Stochastic multi-type SIR epidemics among a population partitioned into households

Ball, Frank; Lyne, Owen D.

doi:10.1239/aap/999187899

Cited by 67 publications

(38 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, similar methods could be applied to models related to the basic two-level mixing epidemic, such as multitype models (Ball & Lyne, 2001), models with overlapping subgroups (Ball & Neal, 2002), or models with three or more levels of mixing.…”

Section: Discussionmentioning

confidence: 99%

Bayesian inference for epidemics with two levels of mixing

Demiris

O’Neill

2005

Scand J Stat

View full text Add to dashboard Cite

Methodology for Bayesian inference is considered for a stochastic epidemic model which permits mixing on both local and global scales. Interest focuses on estimation of the within- and between-group transmission rates given data on the final outcome. The model is sufficiently complex that the likelihood of the data is numerically intractable. To overcome this difficulty, an appropriate latent variable is introduced, about which asymptotic information is known as the population size tends to infinity. This yields a method for approximate inference for the true model. The methods are applied to real data, tested with simulated data, and also applied to a simple epidemic model for which exact results are available for comparison. Copyright 2005 Board of the Foundation of the Scandinavian Journal of Statistics..

show abstract

Section: Discussionmentioning

confidence: 99%

Bayesian inference for epidemics with two levels of mixing

Demiris

O’Neill

2005

Scand J Stat

View full text Add to dashboard Cite

show abstract

“…21 Secondly, multitype epidemics, in which each individual is classified into one of a finite number of types, reflecting, for example, age, sex and/or geographical location, may be considered, though to date this has only been done for SIR models. 14,20 The threshold parameter R * is the maximum eigenvalue of a mean matrix, and in general, a nonlinear optimization problem has to be solved to determine optimal vaccination strategies. However, when global mixing is proportionate, this optimization problem reduces to linear programming 16 and optimal vaccination schemes can be constructed directly, although typically they do not admit an explicit characterization.…”

Section: Concluding Commentsmentioning

confidence: 99%

Optimal vaccination schemes for epidemics among a population of households, with application to variola minor in Brazil

Ball

Lyne

2006

Stat Methods Med Res

Self Cite

View full text Add to dashboard Cite

This paper is concerned with stochastic models for the spread of an epidemic among a community of households, in which individuals mix uniformly within households and, in addition, uniformly at a much lower rate within the population at large. This two-level mixing structure has important implications for the threshold behaviour of the epidemic and, consequently, for both the effectiveness of vaccination strategies for controlling an outbreak and the form of optimal vaccination schemes. A brief introduction to optimal vaccination schemes in this setting is provided by presenting a unified treatment of the simplest and most-studied case, viz. the single-type SIR (susceptible -->infective --> removed) epidemic. A reproduction number R*, which determines whether a trace of initial infection can give rise to a major epidemic, is derived and the effect of a vaccination scheme on R* is studied using a general model for vaccine action. In particular, optimal vaccination schemes which reduce R* to its threshold value of one with minimum vaccination coverage are considered. The theory is illustrated by application to data on a variola minor outbreak in São Paulo, which, together with other examples, is used to highlight key issues related to vaccination schemes.

show abstract

“…Studies of infectious disease transmission are often based on simulations, for instance of the possible outcomes when an infectious subject is released into a susceptible population. Careful consideration of the probabilities of any possible outcome then may be used for statistical inference on important parameters, for transmissibility and mixing behaviour [1][2][3][4]. When there is knowledge about contacts between individuals, simulations may incorporate potential paths of the infection in a given network [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Infectious disease transmission as a forensic problem: who infected whom?

Teunis

Heijne

Sukhrie

et al. 2013

J. R. Soc. Interface.

View full text Add to dashboard Cite

Observations on infectious diseases often consist of a sample of cases, distinguished by symptoms, and other characteristics, such as onset dates, spatial locations, genetic sequence of the pathogen and/or physiological and clinical data. Cases are often clustered, in space and time, suggesting that they are connected. By defining kernel functions for pairwise analysis of cases, a matrix of transmission probabilities can be estimated. We set up a Bayesian framework to integrate various sources of information to estimate the transmission network. The method is illustrated by analysing data from a multi-year study (2002)(2003)(2004)(2005)(2006)(2007) of nosocomial outbreaks of norovirus in a large university hospital in the Netherlands. The study included 264 cases, the norovirus genotype was known in approximately 60 per cent of the patients. Combining all the available data allowed likely identification of individual transmission links between most of the cases (72%). This illustrates that the proposed method can be used to accurately reconstruct transmission networks, enhancing our understanding of outbreak dynamics and possibly leading to new insights into how to prevent outbreaks.

show abstract

Stochastic multi-type SIR epidemics among a population partitioned into households

Cited by 67 publications

References 28 publications

Bayesian inference for epidemics with two levels of mixing

Bayesian inference for epidemics with two levels of mixing

Optimal vaccination schemes for epidemics among a population of households, with application to variola minor in Brazil

Infectious disease transmission as a forensic problem: who infected whom?

Contact Info

Product

Resources

About