The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data
Abstract
We propose a transmission model to estimate the main characteristics of influenza transmission in households. The model details the risks of infection in the household and in the community at the individual scale. Heterogeneity among subjects is investigated considering both individual susceptibility and infectiousness. The model was applied to a data set consisting of the follow‐up of influenza symptoms in 334 households during 15 days after an index case visited a general practitioner with virologically confirmed influenza. Estimating the parameters of the transmission model was challenging because a large part of the infectious process was not observed: only the dates when new cases were detected were observed. For each case, the data were augmented with the unobserved dates of the start and the end of the infectious period. The transmission model was included in a 3‐levels hierarchical structure: (i) the observation level ensured that the augmented data were consistent with the observed data, (ii) the transmission level described the underlying epidemic process, (iii) the prior level specified the distribution of the parameters. From a Bayesian perspective, the joint posterior distribution of model parameters and augmented data was explored by Markov chain Monte Carlo (MCMC) sampling. The mean duration of influenza infectious period was estimated at 3.8 days (95 per cent credible interval, 95 per cent CI [3.1,4.6]) with a standard deviation of 2.0 days (95 per cent CI [1.1,2.8]). The instantaneous risk of influenza transmission between an infective and a susceptible within a household was found to decrease with the size of the household, and established at 0.32 person day−1(95 per cent CI [0.26,0.39]); the instantaneous risk of infection from the community was 0.0056day−1(95 per cent CI [0.0029,0.0087]). Focusing on the differences in transmission between children (less than 15 years old) and adults, we estimated that the former were more likely to transmit than adults (posterior probability larger than 99 per cent), but that the mean duration of the infectious period was similar in children (3.6 days, 95 per cent CI [2.3,5.2]) and adults (3.9 days, 95 per cent CI [3.2,4.9]). The posterior probability that children had a larger community risk was 76 per cent and the posterior probability that they were more susceptible than adults was 79 per cent. Copyright © 2004 John Wiley & Sons, Ltd.
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Hayden, F. G., Treanor, J. J., Fritz, R. S., Lobo, M., Betts, R. F., Miller, M., Kinnersley, N., Mills, R. G., Ward, P., & Straus, S. E. (1999) · JAMA · doi:10.1001/jama.282.13.1240 · Cited by 550
Carrat, F., Tachet, A., Rouzioux, C., Housset, B., & Valleron, A. (1999) · Clinical Infectious Diseases · doi:10.1086/515117 · Cited by 154
Self-citation
Cited by
Containing Pandemic Influenza at the Source
Longini, I. M., Nizam, A., Xu, S., Ungchusak, K., Hanshaoworakul, W., Cummings, D. A. T., & Halloran, M. E. (2005) · Science · doi:10.1126/science.1115717 · Cited by 1,163
Chowell, G., Bettencourt, L. M. A., Johnson, N., Alonso, W. J., & Viboud, C. (2008) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2007.1477 · Cited by 171
Self-citationThe estimation of the effective reproductive number from disease outbreak data
(2009) · Mathematical Biosciences and Engineering · doi:10.3934/mbe.2009.6.261 · Cited by 128
Miyaki, K., Sakurazawa, H., Mikurube, H., Nishizaka, M., Ando, H., Song, Y., & Shimbo, T. (2011) · Journal of Occupational Health · doi:10.1539/joh.10-0024-fs · Cited by 40
The impact of prior information on estimates of disease transmissibility using Bayesian tools
Moser, C. B., Gupta, M., Archer, B. N., & White, L. F. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118762 · Cited by 11
“In recent years Bayesian methods have been developed to estimate transmission parameters, and can be particularly useful in instances with sparse data or when prior data about an outbreak exists; however these types of models have often been limited to Bayesian evidence synthesis or compartmental models [7] [8] [9] [1…”
Method
Metadata
- Authors
- Cauchemez, S., Carrat, F., Viboud, C., Valleron, A. J., & Boëlle, P. Y.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2004-10-25
- Volume
- 23
- Issue
- 22
- Pages
- 3469-3487
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2026494703
- Cited by
- 384
- References
- 2
- FWCI
- 9.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cauchemez, S., Carrat, F., Viboud, C., Valleron, A. J., & Boëlle, P. Y. (2004). A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data. Statistics in Medicine. https://doi.org/10.1002/sim.1912
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