The big push now re Covid19 is to use “social distancing” to cut “R0”, the rate at which infection spreads. More precisely, R0 is the average number of other people that one infected person would infect, if they were not already infected. With no efforts to reduce it, estimates for natural R0 range from
Just linking: https://twitter.com/gro_tse... - there is also a very good R0 treatment.
I noted today an article by Bill Gates describing “researchers are confident they’ll have at least one (vaccine) ready within 18 months”.
In the same newspaper I read of rioting in Nairobi, TODAY, consequent to hunger associated with their COVID-19 lockdown.
Africa can not afford to wait 18 months for a vaccine. Africa can afford neither the time nor the eventual money. Africa could be facing an Armageddon.
Doubtless someone will correct me if wrong, but I’ll suggest *no* vaccine can as reliably induce immunity as the actual infection. So, in this sense, SARS-CoV-2 can be its own best “immuniser”.
A rapid variolation program in Africa could soon render immune enough young people to keep some semblance of economic activity proceeding. The variolated could be certified as such (after two weeks, or however long it ends up taking) and thereafter work in any capacity for which they are trained.
Variolation, once a safe-enough dose is found, would be perfectly cheap. Training in giving intradermal blebs is easy.
If someone has a better, faster, cheaper way of protecting Africa from utter calamity I am yet to hear of it
Is there anyway to predict when the second wave of the pandemic could arise after successfully flattening the curve with less infections (<1% of population) in the first wave?
Is the world prepared for deliberate infection? Is the deliberate infection being tryed anywhere tough not declared?
It is well done video, and includes the effects of isolation, but says nothing about deliberate infection, nor about variance in individual or group infection rates.
Cause is a animated mathematical modelization based nearly about your ideas of variolation, showing how it can evolve.
You gotta say more about why it is relevant before I'll watch an apparently random 23 min video.
Did you see this? https://www.youtube.com/wat...
"A large proportion of viral pathogens that have emerged recently in humans are considered to have originated from various animal species. This is shown by several recent epidemics such as, avian flu, Ebola, monkey pox, and Hanta viruses. There is evidence to suggest that some diseases can potentially be re-introduced to human populations through animal hosts after they have been eradicated in humans. "--Wikipedia
Egads. And what is to be done with the dog and cat populations of the US if they are COVID-19 vectors?
"A tiger at the Bronx Zoo tests positive for coronavirus"By Alaa Elassar, CNN
Dogs have tested positive for the virus in Hong Kong.
One may wonder at the efficacy of lockdowns if this particular virus easily hops between different species such as felines and canines and perhaps all other domesticated animals. I hope we do not end up destroying flocks of poultry.
Or, for that matter, many wild species too. Some speculate this is a bat virus. What if it can cross over into squirrels or pigeons?
Yes - it shows that local initiatives might not be very effective.
For some time I have been thinking about maybe organizing testing of a neighbor community - so that people feel safe waking around and children could be allowed to play together. There is a positive externality to that and there is much local utility - so it seemed a good plan to spread such local initiatives. But alas - a thorough global strategy seems indispensable.
Correction: With a high variance mixed Poisson distribution the ratio of new infections per cycle remains R0, it does not blow out. It was a computation error that led me to think so.The point remains that a small proportion of superspreaders can offset a large proportion of compliant isolators, but the effects can be measured purely in terms of the R0 value. Variance doesn't matter that much.
That said, I think the mixed Poisson branching process is a much better model than just looking at a lognormal for R0. Whatever viral load someone is exhaling, the number of people they infect is going to be more like a draw from a Poisson distribution than a log-normal because the former is a better count for the number of people you come in close proximity to in a given time. LN is continuous and goes out to infinity with a pretty heavy tail. It is used to model financial data because those numbers go through a random series of multiplicative events before surfacing on a balance sheet. Poisson counts how many times a given propensity for an event succeeds in a given interval of time.
I think social distancing is not just suppression but also variolation by proxy with those encountering smaller viral loads and potentially spreading smaller viral loads by drawing it out as well as lessening the severity. If there were a weaker version conferring immunity we could also use that but while the S strain is less deadly, it is already spreading widely. I disagree that we are promoting greater close contact by family isolation; that contact would have been there anyway. The danger is the possible creation of carriers. The trouble with most approaches is there is not sufficient time to act and if we did have that much time, a vaccine would be ready by then. It is a numbers game with the critical factors being protective equipment and testing capability and about the best we can do is buy time. Korea and China have bought this time, probably some others. Many won't be able to but it only represents another risk of many they already face.
You might very well be right, but if so you have way better eyeballing ability than me. If you just mean that the left error bars tend to be shorter than the right ones, then I agree that *that* is a property that is strongly associated with having a zero lower bound. But this doesn't tell us much about how fast the right tail falls off, which (I presume) is what is driving your results.
Eyeballing Fig 13 here, the R0 values seem roughly log normally distributed: https://www.imperial.ac.uk/...
I found & fixed an error; that should be 20 not 33, and 20^10 = 6E34. I've changed the text above to fix that.