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Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak
Author(s) -
BRITTON TOM,
KYPRAIOS THEODORE,
O'NEILL PHILIP D.
Publication year - 2011
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2010.00726.x
Subject(s) - inference , outbreak , measles , econometrics , epidemic model , mixing (physics) , mathematics , statistics , statistical inference , computer science , demography , artificial intelligence , medicine , virology , sociology , vaccination , population , physics , quantum mechanics
. A stochastic epidemic model is defined in which each individual belongs to a household, a secondary grouping (typically school or workplace) and also the community as a whole. Moreover, infectious contacts take place in these three settings according to potentially different rates. For this model, we consider how different kinds of data can be used to estimate the infection rate parameters with a view to understanding what can and cannot be inferred. Among other things we find that temporal data can be of considerable inferential benefit compared with final size data, that the degree of heterogeneity in the data can have a considerable effect on inference for non‐household transmission, and that inferences can be materially different from those obtained from a model with only two levels of mixing. We illustrate our findings by analysing a highly detailed dataset concerning a measles outbreak in Hagelloch, Germany.