Covid-19 Immunity Risk transmission Vaccines

South African and UK: two Covid-19 variants – two countries in crisis ?

The UK and South Africa are two countries where the transmission of the virus has escalated. Last week a highly transmissible new strain was identified as the source in the UK. Three days ago, the explanation of a marked upswing in cases in South Africa was also shown to be related to a new genetic variant of the Covid-19 virus. Indeed, in an unpleasant twist, two cases of this variant were also yesterday reported to the UK. The outbreak caused by these new variants will be much more challenging to control, both within these countries and beyond.

(I hadn’t intended blogging again before the holiday, but these new data are sufficiently concerning I thought readers would want some of this background!)

Some comments on terms!

  1. Mutation – a change in one of the genes of the virus as it multiplies
  2. Variant – as a consequence of one or more mutations, a different version of the virus appears with slightly different genes from the initial version
  3. Strain – often refers to when a particular variant becomes an important cause of some or all of the cases in a particular outbreak

Thus the new UK strain has some 23 separate mutations and this variant has become the dominant strain causing infection in much of this country

What do we know about the new South African strain?

  • South Africa has seen a marked  increase recently in the number of new cases of Covid-19 
  • Around 90% of new cases in that country are due to a new strain, based on a number of mutations
  • As well as the 2 UK cases, the South African variant has now been found in Australia and Switzerland
  • No doubt as other countries undertake the necessary complex genetic analysis this strain will also be identified in many other countries

Concern in younger people

  • At the beginning of the epidemic less than 0.5% of cases in South Africa were in people under 30 (which may be related to who was being tested)
  • In some South African provinces the peak age is now in the 15-19 year age group
  • As with the new UK strain, the new South African one multiplies much more quickly than the previous common strains
  • The problem is that those infected with these strains then produce much larger amounts of virus.
  • Thus, the concern is young people infected with these strains have higher levels of virus than they did with the previous strain: the latter of which only rarely led to a severe illness.
  • The greater viral load with the new strain could make them sicker
  • Indeed, there are a few unconfirmed reports from South Africa, of young people with no pre-existing illnesses who became seriously ill with this strain
  • It is too early to know how big these numbers are

How close are the South African and UK strains?

  • Both strains are quite different variants
  • Both however contain a number of mutations in the spike protein region, thought to be responsible for the increase in transmission
  • Thus both the South African and the new UK strain carry the same ‘N501Y’ mutation in the spike protein
  • It is likely that that these two variants have arisen spontaneously in different countries, but by chance both of them are particularly highly transmissible
  • We may see other countries reporting on other highly transmissible strains

For the UK variant, is there any new analysis of its impact?

  • In my post last week I thought it was highly likely that the UK strain would result in an increase in R
  • The graph below, from an epidemiology modelling unit in Oxford shows that the transmission rate, R, is now well over 1.0
  • It is fairly definite that the increase in R is because of the UK’s new strain (now called B117 or VUI-202012/0)
  • What is interesting is that South Africa and the UK are two countries which, despite all the current lockdowns and mitigations, have two of the highest estimated R values in the world
    • UK R=1.26
    • South Africa R=1.33
  • As a comparison the estimates for other high prevalence countries are lower
    • USA R= 1.03
    • France R = 1.06
    • Italy R = 0.88

Conclusions about these new strains remain the same

  • New strains occurring during a pandemic are not unusual
  • The fact that these strains are more likely to transmit infection does not mean that the infection is more serious, but this will need to be monitored
  • Most experts also still expect the new vaccines to be effective, as the antibodies generated by these vaccines should still ‘work’ against even the mutant forms of the spike protein
  • As I mentioned last week, if necessary new vaccines can be made very easily against a new strain
  • Indeed, one sensible suggestion this week was that even if the vaccines turn out to be less effective against any new strain, that wouldn’t necessarily be a big problem
  • Given what is known about the effectiveness and safety of existing vaccines, it may be possible for regulators to approve slightly modified new vaccines without the need for additional lengthy trials

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Covid-19 Immunity transmission Vaccines

What percent of the population needs to be vaccinated to end the pandemic?

As I mentioned in my posts this week, a vaccine protects us in two ways: (i) direct benefit from our our vaccination and (ii) by herd immunity – the vaccine protecting enough of the population to put an end to the virus spreading between people. There is a need for any CoVid-19 vaccine programme to produce herd immunity as the vaccine may not work in everyone and its effects may not last. In this post I consider, given our current knowledge about the possible vaccines and the behaviour of the virus, just how easy it will be to induce herd immunity.

(A quick note to say that I have tried to make the answer to a complex question easy to follow, especially for people who are not experts in maths!  Feel free to go straight to the conclusions at the end!  You might find it easier to read this on an iPad or laptop rather than a mobile to take it all in – but do feedback whether it is too complicated).

A quick refresher on herd immunity!

  • During an epidemic we can divide the population into 3 groups
    • those who are infected (the red figures below)
    • those who are susceptible – ie have no immunity to the infection (the light blue figures below)
    • those who are  immune-ie are protected against infection and therefore cannot pass it on to others (the green figures below)
    • (Individuals can be immune either because of natural infection or because of vaccination)
  • In the picture below, when there has been no vaccination programme, the larger red figure can spread the infection to lots of the susceptible people
  • Now, following a vaccination programme, together with people who have become immune naturally, the situation is as in the picture below
  • The infected person has far fewer people that they can pass on the infection to. More importantly, the people who are still susceptible are less likely to come in contact with an infected person.  In this picture the large red figure can only infect one other person, whilst the light blue figures are surrounded by people who are immune
  • When transmission of infection effectively stops, we say that there is a state of  herd immunity.
  • As shown in the pictures, we don’t need for everyone to be immune to bring about herd immunity 
  • The proportion who need to be immune varies between viruses.  The more infectious a virus, the higher the proportion needs to be  

What are the key factors that will determine how many people need to be vaccinated to achieve herd immunity with Covid-19?

  • A paper in the Lancet* on November 4th showed that is possible to calculate the answer to this question
  • The calculations need to consider the following factors:
    • What is the rate of transmission? 
    • The short term efficacy of the vaccine
    • How long the vaccine protection will last
  • These are considered in turn below


What is the rate of transmission?

