Even when the R0 reaches 1.0 those who are infected will still infect one more person (on average). Infections continue to happen after the R0 is below 1.0.
I think this based on the assumption that R0 is about 2.3. That means that on average, one infected person infects 2.3 other persons. Then you need to have about 60 percent of population to get immunity against the virus by first being infected with it. At that point you reach the threshold for herd immunity and the disease will soon stop spreading.
However, R0 is not a constant. For example, with good hygiene, avoiding human contacts etc it is possible to lower the rate of average number of persons each sick person infects. If we manage to do that, the threshold for herd immunity will be lower than 60 %.
You have misunderstood what R0 and R_effective mean. R0 certainly can, and often does, go below 1. The only difference between R0 and R_effective is that R0 assumes everyone in the population is suaceptible to the virus.
It will, as an R higher than 1 means that there will be no maximum in the new infections at all (while any health system obviously does have a maximum capacity that is significantly lower than the size of the population).
R going lower than one means, there will be a maximum in the future. Of course, only when numbers are currently increasing.
R is basically the second derivation of the case count (+1). So a lower R first means that the increase in new infections decreases, until the new infections decrease.
Technically, yes, there is an R0 above which a virus is self sustaining. But if something has R0 of 1.25 the herd immunity threshold is about 20%, whereas if it's 18, the HIT is nearly 95%. Additionally, R0 isn't an absolute property of the pathogen, so even assuming there is this absolute HIT, if R0 is around 1, some people will be above 1 and some below 1.
As herd immunity increases, the R0 decreases. A disease with a low R0 spreads more slowly (hence the logistic curve) and is therefore easier to track and trace manually, and has a lower peak and total infection count.
I don't think this is binary at all, or even _that_ stark of a threshold. Transmission doesn't continue at the same exponential rate until it hits around 60% and then suddenly disappear. It doesn't even come close to approximating that behaviour.
The 1-1/R0 is the point that the virus starts to burn out but there is still a long tail of infections. The total population that is infected will be higher than 1 - 1/R0. Also if the estimates of IFR were too high than the estimates of R0 were too low.
R0 is a statistical average, but even if we assume that every person infects exactly 5.7 other people, these two tourists would have infected 11.4 people. Those 11.4 people would infect a further 64.98 people. you're already pretty close to 100 after just two levels of spread.
The only reason it's limited to as low as 100 is that people are taking precautions to prevent the infection from spreading.
R0 is at the initial onset, without any immunity and any preventive measures. If the virus had R0 of 1.3 it would be easily containable. The reality is that it has an R0 of 2-6 and an effective R > 1 in most except Asian countries.
A R0 of 1.3 with an incubation time of 1 week would not lead to the doubling of cases in 4 days. The whole thing is a crime against math.
Couldn't it reach such a percentage with a R0 being lower than 10?
If your R0 is 2.5, herd immunity will make the current R value go under 1 when 1 - (1 / 2.5) = 60% of the population got infected.
While that means that the epidemic will stop growing exponentially, the virus will not stop propagating, as there will still be a non-exponential propagation of the virus (e.g. if 10% of the population is still infectious at the time herd immunity is reached -- when R becomes 1 --, we should expect at least 10% more infections). Or am I missing something?
R:0 is an average, not a promise. It's that simple. Some people will infect 50 people at a wedding. Some people will spend a couple of weeks in bed having the time of their lives. Some people will just stop breathing. R is based on a populace, not a person.
R0 of an infection is the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection.
We are nowhere near all people in a population being susceptible to infection with the vaccine rollout (and the people that are susceptible, it's their choice.)
I understand what R0 means perfectly well. Vaccination and other factors can reduce it from the 6-7 level to something a bit lower. But there is no plausible sum of factors that will bring it below 1 for an extended period. Most interventions are correlated, not independent, and so the sum is less than the parts.
More succintly: In the SIR (Susceptible, Infected, Resolved) epidimic model, r0 is the ratio of dI/dt / dR/dt. r0 < 1 means people are recovering at a rate faster than new people are getting sick.
If each new case, on average results in less
than one new case, and all cases eventually resolve (recovery or death), then it's a mathematical truth that the number of active cases must go down over time. The decrease isn't necessarily monotonic, but over a long enough time frame it's inevitable. The bursty nature of super-spreading will produce variance, but as long as these super-spreaders are accounted for in the observed r0, the variance itself doesn't change the long-term outcome.
Now, the estimated/measured r0 is just a sample/estimate of the (unknowable) "true" r0, and r0 isn't constant, but those are separate issues.
If you assume that R is currently 2 with 0% of the population able to be infected, is it reasonable to assume R would drop below 1 with 100% of the population having 60% probability of being immune?
Reports I've seen suggest 1.4 to 2.5, but it's early days. Essentially if the R factor (number of people infected by a single case [0]) is less than 1, it'll die out, otherwise it'll grow.
However, that's very simplistic; one would like to think the R factor would be lowered by better hygiene.
Separately, it seems that (unlike say Spanish Flu) this tends to be worse for people who have pre-existing health issues.
There is concern that possibly people who are asymptomatic could still be infectious.
Time will tell. One number (that'll take quite some time to even have a stab at) is the mortality compared to, say, common flu.
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