Intro: This is the next block of a series of posts that I am putting up on facebook (originally every day, now less frequent), and have been asked to put up here for wider access. The first block covering days 1-14 is here, days 15-30 are here, days 31-50 are here. They don’t really work as a continuous narrative, so I would encourage a browse. As you’ll twig from the title, I’m picking positives wherever possible. The COVID-19 situation isn’t positive, it’s absolutely dire. But as well as the frontline medical professionals there are a lot of very busy scientists working away on this in the background who I think we should pay tribute to.
Statement of interest: I am not, and never have been, medically qualified. I have an undergraduate degree and PhD in human physiology. This was more than 20 years ago, and since then I’ve worked in other disciplines. Consequently whilst I am comfortable reading the medical literature and distilling it into a few simple sentences, I am not going to comment in any detail on any of the science I’m referring to, and all of the posts will be short. Seeing as my blog posts are usually far too long and geeky, you’ll be glad of this… But I will always state my reference and provide a link. This last bit is important!
Back after a bit of a break for a few days. Today, Bruno Latour, interviewed in the Guardian. Latour is a french philosopher who studies how science works. I’ve been hoping to include something from him on COVID-19 for a while because he is super brainy, but a lot of what he writes is too hardcore for me to understand! But this Guardian journalist managed to distill Latour’s thoughts into something readable.
No science today, a poem instead. “The Great Realization” by Tom Roberts.
Using genetic sequences to track down outbreaks. Some countries are routinely sequencing the COVID-19 genome in every patient diagnosed with the disease. We’ve looked at the GISAID database in a previous post, and at the fact that the differences in the virus genome, whilst small, give us an idea of where new cases have come from. Today’s article looks at this in more detail and discusses how this might be useful as part of contact tracing.
Nice article about the visual representations of coronavirus. Lots of pretty pictures. Also, includes a link to an outline that children (and adults) have been colouring in to help visualise the invisible.
Vaccine trials in macaques. In this trial, macaques were given 6 different potential COVID-19 vaccines and then deliberately subjected to COVID-19 6 weeks later. Vaccinated animals exhibited strong antibody responses much lower viral loads than controls. The 6 vaccines were based on slightly different elements of the COVID-19 spike protein, which means researchers rapidly get a better understanding of what vaccine designs are most likely to be effective.
The Royal Society has been stuffed full of clever people since 1660. Here’s their president, Venki Ramakrishnan, on Following the Science. ‘Evidence-based decision making should absolutely be a cornerstone of government, especially in a pandemic for which science is of paramount importance to our response. However, we must also recognise both the potential and the limits of science.‘
Phase 1 clinical trials of a COVID-19 vaccine. Today’s paper describes a trial in 108 volunteers in China. Phase 1 trials are where you test something on a small number of people to see if it is dangerous (first rule of medicine, do no harm!). Obviously you’re actually hoping that the intervention is beneficial rather than just not harmful. Side effects of the COVID vaccine were mild to moderate (fever, headaches, muscle pain) and subsided within 48 hours after the vaccine was administered. Immune responses were measured after 14 and 28 days and most volunteers were found to have high levels of antibodies to COVID-19, along with a more generalised immune response. Phase 2 clinical trials are now underway (where the vaccine is administered to more volunteers). A long way to go still; its clearly unethical to test whether the vaccine is effective by subjecting human volunteers to COVID-19 deliberately, and so the lack of circulating virus in Wuhan means that efficacy trials will need to be undertaken elsewhere.
Super-spreading events. We’re probably all familiar with the R number – the number of people that a COVID-19 infected individual will go on to infect. If this number is below 1, the virus dies out, if it’s above 1, it will rapidly multiply. BUT, the geekier bit of detail for today’s ‘moment’ is the k number. This is the tendency for cases to cluster around ‘super spreader events’. In diseases that cluster very strongly, most people with the disease don’t go on to infect others, but a very small number of individuals do. This is usually circumstance; perhaps an individual at the stage of the illness where they are shedding large numbers of virus particles, combined with an enclosed space where there’s a lot of surface contact or aerosol generation. Calculating the k value and the understanding the nature of super-spreading events is hugely important; if for example just 10% of interactions lead to 80% of COVID cases, then we can imagine a route out of lockdown where most activities are really pretty safe but a small fraction will need high levels of control and vigilance. Most of the stuff on k number is still in peer review, but there’s a nice clear explanation of it here.
Emergency attendance at A&E. A concerning aspect of the COVID-19 pandemic is the collateral damage to people’s health amid reports that A&E departments are virtually empty. Hence the recent publicity around emergency services being available as normal for those that need them. The reported reason for calling an ambulance is routinely monitored in the UK, and this analysis from the West Midlands showed no decrease in call outs for suspected heart attacks and strokes (the two conditions for which rapid intervention is most likely to be life-saving) since the start of the pandemic. A&E is still there for properly sick people; don’t stay at home, they won’t tell you off.
No geeky science today, just some nice explanatory animations and graphics.
Solving protein structures in order to aid drug discovery. Today’s article in Nature is an account of the global collaborative effort of working out what the COVID-19 virus proteins look like. Starting with the gene sequence published on 10th January, the process of producing and visualising a whole host of the viral proteins is described. The first step is to make gene constructs – sequences that encode specific proteins, and insert them into bacteria, which then produce lots of the protein. The protein is then extracted and purified. Next, the 3D structure of the protein is imaged in high resolution, either using crystallography or electron microscopy. This process generally takes a year, but for COVID-19 was rapidly completed for some of the more familiar proteins and those thought to make particularly promising drug targets. Once these 3D images have been produced, the process of working out what drugs mind bind to these proteins and disable them began.
