Great video. Best explanation I've seen so far. I must say I've never heard the word "interval" without the accent being on the 2nd syllable. On a different note, we should probably define the R-naught on a local basis. If we define it and explain how to control it people and businesses will be less scared and more interactive with the solutions.
bad_manbot because series interval is more important a factor with pathogens that are transmitted only when a person is symptomatic (as in Ebola). Because series interval is based on times between symptom onset between hosts in a given chain of people. If you have a pathogen that can be transmitted both symptomatically and asymptomatically, you are only getting part of the numbers (as in Covid-19), because the series interval does not take into account the asymptomatic cases.
I thought this may help you understand better: What do R0 values mean? Three possibilities exist for the potential spread or decline of a disease, depending on its R0 value: If R0 is less than 1, each existing infection causes less than one new infection. In this case, the disease will decline and eventually die out. If R0 equals 1, each existing infection causes one new infection. The disease will stay alive and stable, but there won’t be an outbreak or an epidemic. If R0 is more than 1, each existing infection causes more than one new infection. The disease will spread between people, and there may be an outbreak or epidemic.
The horizontal axis ( x-axis) has been designated for the unit of time ( mostly number of days), and the vertical axis (y-axis) for newly infected individual. Since the is newly infected individual is always 1 in the case of R0=1, the curve is parallel to the horizontal axis. If the vertical axis was designated for total (sum ) of individual from the past and present at any given day, then the curve would be f(x)=x shape.
Dr Franz, if you look at any common respiratory virus, it is incredibly obvious that R, if it is to have meaning, must be affected by outdoor temperature. Many studies have shown this - Lidwell J. Hyg., Camb. (1965), 63, 427 was an early and good one. Does it make sense to use a model that doesn't include temperature? And is there any other way to explain seasonality? (I say no!) See eg pic of colds in the Netherlands 1925-26 plotted with outdoor temp: @t
For calculation I suppose we should take figure of 155 rather than 156 (because susceptible population is 1000 in which 155 have acquired the disease. 1 has been introduced from outside else denominator should be 1001. Just correct me?
if you want to get technical about it, you would also have to subtract out the already-infected people from the population since they will be immune in future SI time intervals. I think the point of the video is just to explain the concepts, though.
I think it's the population currently infected with the disease, rather than the population who has ever been infected - so suppose you get the Covid-19 and then you recover, then you're no longer counted in the infected population. If you think of the model R0 = SP * D * P, D (the average duration of being infected) is already accounted for - basically when R0 = 1, during the duration of your infection you infect only one other person before you recover, so the infected population is stable.