  • This is the ‘R’ we’ve been hearing so much about
  • We all know that if ‘R’ is below 1.0, then the infection will die out (and theoretically no vaccination is needed)
  • We can only achieve an ‘R’ of that level with very strict social distancing and other mitigation strategies (eg face masks)
  • Without any mitigation strategies, the natural ‘R’ for Covid-19 is around 2.5-3.5 (each infected person, on average, infects between 2.5 and 3.5 other people)
  • In my calculations, I have considered 3 possible scenarios with a vaccination programme
    • We continue to adopt mitigation measures such as face masks and social distancing, accepting that the ‘R’ will fall to say 1.2, but won’t get below the magic 1.0
    • Once the vaccination programme starts, those who have been vaccinated then go back to normal life, ie the transmission rate is 2.5
    • As above, but a more pessimistic R of 3.5
  • This is what the calculations show:
  • To explain this graph, the blue bars show the percent of people who need to be vaccinated to achieve herd immunity for different values of R
  • If R is 1, as explained above, we don’t need a vaccination programme as the infection will disappear in time
  • If R remains as 1.2, then only around 16% of the population will need to be vaccinated to achieve herd immunity, but that means staying in some kind of lockdown until that has been achieved
  • If we go back to normal life and R is as high as 3.5 then we would need to vaccinate around 70% of the population to achieve herd immunity (shown approximately by the white arrows)

The short term efficacy of the vaccine

  • The calculations above assume the vaccine is 100% effective 
  • The Pfizer vaccine data suggested 90% efficacy – that might be optimistic and may not apply to all sub-groups, e.g. those who are elderly
  • Obviously the lower the efficacy, the lower the proportion who are vaccinated who are actually immune 
  • We also do not know what the efficacy of other vaccines might be, so I have assumed that the range will be from 60% to 100%
  • I have recalculated the figures from the graph above to allow for differences in the efficacy rate
  • This is what I found:
  • Let me help you to follow this graph*
  • The orange bars could represent Pfizer’s vaccine, with its reported 90% efficacy
  • The yellow bars could represent another company’s vaccine which may report 70% efficacy
  • Thus, if we remain in some kind of lockdown, ie with a ‘R’ of around 1.2, then to achieve herd immunity we would need to vaccinate 18.5% with the Pfizer vaccine and 22.8% with the new vaccine (red arrows)
  • If we resume normal activities and accept an ‘R’ of 3. 5, then we would need to vaccinate 79% with the Pfizer vaccine and 98% with the new vaccine (blue arrows)
  • Comment: it is highly unlikely that we could achieve anything like a 98% coverage 

*It’s a bit confusing as there are two percentages here. The figures above the coloured squares show the percentage efficacy of the vaccine. The numbers on the vertical axis of the graph show the percentage of people that need to be vaccinated

How long the vaccine protection will last?

  • This will also prove to be a challenge
  • We don’t know how long the immunity reported in the initial findings of the Pfizer trial, or with any of the vaccines, will last for
  • This is important as it will influence over how short a time the vaccination programme needs to be delivered. This will then impact on how long herd immunity will last for
  • If herd immunity begins to be lost, then booster immunization programmes will be needed
  • The Lancet paper did some fairly complex calculations and from their figures I have produced the following graph based on an assumption of
    • A vaccine which is 80% effective
    • An R value of 2.5
  • What this graph shows is that if a vaccine is 90% effective then herd immunity will last for around 17 months.  If it is 97.5% effective it will last for over 2 years.  If it is only 75% effective it will last just 10 months
  • Whenever that time point is reached then, as stated above, if the infection is still around a booster may be needed
  • None of these calculations have considered the fact that over time a new vaccine may be required to cope with a changing strain of the virus
  • However, the vaccine developers have shown they ae very nimble and should be able to adapt production of any new vaccine to cope 


  • Whether or not the vaccine is effective for any of us as individuals: for our continued protection and for society to return to normal, we need a vaccination programme to deliver herd immunity
  • The chances of herd immunity are obviously increased the more effective the vaccine. Vaccines with lower than the current reported success for the Pfizer vaccine can achieve herd immunity – but these would need very high take up rates of vaccination
  • Herd immunity will be easier to achieve if coupled with a continued stringency in adherence to mitigation actions such as mask wearing and social distancing – although this is something of a balancing act, as the aim of a successful vaccination programme is to return to normality  

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Covid-19 Testing transmission

Mass screening: is this the answer?

In the news again this week is the message that new approaches to testing for Covid-19, particularly using self-administered saliva tests, with just a 15 minute wait for a result, could be the answer to the pandemic.  Indeed, there are many examples across the world where everyone in a region or organisation (such as a workplace or university) has been so tested.  In a previous post on this blog, I mentioned that such a saliva test was just ‘around the corner’.  There are 7 important questions that now need to be addressed when considering the value from such a strategy:

  1. Tests for screening or diagnosis? 
  2. What is screening?
  3. Do screening tests for Covid-19 give the right answer?
  4. What are the causes and consequences of false negative tests?
  5. What are the causes and consequences of false positive tests? 
  6. Do we know what the false negative and false positive rates are for the various Covid-19 tests?
  7. Are there any other aspects of the test to consider?
  1. Tests for screening or diagnosis?
  • Early in the pandemic, tests were developed based on mouth and nose swabs to determine if individuals with typical symptoms of Covid-19 actually had the virus
  • The test was therefore used to diagnose an individual as ‘a case’
  • The positive rate of tests in those with symptoms has varied depending on what symptoms lead to the test and the prevalence of other diseases such as seasonal flu at the time of the test
  • In the early stages of the pandemic, when tests were not widely available, proving the diagnosis in people who were mildly unwell was considered unnecessary as it would not change the advice (stay at home etc)
  • Even though there was still no effective treatment for mild cases, the need for a diagnostic test later became more relevant in a negative sense: ie showing someone was negative allowed that person to resume normal activities
  • Over time it has become apparent from testing random samples of the population that a significant proportion of ‘cases’ of Covid-19 do not have symptoms at the time of a test and might never develop them
Proportion of people with symptoms in those who tested positive
  • In these data from the UK national survey, currently only about a third of those who tested positive had symptoms.  Indeed the prevalence was much lower in the summer, the symptomatic rate was under 20%
  • Having asymptomatic infection may not be a problem for the individual but it is for greater society, given that such individuals can pass the infection on to others
  • Thus, the question is raised as to the value of Covid-19 testing in  asymptomatic people for the purposes of screening

2. What is screening?

  • In a strict epidemiological sense, screening is defined as the application of a test to identify cases that had not yet, or might never, become apparent
  • Screening of individuals is of value only if there is benefit of earlier detection
  • For example, it is no use introducing a cancer screening programme if the people who are detected gain no benefit from the earlier detection 
  • Thus, screening of individuals for a disease is only of value if there is something that can be done to change the outcome, for example earlier treatment.
  • By the same token screening of populations is only of benefit if that leads to an intervention that would reduce the population impact of the disease
  • Thus, screening asymptomatic people in the general population for Covid-19 is only useful if there is something that would be done that would
    • Reduce the spread of the infection
    • Allow sections of society to function normally 
  • The testing on its own is useless unless there are effective measures that can achieve those goals
  • For the rest of this post, I will assume that the test results will lead to successful actions in terms of contact tracing, quarantine etc that will bring about the desired reduction in the rates of infection in the target populations 
  • The rest of this post thus considers how important that the screening test will give the right answer enough of the time

3. Do screening tests for Covid_19 give the right answer?

  • Although this appears a simple question, it is really challenging to answer but could be crucial in determining whether there is any benefit from mass screening
  • No test for Covid-19 is completely accurate all the time!
  • There are several reasons why you may get the wrong result
  • Looking at the diagram above we can see that there after testing, people will fall into one of four groups:
    • Have the infection and the test was positive (the green figure)
    • Have the infection but the test was negative (the black figure)
    • Don’t have the infection but the test was positive (the red figure)
    • Don’t have the infection and the test was negative (the blue figure)

At the end of the blog for those who are interested I show how we calculate the rates of false negatives and false positives

4. What are the causes and consequences of false negative tests?

  • These are some of the reasons why there might be false negatives from a Covid test:
    • The person actually was infected but was too early to show up in any test
    • The person was infected and was not producing enough material to show up in that test
    • The person was actually infected but there was a problem with the test itself:
      •  A swab test did not collect samples from the correct areas of the mouth and nose
      • A saliva test sample was not collected sufficiently well  
      • Something went wrong with the  storage and processing
      • The test in the lab gave a false reading
      • The threshold for saying a test was positive was too high (this will be discussed further below)
  • There are a number of consequences of the false negative rate in terms of screening, both for the individual and society

5. What are the causes and consequences of false positive tests?

  • There are some reasons why there might be false positive from a Covid test:
    • Contamination of a swab sample by Covid-19 in the lab, on the glove of the handler etc
    • Contamination by Covid-19 of the chemicals and other materials used to do the test
    • Test is positive but due to the presence of a different but harmless corona virus
    • Test is positive but due to the presence of genetic material that was not from Covid-19 
  • There are a number of consequences of the false positive rate in terms of screening both for the individual and society

6. Do we know what the false negative and false positive rates are for the various Covid-19 tests?

  • This is a challenging question as to know when a test is giving a wrong result; it implies that there is a way we can find out what the true result was, other than from the test!
  • In the lab,  a new test is analysed on samples from patients with known virus loads from more stringent tests and who may have obvious clinical infection with Covid-19 – but that might not be so useful in the real world as we want a new test to pick up infections in people with smaller viral loads who may, for example, be asymptomatic
  • Even in the lab with the ideal conditions, both false positive and false negative rates for Covid-19 tests are around 5%
  • We really do not know what these rates are in community -wide settings
  • A paper in the Lancet at the end of September suggested a false negative rate could be as high as 30%
  • Researchers and test developers have compared new saliva tests with  swab tests as in the example below.  But that study can only give the rates of positives and negatives relative to each other
  • Some research has estimated the rate of false negatives by doing repeat tests after a few days, to see if people are positive the second time
  • This may be sensible but of course the person’s infection status might have actually changed in that time
  • So there can only be a best guess as to what are the false negative and false positive rates for any new test

7. Are there any other aspects of the test to consider?

  • One final comment is that in all the lab tests the result is not a clear cut yes/no and a decision is made as to what reading would be considered as positive
  • If that threshold is made much less stringent -‘raise the bar to being positive’, then this would reduce the number of false negatives – but of course raise the number of false positives
  • If that threshold is made much more stringent – ‘raise the bar to being positive’ then this would increase the number of false negatives but of course reduce the number of false positives
  • It is a difficult decision where to ‘put the bar’ and this will depend on the relative harmful consequences of a being a false positive or a false negative


  • Any speedier test that can be applied to populations and that can lead to effective action to reduce transmission of infection is of course welcome
  • Testing alone which does not change behaviour or policy is a waste of time
  • My aim in this post was to emphasise that we should accept there is no perfect test but we do need to understand the imperfections of the tests that are coming into use

Appendix: Calculation of false negative and false positive rates

  • (In the diagrams below I have used the same colouring as in the image above of the different results)
  • For any test we can calculate the percentage of all results that should be positive but were wrongly called negative: this is called the False Negative rate* and is calculated as below (multiplied by 100 to give a percentage):
*Epidemiologists prefer to use the ‘converse’ of the false negative rate by calculating 100%-false negative rate – we call that “sensitivity”
  • This seems to be counterintuitive, as you would think the false negative rate should be the proportion of all negative tests that are false.
  • But the aim is to make sure that as many as possible of the true cases are identified
  • Similarly for any test we can calculate the proportion of all tests that should be  negative but were wrongly called positive: this is called the False Positive rate* and is calculated as below (multiplied by 100 to give  percentage)
*Epidemiologists prefer to use the ‘converse’ of the false positive rate by calculating 100%-false positive rate -we call that “specificity”
  • That also seems to be counterintuitive, as you would think the false positive rate should be the proportion of all positive tests that are false.
  • But the aim is to make sure that as many as possible of those who are not cases cases are also correctly identified

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Covid-19 transmission

Is lockdown the only option to combat the recent surge ?

As a second wave of infections hits many European countries, the challenge is whether to persist with stringent local measures or go into full lockdown.  From an epidemiological perspective the question is raised: assuming full adherence by individuals and organisations to rules on social distancing, masks etc, should that not be enough to suppress the pandemic?


