Why do we have multiple waves of disease? How can we handle and anticipate the risks? Why do the epidemiologists appear helpless? This and more… As I try to make sense of what happens I share my understandings with you. I kind of try to show the way I think, which may be applied to other areas.
The third wave of COVID19 is upon us
In a very twisted way, the beginning of a new wave of disease is not really visible in graphs. If anything we see very little signs of alarm, and only in the specific parameters. In the beginning, the focus is not on technical analysis but on fundamental analysis. The same thing applies to economic indicators. If we see a technical change, it may often come well before or after a fundamental change.
So we kind of open up to the news. OK. There is a new mutation of the virus. So what? Now, that mutation is more contagious. Interesting. And it affects vaccinated people. This is new… Now what?
What should we count?
Based on local medical policies, not all countries experience the third wave of disease now. In Israel, it is the fourth wave since we were lucky enough to choke the first wave in the middle just to see its revival. In other countries, it may be a different, maybe a smaller number. For India where the delta mutation initiated, this might be the first major wave of the disease. What do we count?
While technically it is possible to count the sign changes of the derivative of the data smoothed over a certain time period, I do not really like this methodology. I prefer to focus on qualitative changes that were reflected in graphs.
The first wave of the disease was characterized by a relatively mild virus, avoiding detection and thus spreading repeadly before anyone could stop it. In the second wave, the British or alpha mutation was significantly more contagious. It attacked a new population and soon replaced the original variation. The third wave now replaces the alpha mutation wherever it is tested.
Compare with historical data
Ideally, this is the time to go to the historical data for comparison. However, there is very little information for comparison. There were very few pandemies. Each time was unique in many senses. Moreover, the historical records are not very accurate.
Probably the Spanish flu was the most well-documented pandemy. It had three waves. The first wave mainly hit weak and old people. The second wave targeted new victims, like pregnant mothers. It was the deadliest. The third wave was less deadly, but some of the people who had been previously sick were sick again. A coincidence? Maybe not.
The intelligence of genetic algorithms
Neural networks are not the only way to generate intelligent algorithms. One of the best ways to build intelligent systems is genetic algorithms. In this scenario, various parts of the system undergo mutations and competition. Only the most effective solutions survive. While the process is basically blind and extremely inefficient, it provides surprisingly creative and intelligent solutions.
Take a virus. It is a relatively simple and stupid mechanism, yet its behavior appears surprisingly intelligent to human observers. If the first wave of the virus is too deadly, its victims will be isolated before the virus can spread. Most viruses do not cause pandemies. They die.
So the virus that survives is sneaky. It is hard to detect, and it has a delayed response. Then various successful mutations of virus compete with each other. The mutations that survive must be more contagious than their peers. With time many of the targets get resilient one way or another. To survive the virus mutates to affect the resilient targets. And then the virus needs to move to new populations, like different kinds of animals. Otherwise, it will die.
Most viruses die. The viruses that survive appear to be intelligent, to the point that we think they were specially constructed. The funny thing is: most specially constructed viruses are stupid: extremely deadly and not expected to travel beyond a relatively small target area.
Why does the virus trick the experts?
Historically viruses did not have to deal with immunologists. Simply no immunologists were present. However, the virus mutates and our experts struggle to predict the mutation. If they could deal with the virus, the virus would likely die young.
The viruses that survive are kind of unpredictable. They mutate, often very fast (like HIV), often so fast that the previously effective treatment becomes ineffective. Other viruses jump species, for example between humans and birds, coming each year in a slightly different way. And more sinister viruses kind of hibernate. Ebola can stay dormant for a year before it comes back.
Additionally, viruses are affected by environmental factors we do not fully understand. Clearly, some viruses are sensitive to hot and cold weather. But other viruses show seasonal patterns without leaving any clues for the behavior. The thing is, every time the virus is almost dead it is under the greatest pressure to mutate in order to survive. And then it comes back.
Even vaccination is not always effective as they target specific proteins, which may get modified in mutation. And the more successful virus is present in more carriers, further increasing the chance of mutation.
What can we do?
Unfortunately, the most basic low-tech methods of protection are the most reliable. Reduce unsafe encounters, and use physical protection. It’s a bummer, but it works. More often than not.
The next line of defense is vaccination. Only it looks like every year we need a new vaccine. This is a huge headache since no vaccine is 100% safe. And then there are costs, and shelf time, and logistics. It is a nightmare.
Even worse, when the virus attacks other species we are kind of obliged to obliterate large stocks. It is not that bad when Danish mink farms or Mexican pig farms are destroyed. What if our dogs or cats are targeted by the virus? Will we kill them too?
And what about monitoring humans with electronic devices? Will we use the last shreds of our privacy and wear electronic handcuffs? That can be a huge issue. Democracies are fragile and can easily die.
The original threat, new targets, immune population
So far it looks like the most effective strategy for virus survival is clear. It starts with the original variant, trying to spread. Then the inner competition of virus variants generates a more effective virus, capable of targeting new populations. Next, most of tje population gets immune. To survive the virus learns to bypass the defences of the immune targets. And finally, when the competition gets too tough it crosses to another species.
Or not. Viruses may die and do die more often than not.
The larger the spread of the virus, the more chances it has to develop an effective mutation. The smarter the virus becomes – and it is already a smart and effective virus. The more likely it is to choose the winning strategy.
Do not think we overcome the disease too early. The larger the competitive pressure, the faster the successful mutations spread. If the virus appears almost over, it is probably desperately trying to mutate.
The genetic algorithm models I ran on my PC usually behave this way. If not, they are usually not sufficiently competitive, not “smart” enough…
