Is there anything new to say on the topic of COVID-19?
Yes, although as positions become entrenched people may choose not to listen! It is also worth recapping on occasion however, as people tend to forget where we have come from and how things have developed along the way. Disclaimer - I am a scientist, and have a scientific view point. I like evidence to back up a hypothesis/statement. I do not seek to covert anyone to a particular way of thinking. I do teach university students however, and I do ask them to question, to think and to form conclusions based on reasoned argument. The most important question they (or indeed anyone) can ask, is 'why?'. So with that caveat out of the way, here is just one semi-scientific viewpoint of the Covid situation! This may be a long post, apologies in advance.
Origins
Covid-19 is a coronavirus. This is a type of virus typically found in some animal species, notably bats, and several variants can cross the species barrier and infect humans. SARS and MERS are two recent examples. Viruses require a living cell as a host. There are many types of virus and they have different mechanisms for replicating - some lay dormant for years, others will use the host cellular machinery to replicate. Some will have no effect on an individual while some will cause significant effects in the host individual. A significant proportion of cancer is actually viral in origin, either directly, or by side effects such as integrating into your genome and turning on genes that should be dormant, or turning off genes that should be expressed. In short, viruses show huge variety and are pretty much ubiquitous. They also mutate rapidly compared to other organisms, generating new strains all the time. These mutations can determine how infectious a virus is to a particular species, and the effects the strain will have on its host organism. Mutations can make a viral strain more infectious or less infectious. The more infectious a virus the faster it spreads (generally). With Covid, the evidence to date suggests that it originated in bats and crossed the species barrier to man. This may have happened via live animal markets in China, but nobody can be certain. It is certainly the case that as we disturb natural habitats and come into closer contact with wildlife that previously we would have avoided (or would have avoided us) we will be exposed to biological agents for the first time. this is not a new phenomenon - think of the Spanish and the Inca's in South America for just one example.
Is there any evidence the virus is man / lab made?
This has been a popular theory in some quarters, not helped by political statements that encourage this thinking. From a scientific viewpoint, it is relatively easy to manipulate the genome of a virus. It is a standard technique both for research, in trying to understand how a virus works, in medicine, for trying to isolate, culture or immunise against a virus, and as a molecular tool. Any university laboratory in the world will have performed procedures relating to this technology, as will any molecular biology R&D lab. What we cannot do however, is make a virus from scratch. So it is certain that nobody 'built' Covid from the ground up as an artificial life form. It is possible to take an existing virus and alter it however, by adding or removing genes or chunks of DNA from the viral genome. This is not a subtle process however, and is extremely easy to spot for a variety of technical reasons. The Covid genome has been sequenced, and that sequence is freely available to the scientific community (and anyone else who wants it). There is no evidence that the genome has been artificially manipulated. The Covid genome has also been compared to other Coronavirus genomes and shows significant similarity as well as expected mutation rates. Without getting too complicated, we can make estimates of mutation rates and the time taken to get from one strain to another, and the Covid genome fits these estimates. So several lines of evidence suggest this is not man-made. Some may choose not to believe these, I would simply suggest that the evidence underpinning a hypothesis is as important as the hypothesis itself, possibly more so.
Sampling and infection rates
So we know the virus exists, and scientific evidence suggests it is a naturally occurring strain. It definitely affects humans, and to a certain extent it does not matter where it has come from, but how it is affecting us. There is plenty of ongoing research into the origins of the virus and mutation rates still, but this is for scientific interest. The study of how it is affecting us all is now as much an epidemiological one, as a molecular one. The key thing to remember when considering population level data is that individuals within a population are all different, and that drawing general conclusions based on sparse data is incredibly complex with wide margins for error. There will always be outliers, there will always be exceptions to the general rule, and populations are not static, so different parts of a (very large) population can show widely different outcomes. This is amplified by sampling strategies - in a sufficiently large population, you need to test a significant proportion of individuals to draw meaningful conclusions and minimise your margin for error. This becomes impossible for extremely large populations, because you cannot test enough of the population to draw meaningful conclusions.
A measure of the spread of infection within a population is given by the 'R' number. This is effectively the number of individuals an infected individual passes the infection onto while they are infectious. A number greater than 1 means the prevalence of a virus in the population is increasing, a number below 1 means it is decreasing. The rise is exponential. 'R' is measured by sampling - effectively you take a random number of individuals from a population, test them for the virus, then repeat this process and work out whether the number of positive individuals has increased or decreased. As it can easily be seen, unless you test a huge proportion of the population, you are not going to get an accurate result. The statistical way to deal with this is to put something called confidence intervals around a number - typically at 95%. This means you define a number, but also a window around that number such that you are 95% certain your estimate is within that window. The bigger the sample you test, the tighter the window is. The issue is that a confidence interval can be difficult to grasp; a confidence interval that spans 1.0 is not helpful (it suggests you are 95% certain the infection rate is either increasing or decreasing!) and variability in the population means a single R number is not representative of all parts of a population anyway. So even in a good scenario, you are using fuzzy data to produce wide estimates, and basing a one size fits all policy on this. Scientifically R numbers are calculated correctly, but they are imperfect (although the best we have) for deciding blanket policy. Some people recognise this and live with the uncertainly, others decry the use of R numbers and ignore them. The approach recently has been to test more, and to base R numbers on local/regional data. To add insult to injury, R numbers work best when infected individuals form a higher proportion of a population, so as infection rates tail off, estimates become even worse. That does not mean testing is useless, but it is just one line of enquiry that informs policy.
Death rates
Much has been made, on both sides of the argument, over deaths due to Covid. Again, we are hampered empirically by poor sampling sizes, lack of accurate testing at time of death, and lack of reliable methodologies for calculating deaths. Should it be at time of death? Should it be the main cause of death? Should it be within 28 days of a positive test? Should we test all deaths post mortem? There is no consensus, and therefore estimates vary even within the UK, let alone between countries. Comparisons are somewhat meaningless, and variation in death rates from year to year anyway make longitudinal comparisons difficult, and accurate ones impossible. Use of different methodologies to support or refute viewpoints is commonplace, and unfortunately very confusing for the general public. An agreed worldwide methodology would help, but again if testing regimens vary, results will vary even of the same calculation is used. It is not in doubt that Covid can cause respiratory distress and in some cases death. Whether individuals would have died anyway is unclear, which co-morbidities are relevant is unclear, and whether deaths are significantly increased or decreased at the population level, and by how much, is also, you guessed it, unclear. Come back in 5 or 10 years and examine the data for a best guess, but even that will be heavily caveated. For now, it is probably safe to simply say that any data on 'death rates' can be refuted or conflicting data presented for an alternate viewpoint. Some will argue the data supports lockdowns, others will argue the data supports that no measures are necessary at all. We can get some idea from other countries, particularly where quarantine or lockdown measures have not been followed (such as Brazil) and note increased deaths, we can look at data from the US and in particular New York and see increased death rates, and we can look at countries with poor living conditions such as India and note high infection rates and deaths. Others will look at Sweden and use that to support alternative arguments. There is no definitive data, so again, look at the strength of evidence underpinning a particular position and draw your own conclusions.
Science, politics and economics
This is a fun one for a scientist! Scientific evidence has largely been derided and devalued in recent decades (I would say that though, I am a scientist remember). Politics, and the need for soundbites in the age of social media and short attention spans has been the priority until recently. Science tends to be cautious, it attaches caveats to findings, rarely makes definitive statements and can be conflicting. Politicians often say they 'follow scientific advice and guidance' as it gives them credibility, but what a politician really follows is the need to be re-elected. They will use science if it meets their needs, but will equally ignore it if it doesn't - that is part and parcel of the job. Yet when we enter a situation such as the current pandemic, scientists are currently in favour again. There is a reason SAGE is in the news, and the Chief Scientific Officer or Healthcare lead flanks whichever politician is giving the days briefing - it adds credibility to the politician. Yet look carefully, and scientists that do not toe the political line will be quickly dropped in favour of those who do. Scientifically, the best way to resolve a pandemic is to isolate everyone to prevent transmission. Politically and economically this is suicide. Balancing these 3 view points - the science to minimise adverse effects, the economic to ensure the effect on the population is not overwhelming, and the politic so politicians can be seen to be doing a good job and be worth re-electing - is a delicate act and the cause of most of the disagreements we see even on threads such as this one, as people cherry pick scientific theory, economic data and political opinion that suit their narrative or viewpoint and then propose this as an argument for or against a position. Some positions are more credible than others (look at the evidence in support of a position to judge this) others are equally credible but in opposition with each other. There is no right answer. No scientist wants to be wrong, no politician wants to be unelectable and nobody wants to tank the economy, but each will have a different perspective on the right way to deal with the reality of 'now'. Hindsight is 20:20 and the past can be studied at leisure.
Random final thoughts
What some see as prudent steps to limit the spread of infection or the consequences of a pandemic situation, others view as an infringement of their civil liberties. I am by no means an expert in this type of sociological argument and try to avoid them as there is no 'right' answer. There are valid points on both sides, and extreme points on both sides that can probably be safely ignored. Some people will accept a small infringement on their day to day activities, others will view any infringement as something worth standing against. The most powerful example is probably the US right to bear arms - some view this as a necessary part of the constitution at the time it was written when the landscape was very different to today, others view it as the right to own a military assault rifle for 'self defence'. Neither side will change the others viewpoint. The more extreme on both sides tend to attract the headlines.
I have steered clear of vaccines and clinical trials, these are complicated and difficult to interpret without significant data. There are also ethical issues I am not in any way an expert in. What we do know is that individual variability means trials must be of significant size to produce statistically meaningful data, and one size will not fit all. This is the case for all medicines and diseases. We currently are uncertain on the longevity of an immune reaction, whether that reaction differs between individuals and if so why. There are multiple factors that influence a bodies response to infection and vaccination. Will a vaccine be produced? Scientifically yes. Will it be 100% effective so we can eradicate Covid like we have smallpox? Probably not, at least not quickly. There are many ways to produce a vaccine, many responses to the same product and much variability and uncertainty about side effects and longevity. There is no quick solution.
Well that was a long post, and there is so much more we could have touched on. There is plenty to learn about Covid, but you have to dig and explore, as well as be willing to listen, judge and evaluate. Always look for the evidence underpinning a position, draw your own conclusions on the strength of this, and above all never be afraid to question.