  • The key facts are well known I am sure!
  • Covid-19  is transmitted from person to person: achieve complete isolation of affected individuals  from other human contact and the pandemic can come to an end
  • Given the relative contribution of asymptomatic spread, and the total failure of Western democracies to have an effective track and trace system, complete isolation has to apply to the whole population
  • The economic and social consequences of complete population lockdown are enormous 
  • Hence the attempt to achieve the same effect by individual and organisational behaviour change with social distancing, masks and other mitigation measures*
  • What does ‘the science’ tell us about how likely it is that such measures would be sufficient ?

*By organisational measures  I refer to those undertaken by schools, work places, shops, public transport etc 

If proof were needed about human to human spread….

  • Rigid enforcement of restricting human to human contact led to the end of the pandemic in Wuhan (Hubei Province)
  • More recently the lockdown in Melbourne, Australia, has been similarly remarkably successful

Do we know that perfect adherence to mitigation measures is not equally effective?

  • It is a challenge to show either theoretically or in practice that social distancing, wearing of facemasks and hand washing, in combination, are sufficient to reduce either individual risk to zero or the population level to trivial numbers
  • I could suggest the perfect epidemiological study:
    • Select (say) 20 small relatively self-contained towns
    • Undertake baseline surveys to identify at the start  those who have, or had evidence of infection by being positive on a swab or antibody test
    • Randomly allocate these towns  to two groups: (i) lockdown versus (ii) optimal individual and organisational adherence to mitigation actions 
    • In the non-lockdown group, the research would have to record the actual levels of adherence 
    • Follow up for (say) 6 weeks
    • Compare the rates of positive swabs/antibody tests and clinical cases
  • Actually, given the funds that have been used to support the current large scale surveys, and the potential that my suggested approach could prove crucial in understanding the benefits of lockdown, such a study would not be impossible to do or fund! 

Any indirect evidence that the mitigation measures in combination may be equally effective as lockdown ?

  • The only indirect evidence comes from so called ‘natural experiments’ where the equivalent of the perfect adherence might be assumed
  • The best example I believe is Singapore
  • Singapore has had around 60,000 cases but with a very high proportion imported from overseas (explaining the spike in summer) 
  • Well known as a law abiding country, Singapore made mask wearing mandatory in April and issued fines for not keeping to safe distancing
  • The data on adherence to mask wearing in public places is compelling, 95% of the population have been reported as wearing masks

Is it that simple?

  • Sadly not.  The fact that there is a high adherence to these measures in a country with a low and declining rate of infection does not prove cause and effect
  • Asian countries in general are doing better than European and American (North and South) in terms of transmission which may be related to background immunity and mutations in the virus
  • Singapore is also a very affluent country and overcrowding and poverty in regions with western countries are key contributors to transmission rate
  • Given therefore we don’t have a scientific answer to the key question about the value of almost perfect adherence to the mitigation measures, we can look at the scientific evidence about the success of masks and social distancing

How successful could mask wearing be?

  • I discussed the data on cloth facemasks in a previous blogpost at the beginning of September:( )
  • I concluded then that “Cloth masks are not the magic bullet to stop an infected person spreading the person, or protect an uninfected wearer from being at risk from others”  
  • There have now been several epidemiological studies, both in Covid-19 and  in other corona virus pandemics, comparing the success of facemasks to prevent transmission.  The reduction in rates with facemasks ranged in these studies from 6% to 80%
  • Without going into details, the reason for this enormous disparity lies in the ways the different studies were undertaken
  • (OK I am defending my epidemiological brethren here!) but these studies are not easy and all kinds of biases can creep in
  • One authoritative review from Norway worked on the premise that facemasks probably would reduce the risk by 40%

How successful can social distancing be?

  • The challenge in understanding what difference social distancing measures make is based on what we believe to be the relative contributions of droplet and  aerosol transmission
  • I have discussed this before but worth repeating that initially it was thought that transmission was by large droplets, coughed, sneezed or even just breathed out
  • The droplets would fall to the ground fairly quickly, hence the 2 metre rule
  • As is now well recognised aerosol (smaller droplet) transmission can occur at distances of up to 8 metres
  • Note my use of ‘can’: we just do not know what proportion of cases are being transmitted at this distance
  • Again – as I am sure is also well known – ventilation, duration of exposure, how forceful the exposure (eg cough/shout/singing) will all affect the risk


I wanted in this post to provide an understanding of how far individual public and organisational adherence to all the current advice and adoption of the relevant measures to mitigate against transmission could achieve the same success as complete lockdown.  The media have all focused on ‘bad behaviour’ but is this the reason for the current surge in Europe?

I guess if this was easily answered then we would have been advised accordingly. I will come ‘off the fence’ though and conclude that even (i) excellent adherence to the face masks that are reasonable for the public to wear and (ii) keeping within a 2 metre distance from non-household members are not sufficient to guarantee (I am guessing at this figure) more than an 80% reduction in personal risk.  When the number of cases in the population is rising then these measures are probably therefore not sufficient to bring the transmission down sufficiently (ie bring down ‘R’ below 1) . 

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Covid-19 Risk transmission

Covid-19 can survive on mobile phones for 28 days: is it time to reappraise risk from surfaces?

Widely reported in the media on Monday this past week were the results from an Australian study which showed that in the laboratory the Covid-19 virus can survive for 28 days on a wide range of surfaces from bank notes to mobile phone screens, in an amount considered sufficient to cause infection.  How worried should we be and what is the best estimate about the role of surfaces in increasing infection risk?

What was known about the risk of surfaces before Covid-19?

  • For over 40 years it has been known that the flu virus can survive on non-porous surfaces such as stainless steel for perhaps 24-48 hours Many other viruses have since been shown to  survive on a variety of surfaces and be a potential source of the transmission of infection
  • More relevant is that for most corona viruses that have been studied, including the ones responsible for the SARS pandemic in 2002-04 and the MERS epidemic in 2012, it has been shown in the laboratory that they could survive for more than a few hours on a variety of surfaces
  • The early advice about surfaces with Covid-19 was based on that laboratory evidence from other viruses 
  • Thus, it is reasonable to ask specifically about Covid-19: “can it survive on surfaces and, if so:
    • which surfaces?
    • for how long?”

What had we learnt in the first 6 months?

