and the humidity again looks implausible.

So of our 26 episodes, it looks like almost all of them are not real and are probably the result of faulty instrumentation or faulty data entry into a database. Episode 13 from Saudi Arabia in 2003 looks very likely to be real, and episode 14 from Pakistan in 2005 may very well be real, though it’s hard to be sure.

So in fact prolonged spells of WBT above 35°C do seem to be very rare, for now at least. In my next blogpost I shall look more at data quality and see if I can come up with a statistical algorithm for distinguishing the real episodes from the others, as I don’t think it’s going to be feasible to look at these graphs one at a time for the less extreme, but still worrying episodes of high WBT, for example episodes of WBT above 32°C, as these are much more common. Once I have been able to distinguish more reliably between the episodes that are real and those that aren’t, then I’ll be able to do a better job of looking at whether the frequency of dangerously high WBT episodes is increasing.

I’ve gotta be honest, I hadn’t heard of the concept of a “web bulb temperature” until earlier this year. I’ve heard about them quite a bit in recent months, and I’m sure we’ll all be hearing about them a lot more often from now on.

If the concept is also new to you, let me explain. It’s pretty much what it says. You take a traditional thermometer with a bulb of liquid at the bottom, and you wrap the bulb in a wet cloth. The water in the cloth evaporates, and the evaporative cooling means that the thermometer will read a lower temperature than it would have done if it were dry. The amount of cooling depends on the humidity of the air: in dry conditions, the evaporative cooling is efficient and the wet bulb temperature will be considerably lower than the dry bulb temperature, but in humid conditions, the difference is less marked. At the extreme, in 100% relative humidity, the water cannot evaporate at all and the wet bulb temperature (WBT) will be the same as the dry bulb temperature.

Why does this matter? And why am I writing about physics on a blog that’s mostly about medical statistics? Well, it has important implications for human health. People die in heat waves. And it turns out that WBT is important in understanding just how dangerous a heat wave is.

If you are exposed to 45°C heat for any length of time, that sounds pretty scary, but it need not necessarily be too dangerous as long as the air is not too humid, you are reasonably healthy, and you can stay well hydrated. We have evolved a cunning mechanism to stay cool in hot temperatures, namely sweating. This works in exactly the same way as the evaporative cooling in a wet bulb thermometer, and cools us down. Even if the air temperature is hotter than our body temperature, then in a dry atmosphere we can still cool down by sweating. But if we’re exposed to that kind of heat in humid conditions, then we’re in trouble.

So WBT is a way of measuring the combined effects of temperature and humidity. There is no magic cutoff for WBT below which everyone is fine and above which everyone dies. Moderately high WBTs that are mostly survivable for young healthy people may still kill large numbers of frail elderly people. However, the threshold of a WBT of 35°C is often mentioned as a limit of human survivability, and some research suggests that lower WBTs may be dangerous even to young healthy people. Certainly for people who aren’t young and healthy, high-ish WBTs below 35°C are likely to cause considerable excess mortality. The duration of high temperatures is also important. An extreme WBT may be survivable for 30 min, but not if you’re exposed to it for several hours.

The idea for writing this blogpost came from a conversation on social media. Fellow Mastodon user Kenneth Freeman asked the question of whether there is any way to know when WBTs hit 35°C somewhere on the planet. After a little bit of thought, I realised that this was a really important question to answer, so I thought I’d have a go.

If you are impatient to know what the answer is, feel free to skip to the results below. But if you are interested in all the geeky stuff about how I found out, then read on.

There are many weather-related websites that will let you search for the weather conditions at a given location at a given time. However, although they are presumably linked up to giant datasets of weather conditions at different locations and times, and so could in theory be easily queried to find the locations and times for specific weather conditions, I couldn’t find any website that would let you search that way round. But Kenneth tooted a link to an relevant scientific paper which had also examined the question of how often WBTs of 35°C are observed. They had used data from the HadISD dataset: a large dataset of observations from weather stations around the world, going back in some cases to the 1930s and updated monthly with the latest data, which is freely available online thanks to the UK Met Office. This seemed to be a good way to find out the answer to Kenneth’s question.

