I am no biologist but I thought it would be fun to give it a try. Hoping it’s not too late to participate. For the purpose of this experiment, I assumed everything written in sound scientific papers was right, as I had neither the time nor the knowledge to do a proper truth-check.
Here is what I found :
The fact that we see very few human cases compared to what we could expect for a virus which can become human-compatible with a single point mutation could be partially explained by the heterogeneity of the mutation rate over the genome:
in this paper, a 1 to 100 ratio of mutation rate inside the human genome is mentioned : https://pubmed.ncbi.nlm.nih.gov/35218359/ There are known factors which hints at a low / large mutation rate for a sequence, so one could investigate further whether this would be the case for the “single point mutation” mentioned -> you can divide the probability by 100 in the best case
A paper where they infect ferret with H5N1 and test how to mutate the genome to make it airborne:
“Four amino acid substitutions in the host receptor-binding protein hemagglutinin, and one in the polymerase complex protein basic polymerase 2, were consistently present in airborne-transmitted viruses.”
As an alternative to tamiflu which can also be used in combination with it : favipiravir
when used in combination with tamiflu, a 2020 paper (with small sample size = 40) suggests that combining tamiflu with favipiravir improve significantly the efficiency
as it has been envisioned as a drug against COVID, it’s easy to find meta-analyses on the side-effects of this drug
a huge problem with tamiflu is that the influenza viruses develop resistance to it. favipiavir does not seem to have this problem
I’m having trouble parsing but I think the first point is about the mutation rate in humans? I don’t expect that to be informative about flu virus except as a floor.
Ah yes, you’re right. I don’t know why but I made the mental shortcut that the mutation rate was about the DNA of cows / humans and not the flu virus.
The general point still holds : I am wary of the assumption of a constant mutation rate of the flu virus. It really facilitates the computation, but if the computation under this simplifying hypothesis leads to a consequence which contradict reality, I would interrogate this assumption. It’s surprising to have so few human cases considering the large number of cows infected if there is a human-compatible viron per cow.
Another cause of this discrepancy could also be that due to the large mutation rate, a non-negligible part of the virons are not viable / don’t replicate well / …
There are papers which show heterogeneity for influenza / RNA viruses but I don’t really know if it’s between the virus population (of the same kind of virus) or within the genome. And they are like a factor 4 or so in the papers I have seen. So maybe less relevant than expected.
Regarding the details, my lack of deep knowledge of the domain is limiting. But as a mathematician who had to modelize real phenomenon and adapt the model to handle the discrepancy between the model’s conclusion and reality, that’s the train of thought which comes naturally to mind.
I am no biologist but I thought it would be fun to give it a try. Hoping it’s not too late to participate.
For the purpose of this experiment, I assumed everything written in sound scientific papers was right, as I had neither the time nor the knowledge to do a proper truth-check.
Here is what I found :
The fact that we see very few human cases compared to what we could expect for a virus which can become human-compatible with a single point mutation could be partially explained by the heterogeneity of the mutation rate over the genome:
in this paper, a 1 to 100 ratio of mutation rate inside the human genome is mentioned : https://pubmed.ncbi.nlm.nih.gov/35218359/
There are known factors which hints at a low / large mutation rate for a sequence, so one could investigate further whether this would be the case for the “single point mutation” mentioned
-> you can divide the probability by 100 in the best case
A paper where they infect ferret with H5N1 and test how to mutate the genome to make it airborne:
https://pmc.ncbi.nlm.nih.gov/articles/PMC4810786/
“Four amino acid substitutions in the host receptor-binding protein hemagglutinin, and one in the polymerase complex protein basic polymerase 2, were consistently present in airborne-transmitted viruses.”
As an alternative to tamiflu which can also be used in combination with it : favipiravir
https://www.sciencedirect.com/science/article/pii/S0163725820300401, a 2020 paper about it
licensed as an influenza drug in Japan
when used in combination with tamiflu, a 2020 paper (with small sample size = 40) suggests that combining tamiflu with favipiravir improve significantly the efficiency
as it has been envisioned as a drug against COVID, it’s easy to find meta-analyses on the side-effects of this drug
a huge problem with tamiflu is that the influenza viruses develop resistance to it. favipiavir does not seem to have this problem
I’m having trouble parsing but I think the first point is about the mutation rate in humans? I don’t expect that to be informative about flu virus except as a floor.
Ah yes, you’re right. I don’t know why but I made the mental shortcut that the mutation rate was about the DNA of cows / humans and not the flu virus.
The general point still holds : I am wary of the assumption of a constant mutation rate of the flu virus. It really facilitates the computation, but if the computation under this simplifying hypothesis leads to a consequence which contradict reality, I would interrogate this assumption.
It’s surprising to have so few human cases considering the large number of cows infected if there is a human-compatible viron per cow.
Another cause of this discrepancy could also be that due to the large mutation rate, a non-negligible part of the virons are not viable / don’t replicate well / …
There are papers which show heterogeneity for influenza / RNA viruses but I don’t really know if it’s between the virus population (of the same kind of virus) or within the genome. And they are like a factor 4 or so in the papers I have seen. So maybe less relevant than expected.
Regarding the details, my lack of deep knowledge of the domain is limiting. But as a mathematician who had to modelize real phenomenon and adapt the model to handle the discrepancy between the model’s conclusion and reality, that’s the train of thought which comes naturally to mind.