  • Given the findings from other corona viruses, from the start of the current pandemic there was a considerable amount of research on the survival of Covid-19  on different surfaces. 
  • The studies are tricky undertake and there are  lots of different ways to do such research 
  • Perhaps not surprisingly, the results varied but there was  broad conclusion that this virus can survive on a wide range of surfaces for a reasonable amount of time after say a sufferer has coughed 
  • In general, the virus survived much shorter times on porous surfaces such as clothing than shiny surfaces
  • Somewhat surprisingly the virus also survived for a few days on non-shiny surfaces such as wood, paper and cardboard
  • Given results such as these, many public health authorities recommended stringent approaches to cleaning
  • Businesses and members of the public then to varying extents adopted mitigation practices which ranged from stringent wiping of surfaces in health care settings to some households removing the front pages of a newspaper prior to reading. 

Are there limitations to the conclusions that can be drawn from such studies?

  • Absolutely! 
  • The studies have to be undertaken in laboratory conditions, but these do not necessarily replicate daily  life
  • Temperature, humidity, UV light all can alter virus survival 
  • The nature of the sample of virus spread on these surfaces was not the same that would be spread from an infected individual, for example from a patient coughing
  • The fact that the virus could be cultured did not necessarily mean there was sufficient virus on the surface to be harmful to health 
  • Furthermore, taking a sample ‘scrape’ from the surface is very different for example from what a person can pick up by touching the surface.
  • Thus, my conclusion was that the virus can survive on surfaces but that was not proof that in the form it survives it is capable to causing infection

What does this new Australian study add?

  • I thought the study was impressive!
  • The research ‘infected’ the surfaces with the concentration and form of the virus that would be typical of (say) that infected patient cough
  • They allowed the liquid preparation of the virus to dry
  • They studied a large range of materials (see table below).  The items listed are those directly studied  or referred to as relevant  in the research report 
Polymer bank note
Paper bank note
100% pure cotton
Brushed stainless steel
  • They studied the virus survival at ‘real life’ humidity and temperatures
  • They measured how much of the virus they could grow from 1-28 days
  • This is what they found:
    • On some surfaces such as cotton there was an immediate absorption of virus
    • Even on cotton virus could still be grown after 14 days
    • On all the other surfaces virus could still be grown 28 days later
  • The rate that the virus disappeared was calculated by how many days would be needed to lose (i) 50% and (ii)90% of the dose that was present after the initial application. 
  • The results (which I have drawn below) show that for all the materials half of the virus that had been applied was still present at 2 days 
  • Further although 90% had been lost by a week, that meant there was still 10% of the original amount of virus left, which was sufficient to cause infection
  • The authors argued, based on their findings, that common objects such as mobile telephones, ATM machines and even modern and old bank notes could still be a risk for transmission after gaps of several days
  • One major criticism of the study is that all objects were kept in the dark, given that UV light exposure would have reduced the amount of the virus (although from a research perspective it made sense to control that aspect)

Should these findings change behaviour?

  • Studies such as this are undertaken by virologists (indeed the results appeared in a publication with the not very surprising title  Virology Journal!)
  • Virologists in research of this type are addressing questions on “can the virus survive on surfaces?” and even (as in this article) “following a typical exposure to an infected person, can the virus survive for so many days that it could be an important source of transmission?”
  • An epidemiologist asks as different set of questions: we know that surfaces can be a source of transmission for these periods but how likely is this to happen in the ‘real world’?
  • As examples there are several potential health hazards that can happen following common health related activities (with no prior history) 
    • Haemorrhage after tooth extraction
    • Severe allergic reaction after paracetamol (USA acetaminophen)
    • Similar reactions after certain foods
  • And in the non-health sector:
    • Tyre blow-out whilst travelling at speed
    • Domestic kitchen appliance catching fire
  • These are all rare and we take the view that the risks are acceptably low and we don’t change our behaviour

Do we know how likely is it that transmission can occur from surfaces?

  • The short answer is no!
  • It is very challenging, outside a local outbreak with a single source, to know what was the route of transmission for any individual Covid-19 infected patient
  • There are anecdotal  reports in the media of a person who contracted the infection after touching a door handle or whatever, but the existence of one case does not allow us to calculate the numerical level of risk
  • Logically it is impossible to test all the surfaces that such an individual may have been in contact with over the previous 10 days
  • It was ‘easy’ with Novichok and the Salisbury poisoning, but that entailed closing down a whole town and a military style exercise!
Source Daily Mail
  • For sure transmission in hospitals in the early phase of the pandemic could be traced to high concentrations of the virus on surfaces, exhaled from patients and probably staff
  • Outside health care settings the likelihood of transmission from surfaces, I would conclude, is lower, probably substantially lower, than most other risks I would accept – including travel on a flight
  • The good news is that infection cannot occur by absorption through the skin, so if I am worried from any surface, I just wash my hands or use hand gel and I’m happy reading  the front page of news

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Anti-viral drugs Immunity Outcome transmission

Genetic mutations in the virus and in humans: Do they offer a way out?

As the pandemic progresses the virus mutates. Mutations in us result in differences in how our immune system fights the infection. In this post I review how recent research on the impacts of these mutations could change the way we control and treat the disease – I found the results really interesting and even hopeful!

What is a mutation?

  • The different genes in every organism, from virus to man, consist of chains of different sequences of 4 building blocks, called nucleotides
  • The 4 – referred to as ‘A’, ‘C’ ‘G’ and ‘T’- can form very long sequences with up to 2 million in  any one gene 
  • You can think of genes as very long necklaces made up of Lego blocks in the 4 main colours
  • The exact sequence of the nucleotides in each individual gene does vary:
    • no two humans have identical sequences in any of our genes: there are many points of difference
    • even though a virus has far fewer and much shorter genes, there can be many differences in the genes (these are then referred to as different ‘strains’)
  • Such differences in the sequence of nucleotides are referred to as mutations. Mutations continually occur in all species
  • In some mutations, there is a substitution of one nucleotide for another
    • In the example below a  ‘red’ T was substituted by a ‘blue’ ‘C’
  • In other mutations, sequences can be missing from one gene to another: this is called deletion
  • In yet other mutations, extra sequences can be added, often copies of short sequences, this is called duplication

How common are mutations in the Covid-19 virus?

  • This virus just has one strand of the genetic material RNA
  • This strand though has 30,000 nucleotides
  • Already scientists have identified 13,000 separate mutations 
  • Although this might seem very high, the level of mutations is 6 times lower than in the influenza virus 

What are the potential consequences of Covid-19 mutations?

  • Most mutations will have no consequences in terms of the infection
  • Even so, identifying such individual mutations can be very helpful in tracking the spread of infection between people
  • Of those that do make a difference, some mutations can affect the transmissibility of the virus (ie how easily it spreads) and others the severity of the infection
  • Early on in the pandemic, one mutation (OK it’s called D614G) was thought to be responsible for altering the spike protein on the surface of the virus that causes it to stick to human cells (and is the bit that most vaccines are directed against)
  • This mutation is now present in 80% of Covid-19 virus samples studied
  • The Chilean outbreak is thought to be due to further mutations in the spike protein gene making it even more sticky to human cells
  • There are other mutations, associated with a deletion of some of the virus’s genetic sequence, that make it less infectious
  • A study from Singapore in the Lancet in August suggested that that country’s lower death rate may be due to a deletion which produces less severe disease

The results were impressive-Singapore has lowest fatality rate in the world:

  • What is interesting is that mutations in the virus are likely to occur in response to how humans combat the infection.  There could be both beneficial and harmful consequences:
    • Beneficial: Consider for example there are two mutations: the one that causes very serious infections and the other that causes mild infection.  People carrying the latter are more likely to spread the infection and thus that version of the virus will become more prevalent.
    • Harmful: There are several examples of  viruses mutating to try and overcome our body’s immune response
  • We should not forget that significant (but what significant means is difficult to be precise) mutations in the virus could affect the success or otherwise of any new vaccine
  • Although at the moment most vaccine researchers believe their vaccines should be resilient against the types of mutation currently recognised  

How common are mutations in humans?

  • The answer of course is immensely common, each one of us probably has millions of mutations
  • Many of these mutations affect how our immune systems fight infections
  • The interesting question is therefore to ask if mutations in these immune genes explain why some people have more serious infections than others
  • The short answer from a vast number of research studies is yes:  differences in these genes between people are linked to various dimensions of the outcome, including mortality
  • An example of a recent study is the one referred to below from the USA that showed that some people have a mutation that reduces the body’s production of interferon, which is our natural anti-viral drug. 
  • Indeed, other studies have shown that an effective interferon response is necessary to fight this virus
  • To be honest, at first sight knowing that people who have more severe disease than others have a genetic basis for this might seem not that helpful, as we cannot change our genes
  • But what this kind of research can do is to help suggest new drug possibilities.  Just yesterday came a press release from the biotech company ILC Therapeutics for an inhaled version of interferon that might be a useful new drug
  • This follow from another small UK study in July of the use of another type of  interferon which showed an 80% reduction in the likelihood of severe complications
  • These are early days and more evidence is needed


  • There has been a massive amount of research into the genes that:
    • allow Covid-19 to invade and cause such mayhem
    • impact on our immune response 
  • None of the research findings individually will be a game changer, there is a very complex jigsaw of how all the pieces fit together 
  • A better understanding of the mutations in the virus can help in tracking the infection both over time and between regions and local outbreaks
  • More importantly these insights from genetic mutations can help focus attention on novel approaches to treating the infection    

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Risk transmission

Lockdown for the many or the few: when experts disagree!

Yesterday two groups of scientists sent conflicting open letters to those leading the national responses to the increasing number of cases in the 4 countries of the UK.  The issues raised in these letters are relevant to how other countries/regions in Europe, North America and beyond manage the outbreak. When scientists disagree, the public loses confidence in their opinion and governments are left wondering whose advice  to follow.

In this post I dissect the points raised and attempt to reconcile the different conclusions as to what  countries should do.

The 2 letters:

  • Both were led by epidemiology colleagues from Oxford University who work in the same department of Primary Care
  • The first arguing for a whole population approach was led by Professor Trish Greenhalgh which said:
    • Restrictions in terms of social distancing/lockdown should apply to everyone because:
    • Including everyone is the best way to reduce the incidence of infection 
    • There is too much uncertainty to have a targeted approach 
  • The second arguing for a targeted approach was led by Professor Carl Heneghan which said:
    • Restrictions in terms of social distancing/lockdown should apply to those who are the most vulnerable, such as the elderly because:
    • Limiting actions to contain the virus  to those for whom there is clear evidence that there is a measurable benefit 
    • In this way we minimise unnecessary harmful impacts on the economy, mental health and education 

Critique of the Greenhalgh letter 

Point made in the letterMy response
Everyone in the  population is at some risk:  even young people can have complications and long term health problemsIt is a ‘numbers game’ and the low rate of serious complications (for example) in young people should be taken into consideration
Cannot easily have different policies for vulnerable and non-vulnerable.  Example -grandparents often involved in child careThe data from several countries would suggest that the recent increasing incidence has been greater in younger people.  This suggests that shielding works
The letter argued against the targeted policy – that if a sufficient proportion of low risk people get infected – this  would lead to a degree of herd immunity.  This would then protect the (say) older people who have been shielding.   This letter argued that that such policy is flawed – based on the assumption that people cannot get re-infected, for which there is no evidence  The rates of a second infection thus far are very very low so it is plausible that we could achieve herd immunity whilst protecting the vulnerable.  But I agree that the achievement of herd immunity is unlikely to come from natural infection rather than vaccine
No evidence from other countries that having targeting policies is more effective than general restrictionsThis is true, but  it is probably unrealistic to expect to obtain definitive evidence.  The authors also write (next point) that we should not necessarily require such evidence 
Relying on high quality research on a single aspect to make decisions in a situation as complex as managing Covid-19 is dangerous and need to take a more balanced viewI don’t disagree

Critique of the Heneghan letter 

Point made in the letterMy response 
Current UK government policy does not have a clearly stated overall strategy, so impossible to judge if any policy is successful I agree, but not sure how we would reach agreement on what that policy should be
Having a policy just focusing on reducing deaths is too restrictedI am not sure that a policy of just reducing deaths has been stated. One problem is that deaths are the most easily measured and so the media in these situations focus on comparing death rates both within and between countries 
Mortality is concentrated in older people and those with pre-existing health problems. Therefore, this is the group that should be targeted for interventionsThe authors are arguing both ways on this, they argue above against focusing on those at increased risk of dying if they get Covid-19. 
Blanket lockdown policies on the whole population which are ‘unnecessary’ can as a side effect result in a reduced access to health care for non-Covid-19 diseases.  This can  lead, for example, to increased numbers with untreated heart disease or cancerI think this is a significant concern and countries will need to provide robust data on the consequence of lockdown on health care for other diseases.  But this is not a ‘zero sum’ game; ie countries can have high levels of lockdown for the general population and invest heavily in maintaining health care more widely
A previously widely discusused point on the economic costs of lockdown and that, for example the unemployment and other consequences have their substantial health risksThis has to be true, or at the very least is a testable hypothesis.  The results from available national data will take several months to gather.  Further, these health consequences may take decades and not just months to become apparent 


  • No-one suggests this is easy and the trade-off between the harmful impacts of Covid-19 and of strict lockdown are well known I am sure to all readers of this blog
  • These two open letters also identify that despite the fact we are 9 months into the pandemic, there are still so many unknowns
  • Not surprisingly there is scope for disagreement on interpretation of the available evidence 
  • These letters do bring into focus what are the key issues 
  • If I were forced to judge which letter provides the stronger argument, it would be that the targeted approach is likely to  win out in terms of the sum total effects on human health in our populations 

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Covid-19 Masks transmission

Face masks 2: How effective are cloth face masks?

Face masks covering the nose and mouth are designed to act as a barrier to reduce viral spread. If people who are infected wear a face mask, is that enough to stop them infecting other people? In this post I review the evidence that the wearing of ‘normal’ cloth face masks is a good enough barrier to prevent passing infection to others.

To recap from my post last week:

In my first post on face masks last week, I posed the key question: “How much can the widespread  wearing of face masks reduce the spread of infection in the population?”  I argued that even if everyone wore face masks, the number of new cases in any area  would also depend on several other factors:

  1. The effectiveness of the chosen masks as a barrier to passing on the virus
  2. The safe and appropriate use of any face masks worn 
  3. The level of adherence to other mitigation measures such as social distancing, hand washing etc
  4. The background risk of infection in a local population. 

In this post I address the first question, on the success or otherwise of face masks as a barrier to the passage of virus

Who is protecting whom?

Source: Mario Tama/Getty
  • The research on the effectiveness of face masks as a barrier is based on studying how they stop the virus spreading from an infected person. 
  • It is very difficult (ethically and logistically) to do a study  to show that wearing a face mask is an effective barrier to receiving the virus from an infected person
  • It is a reasonable assumption that the barrier is equally effective in both directions  – although the receiver can also get infected via the eyes or passing droplets by hand to their nose or mouth

Surgical masks

  • Much of the research is on the effectiveness of surgical masks and their use in health care settings
  • Surgical masks have non-woven fabrics (no holes) and are disposable 
  • Several studies show that surgical masks are very effective at reducing the risk of infection
  • As an example, research  from Hong Kong published in May studied people all of whom had a positive swab test either for Covid-19 (or  for common cold or flu).
    • They then studied the proportion of those people who were breathing out viruses, both without and with a face mask.
    • They measured if there was shedding of the virus in both large droplets and small droplets (aerosols*)
    • Of those with CoVid (blue bars in graph below), who did not wear a face mask,  30%  shedded virus as droplets and 40% as aerosols.
    • With a face mask none of those with Covid-19 shed the virus in either form
    • Interestingly in this study, surgical masks were more effective as a barrier to Covid-19 than to the common cold or flu viruses

What about cloth masks?

  • Disposable surgical masks are not a practical proposition for use in the general  population
    • The number of masks needed would be enormous
    • The cost for individuals or organisations is excessive
    • They are not comfortable for wearing for long periods such as a school or working day
  • In SARS and in other pandemics, authorities had suggested that cloth masks are the only feasible alternative for the general population who are not at the same risk as say health care workers
  • Several previous research studies, from SARS 1 and other epidemics, have shown that surgical masks are far superior to cloth masks, but might cloth masks be good enough?

Challenges in assessing the value of cloth masks

  • For sure it is more difficult to block aerosol spread than droplet spread in woven cloth materials even if we can’t see the holes
  • Holding up a mask to the light and seeing if nothing shines through is helpful but this is not proof it would filter out the smallest droplets
  • It is also clear that the likelihood of transmission of virus even through a well-fitting cloth mask is increased by:
    • the amount of virus
    • how much speaking/shouting/coughing etc takes place
  • Thus, it is really difficult from the available research to be definitive about how effective cloth masks are as a barrier.

There are cloth masks and cloth masks!

  • There are several designs of cloth masks which make it difficult to draw conclusions about cloth masks in general 
  • Factors that vary include:
  • Number of layers
  • Shape: conical (see picture) or traditional folded
  • Whether there is an additional filtration layer
  • In one recent review comparing 15 different designs and materials of cloth masks, the effective filtration rate varied from 28-90%, though 80% is frequently achieved

What is my summary  about cloth masks?

  • No cloth mask stops 100% of droplets 
  • There are inevitable uncertainties in the precise protection afforded by any individual mask 
  • Depending on the construction of the mask, an 80% reduction might be achieved but that might vary with the size of the droplets
  • The more an infected person shouts, coughs etc then the harder it is for a cloth mask to be effective
  • The longer the mask is worn the less effective it might be – but regular washing helps
  • The effectiveness of masks obviously depends on how they are worn, and whether they let out droplets round the side


  • Cloth masks are not the magic bullet to stop an infected person spreading the person, or protect an uninfected wearer from being at risk from others.  
  • Widespread usage might still have a major role in lowering the overall rate of infection in the population.  The size of this effect will be explored in my next post

Interested in further reading:

  1. How the effectiveness of different cloth masks can be measured.
  2. Study of the effectiveness of surgical mask against Covid-19. 
  3. A comprehensive  review of all the studies on the effectiveness of face masks.
Covid-19 transmission

The surge in reported cases across Europe in August: What is going on?

European countries were the largest contributor of cases globally in March and April.  As authorities introduced the necessary lockdowns, social distancing, testing and contact tracing, there were marked falls in deaths and the numbers of cases. The debate then moved to avoiding a second wave. 

But in very few countries have cases completely  disappeared and more worryingly there has been a surge of cases in many countries over the past few weeks.  How substantive is this surge and what are the explanations?

What are the data?