A part of the plan that the UK got right. The government has come in for a lot of justified criticism about how they have responded to this pandemic. But nestled amongst the incompetence, the legacy of underfunding and the post-truth communication strategy, there is the odd bit of planning we appear to have got right. After the H1N1 flu pandemic in 2009, the NIHR (National Institute of Health Research) funded a portfolio of preparedness projects; the type you’d need if a global influenza outbreak arose. More recently, the NIHR asked these projects to extend their scope to include the possibility of other infectious diseases, not just influenza. These projects were designed, planned, peer reviewed and approved, and were put on standby with a low level of maintenance funding so they could be brought into action quickly if the need arose. That need is now, and 8 of the 9 are now deemed relevant and useful to the COVID-19 pandemic and are up and running. You can read about them here.
Impacts of reduced air pollution on mortality in China. Measurable decreases in NOx and PM2.5 have been reported ever since lockdown began, but are actually quite tricky to interpret because of their correlation with meteorological features, and then even harder to relate to mortality. But the short answer is that the number of deaths avoided in China due to lower NOx levels is 8911, and 3214 less due to lower PM2.5 (it’s a bit dodgy to add these numbers together, partly because the same person can’t die twice). The fact that this exceeds China’s estimated mortality from COVID-19 is a shocking testament to how we’ve normalised deaths from air pollution.
Drug treatments. It takes years to develop a drug against a particular virus, but since there are some common features between different viruses, there are lots of clinical trials underway on existing anti-virals that have been used against SARS, MERS etc. In this phase 2 clinical trial published in the Lancet, a combination of 3 anti-virals was significantly more effective than the common current approach of 2 anti-virals, when used in patients with mild to moderate COVID-19. There were only mild side effects, and the 3 drug combination alleviated symptoms, reduced viral shedding and allowed patients to recover from COVID-19 more quickly than in the control group taking only 2 drugs. Expect to see a steady trickle of new papers emerging on the effectiveness or otherwise of drug treatments over the next few months.
Vaccine development. We’ve probably all heard of the idea that vaccines can be based on injecting a weakened version of a virus into the body. Our immune system recognises it as a threat, and develops antibodies to combat it, and these are then ready to deploy against an actual live virus of the same type. We don’t always use weakened/inactivated viruses as the starting point; some vaccines being tested at the moment are based on recreating sections of the spike protein on the surface of the COVID-19 virus. Once we’ve found a vaccine that works (clinical trials normally take years rather than months), the next time consuming bit is to build up the manufacturing system. The way this is done depends on the type of vaccine, but it can involve having to grow huge amounts of the virus in a suitable growth medium (this is often chicken eggs), and then isolate the virus, kill it, and prepare it into the injection formula. This scaling up process is unlikely to take less than 6 months, and is highly dependent on the type of vaccine. It’s also expensive. Despite this, work is already underway to do the necessary scale up of several of the most likely platforms before we know that a particular vaccine works. We know that much of this work will be turn out to be redundant. But we have a choice between creating redundant systems, or 6 months extra in the development cycle. 3 articles to choose from today on the subject: BMJ, NEJM and Nature.
In the rush to help, lots of clinical research teams are undertaking clinical trials. As discussed on day 46, they get registered before they start on one of a number of databases. The good ‘ol Lancet are now mining all of these databases so that all COVID-19 trials can be found in the same place. This will help avoid duplication of effort, and encourage collaboration between research teams. Larger trials have more statistical power; they can detect small but important benefits against a background of high variability. Given that we still have such limited evidence of drugs that are effective, doing better clinical trials is imperative. Searchable by country, treatment, study design etc. For those of us with short attention spans, there’s also a predicted end date for each trial; many are rapid assessments, expecting about 400 to be complete within the next couple of months. So please stay at home in the mean time… x
Day 53 – bonus post!
This wasn’t a moment of reprieve, and it’s definitely not positive, but it is the clearest summary I have seen of what might be needed for the UK to leave lockdown.
I know a lot of what I post is impenetrably geeky. This one in the BMJ isn’t, I promise. Step 1: read it. Step 2: stay at home. Love to all. x
We now have an independent ‘SAGE’ group. Its meeting is on youtube. It’s over 2.5 hours, but it is good. Dip in for a listen, it doesn’t really matter which bit. The first bit is a modelling expert talking about the need for public and unambiguous quantification of policy decisions; what will happen in particular circumstances, and the important distinction between strategies to control the second wave in a few months time, and the immediate policy decisions that cause a flare up of the current wave of the epidemic. The striking thing about the discussion is the respect that the participants have for each other. It’s also a good illustration of how ‘science’ is not a single discipline and another example of how the government’s ‘following the science’ mantra is a meaningless soundbite.
There’s an understandable fear that healthcare workers are contracting COVID-19, either in their workplaces or the community. If they don’t display symptoms, then there’s also a fear of them spreading the infection. This study at Barts hospital in London is swabbing asymptomatic healthcare workers on a weekly basis. They find that the prevalence of COVID-19 in this group tracks that of the general public in London, and importantly is declining at a similar rate, suggesting that staff are doing an excellent job of maintaining safety standards and are not picking up COVID from patients, despite them still treating high numbers of patients and therefore being exposed to this risk on a daily basis.