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replace output to plot from filter(compartment == "infectious") with new_infections() to show the incidence curve, which is different to the total number or cumulative amount in the infectious compartment at each time.
replace output to plot from filter(compartment == "infectious") with new_infections() to show the incidence curve, which is different to the total number or cumulative amount in the infectious compartment at each time.
instead of replace, show the two use cases:
If you want to show the total burden of the disease, the ‘infectious’ compartment is more correct.
If you want to show the daily burden, then you should use new_infections() to get the daily incidence.
definition: the number of individuals who have been infected in an outbreak. At the end of an outbreak, this is essentially the same as the number of final outcomes.
For a different model, different compartments are used:
in model_default, epidemic size is taken from "recoveries"
in model_ebola, epidemic size is taken from "removed"
relevance: can be used to inform the ratio of susceptibles in a future scenario.
caveat: if you calculate the epidemic size at the peak of the outbreak, it could be an underestimate as many people are infectious.
possibly misunderstanding: the epidemic size does not take from "infectious". The ‘infectious’ compartment increases and decreases with time, so it would be incorrect to say that the epidemic size has decreased, within the time frame of analysis which assumes a constant population.
replace output to plot from
filter(compartment == "infectious")
withnew_infections()
to show the incidence curve, which is different to the total number or cumulative amount in the infectious compartment at each time.Evaluate to use
epidemic_size()
to show the end point of each compartment for scenario comparison or refer to package vignette on modelling intervention scenarios into tutorial episode on modelling interventionsThe text was updated successfully, but these errors were encountered: