Title | ||
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Bayesian estimation of the offspring mean in branching processes: Application to infectious disease data |
Abstract | ||
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A single-type Bienayme-Galton-Watson branching process (BGWBP) with a generalized power series offspring distribution is considered as a model of the spread of an infectious disease in a population. Our main goal is to estimate the basic reproduction number R"0, which is represented by the offspring mean of the BGWBP, applying a Bayesian approach. The only data assumed to be available are the initial number of infected individuals and the total number of infected individuals. We are using the Metropolis-Hastings algorithm to simulate the posterior distribution. The usefulness of the described method is demonstrated on some real data on the number of reported cases of mumps in Bulgaria during the period 2005-2008. |
Year | DOI | Venue |
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2012 | 10.1016/j.camwa.2012.01.049 | Computers & Mathematics with Applications |
Keywords | Field | DocType |
basic reproduction number r,infectious disease,metropolis-hastings algorithm,bayesian approach,total number,initial number,bayesian estimation,generalized power series offspring,infectious disease data,posterior distribution,infected individual,metropolis hastings algorithm | Econometrics,Population,Mathematical optimization,Metropolis–Hastings algorithm,Offspring,Posterior probability,Basic reproduction number,Statistics,Bayes estimator,Mathematics,Branching process,Bayesian probability | Journal |
Volume | Issue | ISSN |
64 | 3 | 0898-1221 |
Citations | PageRank | References |
1 | 0.41 | 0 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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Angel G. Angelov | 1 | 1 | 0.41 |
Maroussia Slavtchova-Bojkova | 2 | 2 | 0.77 |