A good way, though not a trivially easy way. There are about 23GB of data in the dataset, which all needs to be sifted through. The dataset is stored in netCDF files, which apparently is a format used widely in climate science, but was new to me. However, there is of course a Python package to read netCDF files, so after a brief learning curve I was able to read the data and look for high values of WBT using Python code.

As an aside, this project turned out to be a brilliant example of the power of open data. One of the things that 20+ years’ professional experience as a statistician has taught me is that you should always make sure you understand exactly what you are looking at in your dataset before you try to do anything with the data. So as part of that process, I re-calculated the WBTs using the temperature and humidity data in the dataset to make sure I’d understood what the WBT variable in the dataset was and that it was what I was expecting it to be.

The WBTs that I calculated were not the same as the WBTs provided in the dataset.

I assumed at this stage that I must have got something wrong. I’m a medical statistician, not a climate scientist, and I figured that the climate scientists at the Met Office would know how to calculate WBT better than I do. So I emailed the Met Office to ask them what I was doing wrong, and it turned out that in fact my calculations were correct and they had an error in their code that they’d used to calculate WBT. I have to say that I am very impressed with the speed with which they corrected their data. The day after I emailed them, an updated version of the dataset appeared on their website, along with an explanation that they’d had to correct it.

It’s ever so easy to make mistakes when analysing data. We all do it. By making their data freely available online, the Met Office had vastly increased the chances that someone might come along and spot the mistake so it could be corrected. How long would that error have remained in the dataset if the data had not been freely available, one wonders? Top marks to the Met Office for making the data available and for promptly and transparently correcting the error once I’d alerted them to it.

Although the dataset goes back to the 1930s in some cases, most of the weather stations included in the dataset don’t go back that far. Many of the stations came on stream in the 1970s and 1980s. By 1990, 90% of the current number of weather stations were already contributing data. So I am starting my analysis at 1990, to avoid the problem of trying to make comparisons across time periods with vastly different amounts of data collection.

In addition to that, the data quality before 1990 looks like it may not always be that great. I did have a look back at earlier data to see what was there, and I found one weather station in the US with some very prolonged spells of WBT > 35°C around 1980. This seemed like such an outlier I contacted the Met Office about it (it has to be said that the team behind the HadISD dataset at the Met Office are wonderfully helpful people), and after taking a look at it they thought that the data really didn’t seem reliable, and that the most likely explanation was that the temperature data had been recorded in Fahrenheit and whoever originally uploaded the data had forgotten to convert it to Celsius.

Data quality is something that needs a lot of careful work, and the Met Office do have some extensive quality control procedures, which are described in a published paper. Of course the occasional spurious value can slip through even the most rigorous quality control, but having excluded the pre-1990 data, I am assuming that the Met Office’s quality control procedures are good enough and that the data are reliable. Certainly nothing else I’ve seen later than those values from the US station around 1980 leapt out at me as being obviously weird.

This analysis is somewhat quick and dirty at this stage. You will note that I’ve called this post “Wet Bulb Temperatures, Part 1”, as I plan to come back another day and tell you about some more sophisticated analyses. I don’t have any maps to show you today, and I’d love to do that in the reasonably near future. I have also used a very rough and ready method of calculating the duration of spells of high WBT: I have assumed that the spell lasted from the first time a weather station recorded a WBT above the threshold and ended at the last time the WBT was above the threshold at the same station, having been consistently above the threshold since. For long spells and stations that record data every hour, that’s probably not a bad approximation. However, consider a hypothetical example of a station reporting data only every 3 hours, with the WBT at 34.9 at 1200, 36 at 1500, and 34.9 at 1800. I would have counted this as a zero duration above 35, as there was only a single observation above 35. However, in practice, the WBT was probably above 35 for most of the time between 1200 and 1800. So my analysis here is rather conservative: the actual number of spells of long duration is probably higher than I’m reporting here. It wouldn’t be that hard to come up with more accurate estimates by interpolation between the observations, but I haven’t done that yet. I’ll do that another time and let you know what that looks like.

