On parameter estimation in population models II: multi-dimensional processes and transient dynamics.
Recently, a computationally-efficient method was presented for calibrating a wide-class of Markov processes from discrete-sampled abundance data. The method was illustrated with respect to one-dimensional processes and required the assumption of stationarity. Here we demonstrate that the approach may be directly extended to multi-dimensional processes, and two analogous computationally-efficient methods for non-stationary processes are developed. These methods are illustrated with respect to disease and population models, including application to infectious count data from an outbreak of "Russian influenza" (A/USSR/1977 H1N1) in an educational institution. The methodology is also shown to provide an efficient, simple and yet rigorous approach to calibrating disease processes with gamma-distributed infectious period.
Ross
J V
JV
King's College, University of Cambridge, Cambridge, CB2 1ST, UK. jvr25@cam.ac.uk
Pagendam
D E
DE
Pollett
P K
PK
D004196:Disease Outbreaks; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology; D008390:Markov Chains; D008962:Models, Theoretical; D011157:Population Dynamics