A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods

O’Neill, Philip D.

doi:10.1016/s0025-5564(02)00109-8

Cited by 136 publications

(99 citation statements)

References 12 publications

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“…In the past, this approach has been successfully used to deal with similar problems (9,(25)(26)(27)(28)(29). The dataset is "augmented" with missing dates of infection, the few missing diagnoses, and the few missing/censored dates of symptom onset (i.e., the statistical model allows for the possibility that individuals may have been infected after their last interview and that those with missing diagnoses may have been infected too).…”

Section: Methodsmentioning

confidence: 99%

Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza

Cauchemez

Bhattarai²,

Marchbanks³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

339

349

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Evaluating the impact of different social networks on the spread of respiratory diseases has been limited by a lack of detailed data on transmission outside the household setting as well as appropriate statistical methods. Here, from data collected during a H1N1 pandemic (pdm) influenza outbreak that started in an elementary school and spread in a semirural community in Pennsylvania, we quantify how transmission of influenza is affected by social networks. We set up a transmission model for which parameters are estimated from the data via Markov chain Monte Carlo sampling. Sitting next to a case or being the playmate of a case did not significantly increase the risk of infection; but the structuring of the school into classes and grades strongly affected spread. There was evidence that boys were more likely to transmit influenza to other boys than to girls (and vice versa), which mimicked the observed assortative mixing among playmates. We also investigated the presence of abnormally high transmission occurring on specific days of the outbreak. Late closure of the school (i.e., when 27% of students already had symptoms) had no significant impact on spread. School-aged individuals (6-18 y) facilitated the introduction and spread of influenza in households, but only about one in five cases aged >18 y was infected by a school-aged household member. This analysis shows the extent to which clearly defined social networks affect influenza transmission, revealing strong between-place interactions with back-and-forth waves of transmission between the school, the community, and the household.here is a large body of theoretical literature on how social networks and population structures may affect the spread of communicable diseases and hence influence the design of optimal control strategies (1-8). Such work often makes use of detailed data on populations (e.g., demographics in households, schools, and workplaces; mobility and land-use data; contact surveys; or time-use data) but then makes assumptions about how transmission rates change with the type of interaction (e.g., as a function of the setting and the spatial or social distance between individuals, etc.).

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Section: Methodsmentioning

confidence: 99%

Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza

Cauchemez

Bhattarai²,

Marchbanks³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

339

349

View full text Add to dashboard Cite

show abstract

“…From a classical statistics point of view the maximum likelihood estimator (MLE), that is the parameter values which maximise the likelihood of the observations, is viewed as the set of parameters that best captures the dynamics (see Casella and Berger [36] for an introduction to classical likelihood methods). Additionally, for parameter inference by Markov Chain Monte Carlo (MCMC) [37,38,39] the calculation of likelihood ratios is a necessary, and often computationally intensive step. Hence the improvement in speed o↵ered by the FSR method has clear advantages in parameter inference if the results are su ciently accurate.…”

Section: Accelerated Likelihood Calculationmentioning

confidence: 99%

Rapid simulation of spatial epidemics: A spectral method

Brand

Tildesley

Keeling

2015

Journal of Theoretical Biology

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Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or subpopulations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended 'image' of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational e ciency of spatial simulations. We demonstrate the e ciency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel. Highlights• Kernel-based spatial transmission models are computationally expensive for large population sizes.• We present an e cient and accurate alternative simulation methodology (dubbed FSR simulation) based upon a spectral representation of the infection rate.• FSR simulation also permits the accelerated likelihood calculation for more e cient parameter inference.• Using FSR simulation it was possible to perform detailed outbreak forecasting for a generic cattle infection at the continental-scale.

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“…This model is widely used for developing or modeling viral epidemic. Inference of viral epidemic process in populations has been studied in [2], [4], [5], where various features related to the propagation of a viral epidemic, such as the rates of infection and the length of latency periods are investigated [1]. Different types of methods are used for detecting the presence of virus in a file or data stored in a computer [14].…”

Section: Related Workmentioning

confidence: 99%