Home > Research > Publications & Outputs > Enhancing Bayesian risk prediction for epidemic...

Links

Text available via DOI:

View graph of relations

Enhancing Bayesian risk prediction for epidemics using contact tracing

Research output: Contribution to journalJournal article

Published

Standard

Enhancing Bayesian risk prediction for epidemics using contact tracing. / Jewell, Christopher Parry; Roberts, Gareth .

In: Biostatistics, Vol. 13, No. 4, 01.09.2012, p. 567-579.

Research output: Contribution to journalJournal article

Harvard

APA

Vancouver

Author

Jewell, Christopher Parry ; Roberts, Gareth . / Enhancing Bayesian risk prediction for epidemics using contact tracing. In: Biostatistics. 2012 ; Vol. 13, No. 4. pp. 567-579.

Bibtex

@article{5f8da92eee3e46fcbf5600b80c866c1a,
title = "Enhancing Bayesian risk prediction for epidemics using contact tracing",
abstract = "Contact-tracing data (CTD) collected from disease outbreaks has received relatively little attention in the epidemic modeling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource constraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on the disease transmission rate. This paper presents a novel methodology for combining CTD with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of CTD improves posterior predictive accuracy and can directly inform a more effective control strategy.",
author = "Jewell, {Christopher Parry} and Gareth Roberts",
year = "2012",
month = sep
day = "1",
doi = "10.1093/biostatistics/kxs012",
language = "English",
volume = "13",
pages = "567--579",
journal = "Biostatistics",
issn = "1465-4644",
publisher = "Oxford University Press",
number = "4",

}

RIS

TY - JOUR

T1 - Enhancing Bayesian risk prediction for epidemics using contact tracing

AU - Jewell, Christopher Parry

AU - Roberts, Gareth

PY - 2012/9/1

Y1 - 2012/9/1

N2 - Contact-tracing data (CTD) collected from disease outbreaks has received relatively little attention in the epidemic modeling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource constraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on the disease transmission rate. This paper presents a novel methodology for combining CTD with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of CTD improves posterior predictive accuracy and can directly inform a more effective control strategy.

AB - Contact-tracing data (CTD) collected from disease outbreaks has received relatively little attention in the epidemic modeling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource constraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on the disease transmission rate. This paper presents a novel methodology for combining CTD with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of CTD improves posterior predictive accuracy and can directly inform a more effective control strategy.

U2 - 10.1093/biostatistics/kxs012

DO - 10.1093/biostatistics/kxs012

M3 - Journal article

VL - 13

SP - 567

EP - 579

JO - Biostatistics

JF - Biostatistics

SN - 1465-4644

IS - 4

ER -