Abstract
In the previous chapter, statistical analysis was based on what we called complete observation of the epidemic process; both the times of infection and removal (recovery) for all infected individuals were observed. In real life such detailed data is rarely available. In the present chapter we study estimation procedures for less detailed partial data. The likelihood for partial data is usually cumbersome to work with, being a sum or integral over all complete data sets resulting in the observed partial data. Other estimation techniques can turn out to be much simpler. Below we present two general techniques which have been successful in several epidemic applications: martingale methods and the EM-algorithm.
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© 2000 Springer Science+Business Media New York
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Andersson, H., Britton, T. (2000). Estimation in partially observed epidemics. In: Stochastic Epidemic Models and Their Statistical Analysis. Lecture Notes in Statistics, vol 151. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1158-7_10
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DOI: https://doi.org/10.1007/978-1-4612-1158-7_10
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95050-1
Online ISBN: 978-1-4612-1158-7
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