I have also not taken account of where stations are. If 2 stations that are close to each other both report episodes of high WBT at the same time, I have counted that as 2 separate episodes, although it might be more accurate to count it as 1 big episode. However, I guess it’s still a measure of how serious the extreme temperatures are. Also, what I’m focusing mostly on here is episodes where the WBT was at least 35°C for 6 h, as those as the episodes which are most likely to pose a serious threat to human survivability, and I have checked and none of those episodes were recorded at more than one station on a single day. I can’t promise that’s true of some of the less extreme episode durations. I hope to do some kind of analysis of the geographical extent of extreme WBT episodes some other time.

And my final caveat is that I am a medical statistician, not a climate scientist. I am aware that I’m going rather outside my field of expertise by analysing climate data, and I can’t exclude the possibility that I have overlooked something really important that would be obvious to any climate scientist. If you think I have erred in that way, do let me know via the comments below.

Anyway, with those caveats in mind, let’s look at the results.

Results

I’ll start with a list of extreme WBT episodes. These were any time the WBT was at least 35°C for 6 h or more (there were in fact no episodes longer than 6 h), or was at least 32°C for more than 12 h.

Weather station	Date of observation	Hours WBT ≥ 35	Hours WBT ≥ 32	Max WBT
944490-99999, LAVERTON AERO, Shire Of Laverton, Western Australia, 6440, Australia (-28.617, 122.417)	22 Jan 1995	6	6	36.2
412460-99999, SOHAR MAJIS, Al Batinah North Governorate, Oman (24.467, 56.65)	5 Jul 1998		15	32.9
412460-99999, SOHAR MAJIS, Al Batinah North Governorate, Oman (24.467, 56.65)	12 Jul 1998		18	34.2
412420-99999, DIBA, Musandam Governorate, Oman (25.617, 56.25)	8 Jul 2001		15	33.4
412420-99999, DIBA, Musandam Governorate, Oman (25.617, 56.25)	4 Jul 2002		18	33.2
412580-99999, MINA SULTAN QABOOS, Muscat, Muscat Governorate, Oman (23.633, 58.567)	28 Jun 2004		15	32.7
412420-99999, DIBA, Musandam Governorate, Oman (25.617, 56.25)	12 Jul 2004		15	33.5
417150-99999, SHAHBAZ AB, Shikārpur District, Sindh, Pakistan (28.284, 68.45)	6 Jun 2005	6	12	37.4
942170-99999, ARGYLE AERODROME, Shire Of Wyndham-East Kimberley, Western Australia, Australia (-16.633, 128.45)	13 Jan 2013	6	12	36.9
946590-99999, WOOMERA, Pastoral Unincorporated Area, South Australia, Australia (-31.144, 136.817)	11 Mar 2013	6	9	36.6
760400-99999, EJIDO NUEVO LEON BC., Municipio de Mexicali, Baja California, Mexico (32.4, -115.183)	23 Jul 2018	6	12	38.4
412170-99999, ABU DHABI INTL, Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates (24.433, 54.651)	19 Aug 2020		15	33.1
760400-99999, EJIDO NUEVO LEON BC., Municipio de Mexicali, Baja California, Mexico (32.4, -115.183)	21 Jul 2021	6	6	37.5
760400-99999, EJIDO NUEVO LEON BC., Municipio de Mexicali, Baja California, Mexico (32.4, -115.183)	16 Aug 2021	6	6	37.2
412670-99999, QALHAT, Ash Sharqiyah South Governorate, Oman (22.667, 59.4)	9 Jul 2022	3	33	36.2
412400-99999, KHASAB PORT, Musandam Governorate, Oman (26.217, 56.25)	24 Aug 2023		30	33.1

(The “Weather station” details are the ID number of the weather station and its name as recorded in the HadISD dataset, followed by the address of its region, and finally the latitude and longitude coordinates.)

What we can see is that there have been only a handful of episodes of a WBT of ≥ 35°C that lasted for 6h, and they are skewed towards the more recent years of the dataset. What is particularly striking is that the paper by Raymond et al I linked to previously, published in 2020 and using data up to 2017, which looked at the emergence of high WBTs, is already badly out of date. Of the 7 episodes of WBT ≥ 35°C that I have found, 3 of them have occurred after 2017. There are also 2 episodes of WBT ≥ 32°C that lasted for over 24 hours, which must be very hard to deal with, which have both occurred in the last couple of years.

