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As well as r_eff() for effective R (i.e. the dominant eigenvalue of the next generation matrix), it can be useful to have a complementary function that calculates the dominant eigenvector, which indicates the expected distribution of new infections across multiple transmission generations (i.e. once any transient distribution of introduced infections has faded).
This would make it possible to produce plots like the below from Klepac et al (2020), which can be used for planning and situational awareness (e.g. if observed incidence does/doesn't match expectations of what transmission driven by physical contact would produce).
The text was updated successfully, but these errors were encountered:
As well as
r_eff()
for effective R (i.e. the dominant eigenvalue of the next generation matrix), it can be useful to have a complementary function that calculates the dominant eigenvector, which indicates the expected distribution of new infections across multiple transmission generations (i.e. once any transient distribution of introduced infections has faded).This would make it possible to produce plots like the below from Klepac et al (2020), which can be used for planning and situational awareness (e.g. if observed incidence does/doesn't match expectations of what transmission driven by physical contact would produce).
The text was updated successfully, but these errors were encountered: