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estimate Reproduction Number and Generation Time #6

Open ashlinrichardson opened 3 years ago

ashlinrichardson commented 3 years ago

The routine Mtabulate L2559 in simulation.js contains the calculation of R0 for the multiverse, in L2571 ff (this means following....) ....

Please note that both M.xxx (the multiverse) and U.xxxx (the Universes) each keep their own count of R0

and this is done by finding the ORANGE count (persons that have become INERT), and adding the kill=count M.susCt (the count of susceptible which have been infected by the infective transmitter....

This determination is done in P[x].susCt.......in the routine VLtransfer L1918 and the part that deals with infection is in L.1966, L 1972

If you do the math, then at the end when everyone is INERT, the total number of infectives divided by the total number of oranges is near 1...

I suppose that what I should be doing is not dividing by total number of oranges but only those oranges that have non-zero P.susct......

This is probably an error in calculation, and should be tested to see what the difference is between oranges that have no history of infection, and the total number...

Ernie

ashlinrichardson commented 3 years ago

Motivating abstract (Liu et al).. Revisit this later..:

"The basic reproduction number is one of the conceptual cornerstones of mathematical epidemiology. Its classical definition as the number of secondary cases generated by a typical infected individual in a fully susceptible population finds a clear analytical expression in homogeneous and stratified mixing models. Along with the generation time (the interval between primary and secondary cases), the reproduction number allows for the characterization of the dynamics of an epidemic. A clear-cut theoretical picture, however, is hardly found in real data. Here, we infer from highly detailed sociodemographic data two multiplex contact networks representative of a subset of the Italian and Dutch populations. We then simulate an infection transmission process on these networks accounting for the natural history of influenza and calibrated on empirical epidemiological data. We explicitly measure the reproduction number and generation time, recording all individual-level transmission events. We find that the classical concept of the basic reproduction number is untenable in realistic populations, and it does not provide any conceptual understanding of the epidemic evolution. This departure from the classical theoretical picture is not due to behavioral changes and other exogenous epidemiological determinants. Rather, it can be simply explained by the (clustered) contact structure of the population. Finally, we provide evidence that methodologies aimed at estimating the instantaneous reproduction number can operationally be used to characterize the correct epidemic dynamics from incidence data."