Although I haven’t done any statistical analysis to see how likely it is that the clustering of extreme heat episodes in the later period could be due to chance, it certainly looks at first glance that the frequency of such events is increasing.

Let’s look at some graphs as well. Here are the number of episodes of WBTs above thresholds from 32 to 35°C of varying durations over time.

Again, I have not done any statistical analysis of this, but it does seem that there is an upward trend in many of those graphs with the frequency of high WBT events increasing over time.

If this trend of increasingly extreme WBT events continues, this could have serious implications for human health in the affected areas. It is no exaggeration to say that thousands, maybe millions, could die in such extreme weather events.

So this would be a really bad time to relax our efforts to reduce greenhouse gas emissions. But surely no-one would be stupid enough to do that, would they?

Update 29 December 2023: please see the next post, Wet Bulb Temperatures, Part 2, which explains why much of the data above may not be all it seems.

Well, 2020 was quite a year. I’m sure it’s one that most of us are glad is over.

Here in the UK, we have been badly hit by the covid-19 pandemic, indeed we have one of the worst death rates in the world. It didn’t have to be this way: as an island nation with a well developed health system, we could have handled the pandemic far better. Unfortunately, we have a government of incompetent idiots who have simply not been up to the job of dealing with it.

As I write this in early January 2021, covid-19 cases are at high levels and rising rapidly, following a reasonable approximation to exponential growth since the beginning of December with a doubling time of just over a fortnight. This is, frankly, terrifying, given that hospitals are already stretched to their limits.

But there is a ray of hope, in the shape of vaccines. We now have 3 vaccines approved for use in the UK, and over a million people have already been vaccinated. It has been an extraordinary achievement to get not one, but 3 vaccines invented, tested in large clinical trials, and approved in such a short space of time. The scientists, clinicians, clinical research professionals, statisticians, regulators, and last but by no means least clinical trial volunteers should be incredibly proud of what they have achieved.

It will take many months or possibly even years to vaccinate the whole UK population. But sensibly, vaccination is being prioritised for those most at risk, mainly starting with older age groups. The government have promised that they will have vaccinated the 15 million people at highest risk by the middle of February, including everyone over 70 as well as health and social care workers and those who are clinically extremely vulnerable.

They will break that promise of course, just like they break all their promises.

But hopefully at some time in the next few months, even if not as early as mid-February, all those high risk people will have been vaccinated. What does that mean for getting our lives and the economy back to normal?

Vaccinating that number of people will certainly not give us any meaningful herd immunity, but given that most deaths from covid-19 occur in the elderly, we would expect that vaccinating all the over 70s will dramatically cut the death rate.

At that stage, there may be a temptation on the part of politicians to open up the economy again, taking the view that perhaps it doesn’t matter if covid-19 is still circulating widely if few people are dying from it.

I think this would be a mistake. First, just because most deaths from covid-19 occur in the elderly, it does not mean that younger people don’t die from it at all. Very approximately 10% of covid-19 deaths are in people under the age of 60, and if the virus is spreading rampantly through the population and millions of people are infected, then the absolute numbers of younger people who die will not be negligible.

But there is a further reason to be cautious: long covid.

There is still much that we don’t know about long covid, but what we do know is that a small proportion of patients continue to have significant symptoms weeks or even months after the acute infection. It has been estimated that about 1 in 10 patients still have symptoms after 12 weeks.

If millions of people are being infected, then that suggests that hundreds of thousands of people may suffer from long covid.

What we don’t yet know is how long the symptoms of long covid last. Maybe most people will be back to normal within a year, or maybe the symptoms are generally permanent. We simply do not yet have enough long term data to know, given that the disease only first appeared just over a year ago.

Some of the symptoms of long covid are very worrying. Quite apart from potentially permanent lung and heart damage, one study found that cognitive performance could be reduced in a manner equivalent to 10 years of ageing.

If the symptoms of long covid do turn out to be permanent, then having hundreds of thousands of people affected by them would be nothing short of a public health catastrophe.

So while there will be a temptation to get back to normal life once deaths from covid are much reduced following vaccination of those at higher risk, I think that temptation needs to be resisted for a while longer until enough of the population have been vaccinated to give significant herd immunity.

At any rate, much as I miss my local pubs, I will not be going back to them until after I’ve had my vaccine.