On parameter estimation in population models II: Multi-dimensional processes and transient dynamics

J.V. Ross, D.E. Pagendam and P.K. Pollett

Theoretical Population Biology, 2009, vol. 75, issue 2, 123-132

Abstract: 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.

Keywords: Ecology; Epidemiology; Parameter estimation; Infectious period distribution; Markov processes; Dynamic landscape; Stochasticity; Diffusion approximations (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:thpobi:v:75:y:2009:i:2:p:123-132

DOI: 10.1016/j.tpb.2008.12.002

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