The Library

Bayesian analysis for emerging infectious diseases

Tools

Jewell, Chris P., Kypraios, Theodore, Neal, Peter and Roberts, Gareth O.. (2009) Bayesian analysis for emerging infectious diseases. Bayesian analysis, Vol.4 (No.3). pp. 465-496. ISSN 1931-6690

Preview

PDF
WRAP_Jewell_Bayesian_analysis_emerging.pdf - Published Version - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (3899Kb)

Official URL: http://dx.doi.org/10.1214/09-BA417

Abstract

Infectious diseases both within human and animal populations often pose serious health and socioeconomic risks. From a statistical perspective, their prediction is complicated by the fact that no two epidemics are identical due to changing contact habits, mutations of infectious agents, and changing human and animal behaviour in response to the presence of an epidemic. Thus model parameters governing infectious mechanisms will typically be unknown. On the other hand, epidemic control strategies need to be decided rapidly as data accumulate. In this paper we present a fully Bayesian methodology for performing inference and online prediction for epidemics in structured populations. Key features of our approach are the development of an MCMC- (and adaptive MCMC-) based methodology for parameter estimation, epidemic prediction, and online assessment of risk from currently unobserved infections. We illustrate our methods using two complementary studies: an analysis of the 2001 UK Foot and Mouth epidemic, and modelling the potential risk from a possible future Avian Influenza epidemic to the UK Poultry industry.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Epidemics -- Mathematical models
Journal or Publication Title:	Bayesian analysis
Publisher:	Int Soc Bayesian Analysis
ISSN:	1931-6690
Date:	2009
Volume:	Vol.4
Number:	No.3
Number of Pages:	32
Page Range:	pp. 465-496
Identification Number:	10.1214/09-BA417
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
References:	Alexandersen, S., Zhang, Z., Donaldson, A. I., and Garland, A. J. M. (2003). \The pathogenesis and diagnosis of foot-and-mouth disease." J. Comp. Pathol., 129(1): 1{36. 473 Andersson, H. and Britton, T. (2000). Stochastic epidemic models and their statistical analysis, volume 151 of Lecture Notes in Statistics. New York: Springer-Verlag. 473 Andrieu, C. and Moulines, ¶E. (2006). \On the ergodicity properties of some adaptive MCMC algorithms." Annals of Applied Probability, 16: 1462{1505. 482 Bailey, N. T. J. (1975). The mathematical theory of infectious diseases and its appli- cations. Hafner Press [Macmillan Publishing Co., Inc.] New York, second edition. 469 Becker, N. G. (1989). Analysis of infectious disease data. Monographs on Statistics and Applied Probability. London: Chapman & Hall. 466 Becker, N. G. and Britton, T. (1999). \Statistical studies of infectious disease incidence." J. R. Stat. Soc. Ser. B Stat. Methodol., 61(2): 287{307. 466 Cauchemez, S. and Ferguson, N. (2008). \Likelihood-based estimation of continuous- time epidemic models from time-series data: application to measles transmission in London." J. R. Soc. Interface, 5: 885{897. 477 Claas, E., Osterhaus, A., van Beek, R., De Jong, J., Rimmelzwaan, G., Senne, D., Krauss, S., Shortridge, K., and Webster, R. (1998). \Human in°uenza A (H5N1) virus related to a highly pathogenic avian in°uenza virus." The Lancet, 351: 472. 478 Dagum, L. and Menon, R. (1998). \OpenMP: an Industry Standard API for Shared- memory Programming." IEEE Comput. Sci. Eng., 5: 46{55. 482 Deardon, R., Brooks, S. P., Grenfell, B., Keeling, M. J., Tildesley, M. J. S. S. J., Shaw, D., and Woolhouse, M. E. J. (2007). \Inference for individual-level models of infectious diseases in large populations." Submitted. 477 Defra (2007a). \Defra Website." [Online; Accessed 24-04-2007]. URL http://www.defra.gov.uk 465, 474 \| (2007b). \Eggs and poultry facts and statistics." [Online; Accessed 12-01-2007]. URL http://www.defra.gov.uk/foodrin/poultry/statistics/index.htm 478 Diggle, P. J. (2006). \Spatio-temporal point processes, partial likelihood, foot and mouth disease." Stat. Methods Med. Res., 15(4): 325{336. 473, 474, 477 European Centre for Disease Surveillance (2006). \Weekly surveillance report." Euro Surveill., 11(51): pii=3098. URL http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=3098 478 Ferguson, N. M., Donnelly, C. A., and Anderson, R. M. (2001a). \The Foot-and-Mouth Epidemic in Great Britain: Pattern of Spread and Impact of Interventions." Science, 292(5519): 1155{1161. 473 \| (2001b). \Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain." Nature, 413: 542{547. 473 Gibson, G. (1997). \Markov chain Monte Carlo methods for ftting spatiotemporal stochastic models in plant epidemiology." Applied Statistics, 46(2): 215{233. 466 Gibson, G. and Renshaw, E. (1998). \Estimating parameters in stochastic compart- mental models using Markov Chain methods." IMA J. Math. Appl. Med. Biol., 15: 19{40. 466 Haario, H., Saksman, E., and Tamminen, J. (2001). \An adaptive metropolis algo- rithm." Bernoulli, 7(2): 223{242. 466, 481 Keeling, M. J., Woolhouse, M. E. J., Shaw, D. J., Matthews, L., Chase-Topping, M., Haydon, D. T., Cornell, S. J., Kappey, J., Wilesmith, J., and Grenfell, B. T. (2001). \Dynamics of the 2001 UK Foot and Mouth Epidemic: Stochastic Dispersal in a Heterogeneous Landscape." Science, 294(5543): 813{818. 473, 474, 476, 477 Kypraios, T. (2007). \E±cient Bayesian Inference for Partially Observed Stochastic Epidemics and A New class of Semi¡Parametric Time Series Models." Ph.D. thesis, Department of Mathematics and Statistics, Lancaster University, Lancaster. Avail- able from http://www.maths.nott.ac.uk/personal/tk/files/Kyp07.pdf. 466, 471, 475, 476 Lekone, P. and FinkenstÄadt, B. (2006). \Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study." Biometrics, 62: 1170{1177. 477 Neal, P. and Roberts, G. (2005). \A case study in non-centering for data augmentation: stochastic epidemics." Stat. Comput., 15(4): 315{327. 466, 471 O'Neill, P. D. and Roberts, G. O. (1999). \Bayesian inference for partially observed stochastic epidemics." J. Roy. Statist. Soc. Ser. A, 162: 121{129. 466 Papaspiliopoulos, O. (2003). \Non-centered parametrisations for hierarchical models and data augmentation." Ph.D. thesis, Department of Mathematics and Statistics, Lancaster University, Lancaster. 477 Papaspiliopoulos, O., Roberts, G. O., and SkÄold, M. (2003). \Non-centered parame- terizations for hierarchical models and data augmentation." In Bayesian statistics, 7 (Tenerife, 2002), 307{326. New York: Oxford Univ. Press. Editors J. M. Bernardo and M. J. Bayarri and J. O. Berger and A. P. Dawid and D. Heckerman and A. F. M. Smith and M. West. 466, 471 Roberts, G. and Rosenthal, J. (2007). \Coupling and Ergodicity of adaptive Markov chain Monte Carlo algorithms." Journal of Applied Probability, 44: 458{475. 466, 481, 482 Savill, N. J., Shaw, D. J., Deardon, R., Tildesley, M. J., Keeling, M. J., Woolhouse, M. E., Brooks, S. P., and Grenfell, B. T. (2006). \Topographic determinants of foot and mouth disease transmission in the UK 2001 epidemic." BMC Vet Res, 2. Available at doi:10.1186/1746-6148-2-3. 475 Scheel, I., Aldrin, M., Frigessi, A., and Jansen, P. A. (2007). \A stochastic model for infectious salmon anemia (ISA) in Atlantic salmon farming." J. R. Soc. Interface, 4: 699{706. 473 UK National Audit Office (2002). \The 2001 outbreak of foot and mouth disease." Report by the Comptroller and auditor general, HC 939, Session 2001-2002, London: The Stationery O±ce. 473 WHO (2007). \Avian Influenza." [Online; Accessed 12-01-2007]. URL http://www.who.int/csr/disease/avian_influenza/en/index.html 478 Xu, X., Subbarao, K., Cox, N., and Guot, Y. (1999). \Genetic Characterization of the Pathogenic Influenza A/Goose/Guangdond/1/96 (H5N1) Virus: Similarity of Its Hemagglutinin Gene to Those of H5N1 Viruses fromthe 1997 Outbreaks in Hong Kong." Virology, 261: 15. 478
URI:	http://wrap.warwick.ac.uk/id/eprint/16601