Coronavirus Models

With any model of Coronavirus in the UK all you can do is go on past data and likely averages and assumptions. There hasn’t been a coronavirus outbreak so comparisons to other types of virus is pretty shaky. For coronaviruses, most of the research in the UK was done by the Common Cold unit that ran from about 1946 until 1989. It was shut down by Margaret Thatcher’s government in 1989 as it was not producing a product that could be measured in a purely monetary fashion. They wanted firm results and the unit could not give any. With all the modern advancements in knowledge there is still no cure for the common cold as it usually is a complex of many different viruses, one of those being coronaviruses, so it was an almost impossible objective being demanded. Many areas of research have been lost because they didn’t return a direct monetary benefit.

There are about 200 main viruses that contribute to the common cold, being of 4 main types, rhinoviruses, coronaviruses, adenoviruses and enteroviruses. The common cold averages about 15% of the majority of the symptoms being from 4 main types of coronavirus. On average a person will get something like 3 colds a year of varying effects over a lifetime of 72 years, so they will catch a cold somewhere near 200 times. 15% subdivided by 4 suggests that they will catch the same coronavirus approximately 7-8 times, or once very 9 years, but it really depends on exposure, the virus taking a certain amount of time to cycle around for the next infection. This probably doesn’t mean that you get 9 years protection, but rather it just takes that amount of time for the virus to get back to you. We’ve all known government departments like that when returning a call.

Now enter a 5th coronavirus Covid-19. It probably has similar characteristics as other coronaviruses, but is a lot more dangerous. SARS and MERS being even more so in similar proportions. From medical records and taking into account reporting variations through the week we can see in the UK the virus takes about 20 days from reporting to being off reports and that most mortality occurs averaging about 12 days after being reported.

So, to build a comparison you need to take the current date, first step back 12 days for the mortality lag, then back another 20 days for number of infections that will give this return.

So, for mortality percentage date X, you will need to calculate cases at date X-12 and subtract cases at X-32, 20 days earlier to give the cases that would be active on average over this period.

                                                            (Deaths Date X)

 (Mortality Date X)% =  ————————————————-  x. 100

                                          (Cases Date X-12) – (Cases Date X-32)

If you look at the original virus in China it had a mortality of about 2.5%, similar to that for the Alpha variant. Delta had a mortality of about 3.1% and the new AY4.2 about 3.3%.

You then need to factor in an increasing vaccination rate in a country of its mainly vulnerable population.

Basically, there are a number of factors that go towards indicating an outcome.

Age, health, occupation, sex, ethnicity and possibly blood grouping, in that order of effect.

Age mainly takes effect after about 40, occupation will give levels of exposure and duration, sex important as more of the genes affecting immune responses are grouped around the X chromosome compared to the Y to a factor of about 60/40. This will mean that females will generally be better protected against such viruses than males, both reducing with age and infirmity. The upshot being that females will more likely survive an outbreak than males and live generally longer, but things that promote immune responses may cause slightly higher side effects in females than males. Blood grouping similarly having a trend towards certain blood groups for immunities, O being better than A or B.

The only difference with women is if they are pregnant. A pregnant woman’s defences are reduced or damped by their hormones to allow them to have a life grow inside. If their defences were operating at a normal level then their body would fight and damage the baby they are carrying. This happens in some cases where the immune system does not recognise the baby’s characteristics and causes problems, so you get a case where their body thinks of it in the same way as an organ transplant. So, pregnancy puts the woman at an enhanced risk.

People will have varying responses to the virus. If they haven’t been exposed of course there is no defences involved so no herd immunity. Herd immunity is an unknown and little understood quality, probably due to an overall and cumulative match to less-specific coding. If they are exposed then it comes down to the number of times, the length of times and the proximity, any combination increasing the overall exposure. Then the body will respond to the infection from completely repelling it right at the start, so exposed and no contamination being detected, to dying from it at the other end. So, you will get not exposed, exposed and not returning a positive result, exposed and little effect returning a positive result, a moderate result, a serious result, a critical result and dying from it. A serious or critical result probably resulting in a form of long covid or long-lasting effects depending on age and health levels. The likelihood is the cumulative exposure you get predicting the level at which it invades the body.

So, a 20 year old white female in perfect health who works in a private office, who is not particularly sociable with blood group O would get little cumulative effect. On the other end of the scale a 75 year old overweight black man who has diabetes of blood group AB- who still drives a taxi cab and smokes and drinks heavily with a large extended family is not so good.

But in the end, you can only work on averages, compared to averages, compared to averages for effects. Not brilliant, but an indication. Certain populations have certain characteristics that predispose them to certain routes the infection spread will take and re-take. Because of the number of people on the planet, probably only have infected less than 7% in 2 years, and the time it takes to recycle, covid being completely new to the human cycle, it’s likely that this will happen for a couple of decades, so planning has to take into account this occurrence, not simply thinking it’s quickly over and everybody can now get on with day to day life. It’s not over and being kept at bay, hopefully not changing into a MERS type level unnoticed along the way.

For countries, age, population densities and spread, nutrition and medical facilities further affect likely contagion rates and outcomes. The other problem is over enthusiastic legislation and restriction that may cause many extra deaths simply due to the lack of provision of services. The response needs to be equivalent to the risk, not over the top and the lack of sociability that may ensue giving a lower top up of resistance against other diseases such as influenza. Gradual peaks and troughs being made to wildly fluctuate.

A virus is a highly mutating piece of genetic code, much faster than the cells it invades. Normally mutations do not survive, only a few actually having the right characteristics to work properly, but they quickly develop. Some anti-viral drugs work in a way to speed up this pattern of mutation so that the virus is overwhelmed with non-functional values. But the more cases then there are more mutations, there being about 1200 minor mutations, 38 major mutations and 13 worrying ones of which 3 expanded to replace the others, being slightly more efficient, within a 2 year period. What is worrying is how quickly Covid-19 came after SARS and MERS, which are quite closely related.

Then we come to vaccines. Not all vaccines are created equal, some being more high tech than others. mRNA being the latest additions. But exact coding for viruses has its drawbacks, with long term more traditional vaccines using standard less specific defences. The difference can be similar to analogue and digital radio, where digital can transfer so much more, but if the frequency is off it delivers a lot less. There may have been some rivalry going on with developers not wanting just an equivalent protection, but a much superior one. So, the very nature of making them more specific means than with a highly changing virus it loses ‘focus’ quicker after a certain level of changes. The figures of being initially 95% effective compared to 90% or 85% from more traditional ones and after 6 months being as much as 10% lower than traditional ones may be proof of this over specification problem. For the current one they are more effective, but mutations rendering them less effective over time as they are more specific.

But, there is an analogy to the widespread use of such things as malaria drugs and the over use of antibiotics.

Evolution is an on-going affair. For each change in environment an organism responds by altering itself to try to meet the requirements of that new environment. People are divided as to if viruses count as a form of life, but it fulfils one key requirement of life in that it changes to meet a new environment. A machine does not.

We have this sudden change where vaccines and drugs curtail its expansion and limit its survival. So, it’s likely that this widespread use will sponsor a version that will not be curtailed or might use them to help, or change in a way that renders them less effective. With this change can come great danger as it may be into a form that is really overwhelming. Under the guise of existing infections, we might not even notice if this happens and gets out, being lost in the Covid static. What would be the ideal situation for this to happen? Probably a highly mobile country with a large population, a large number of ongoing infections where there was only about 50% of the people vaccinated and still travelling to many other countries.

Calc band

Etna 2027 6

Vesuvius 2029 2030 8-9

Yellow 2050 29

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