  • I have selected a number of large European countries, some with previously high and others with low level of infections. 
  • I have used the available data to compare the number of reported cases in the latest  data (typically the second full week in August) with those in the equivalent week in July. 
  • I have calculated what is the relative size of any increase, as displayed in the results in the table below.  For example,  Denmark has nine times as many cases in the August, compared to the same week in July.
  • Large relative increases are seen both in countries which have been considered successful at containing the outbreak, such as Greece, Germany and Denmark, and in countries with harder to control outbreaks, such as Spain and France.  
  • (Before the UK becomes complacent, the absolute rate of currently reported cases is still twice that of Germany)

Are these increases real?

  • Despite seeming to validate a Trump comment, it is a truism that the more tests that are done, the more cases that will emerge
  • The number of people with positive tests considerably under-estimates the true number of actual cases
  • In broad terms though, the level of under-estimation is unlikely to have changed to such a degree over one month, given that all these countries had well established testing systems by July
  • There is a ‘multiplier’ phenomenon.  By this I mean the more cases that emerge in a place, the more people turn up for testing  – because of either ‘track and trace’ systems or greater public awareness.  But this would not explain why the past week has seen such an increase
  • And why should all these countries show an increase in August?
  • The media have correctly identified what are in fact common factors in these countries:
    • Relaxation of lockdown
    • Increased infection in younger people, who are socialising more
    • Localised epidemics in various factories and work places

At the beginning of the outbreak, I had thought it was possible that:

  • The virus might just disappear without explanation, as some argue was the case with  SARS Cov-1
  • The virus might just mutate to a less transmissible form
  • The virus transmission could be stopped by strict public health measures effectively preventing any future cases – with such measures achieving the necessary consent in a modern liberal democratic state

I now believe that:

Despite what we were told at the beginning of the epidemic, as shown in this figure from the Economist………

  • None of these will happen! 
  • Any reduction in the number of cases is temporary and once the lid is lifted,  and there is greater direct human to human contact, spikes will continue to occur
  • However successful any country has been in the past does not make them any more secure now
  • For me now, the term”second wave” is meaningless.  This current wave will be with us until we have a vaccine. 
  • We can change the shape of this curve but it will not come down to the level where the threat to public health has gone 

Covid-19 transmission

Is it safe to reopen schools in September?

Is the concern protecting childen or adults?

One of the most important decisions in managing the Covid-19 outbreak has been the decision whether, and by how much, to open schools.  The UK government has announced their intention to make this a priority in terms of lifting of lockdown measures.  In this post, I review the available data that could inform this decision and  include  the results of a computer simulation study published in last week’s Lancet.  I conclude a successful return to full education is possible but this would require a major shift in approach to the control of the pandemic.  

First the background epidemiology of Covid-19 in children

Much is well known, although there are limits to the available information as there are few really high-quality studies in children so far.  These are the key findings however:

Children have the same overall rate of infection as adults

  • In the UK, repeat testing of a random sample of 20,000 households showed that 0.3% of children aged 2-11 had a positive swab test in the period 26 April to 27 June, ie had current evidence of the virus.  This was very close to the percentage in all other age groups
  • In a study in July from Spain, around 4% of those aged 0-19 had a positive antibody test – a measure of ever having Covid-19 – which was not much lower than the 5% rate seen in adult groups. 

Children have the same risk of becoming infected if exposed

  • These rates of  infection in children and adults  are related to their likelihood of exposure to an infected person 
  • A separate question is thus whether if exposed to the same risk, eg a household contact, do the children have the same risk of being infected ? 
  • A study from Barcelona in June found the rate of infection in household contacts of an infected person was around 18%, and was the same in children as in adults.

Outbreaks in school and other settings do occur

  • There are many instances of major clusters of cases in childhood settings
  • One example was 153 cases in a school in Jerusalem
  • A second example was cases in an overnight summer camp in Georgia, USA when 260 out of 344 tested positive

Children have milder disease 

  • Only 5 children have died from Covid-19 in UK.  As a comparison there were 16 deaths in children in the influenza epidemic in 2017/18
  • Fewer than 80 children in the UK were sufficiently ill to require admission to an intensive care unit (by the end of July).  This is compared to 13380 adults 
  • In the Barcelona study mentioned above, almost all (99%) of the children  had either no symptoms or minor symptoms

Is being in contact with an infected  child less of a risk to an adult than being in contact with an infected adult?

  • This is an interesting question  and obviously relevant to schools opening.  
  • There is no definitive answer but some indirect data which is inconclusive 
    • A German study found that the concentration of virus in swab samples was the same in children as in adults 
    • The Barcelona study showed that children who are infected carry the virus for a shorter period than adults.  (Note: this might be due though to the milder disease in children)  

Where do we go from here?

  • The question around school opening clearly  is not: “Are children at increased risk?”.  They are at the same risk of getting infected as adults but for them the disease is likely to be trivial.
  • Thus, the societal question is: “What is the increased risk to vulnerable adults from being exposed to children who return to school?” 
  • This raises the interesting philosophical question as to how society values the lives and general well-being of children and adults
  • The Denmark model of effective physical distancing has been held up as a model for school reopening, whereas others have said even this is too risky.

What does the Lancet study this week argue?

  • An international group of researchers developed a computer model based on a series of assumptions given current social distancing behaviours.  
  • Opening schools full time could be done without risk of a major increase in the epidemic but only if
    • 75%  of all individuals who are symptomatic are tested
    • 68% of the contacts of those who are positive are traced
    • All contacts who test positive are isolated
  • This is a difficult ask

Is there an alternative strategy?

  • One way out of this is a step change in mass testing, possibly of all school children on a regular basis  – this is now being actively explored
  • We will need cheaper tests that are simpler to process  
  • Tests are already being developed  that could be done at scale for as little as $1 and give a result in 40 minutes
  • Indeed, there are tests that can be processed locally without being sent to a specialist laboratory

Go for sewage!

  • Infected individuals, including those without symptoms, may shed virus in their stools even when the swab test is negative
  • The National Wastewater Epidemiology Surveillance Programme is screening sewage with the aim of identifying local outbreaks earlier 
  • This system is currently being tested at  wastewater treatment plants.  If this technology could be applied  at the level of institutions such as schools, that would  be a rapid and efficient way of identifying if there was a cluster of cases in that school 
  • If this was the case,  that would be a useful trigger for all the pupils to be tested