Statistical Modeling of Spatiotemporal Infectious Disease Transmission

Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases

Volumn , Issue , 2015, Pages 211-231

  Deardon, Rob ^a   Fang, Xuan ^a   Kwong, Grace P S ^a  

^a UNIVERSITY OF GUELPH   (Canada)

Author keywords

Bayesian inference; Markov chain Monte Carlo; Parameter space discretization; Spatial disease transmission models

Indexed keywords

BAYESIAN NETWORKS; CHAINS; INFERENCE ENGINES; MARKOV PROCESSES; MONTE CARLO METHODS;

BAYESIAN INFERENCE; BAYESIAN MARKOV CHAIN MONTE CARLO; COMPUTATIONAL ISSUES; DISEASE TRANSMISSION; MARKOV CHAIN MONTE-CARLO; PARAMETER SPACES; SPATIO-TEMPORAL DYNAMICS; STATISTICAL MODELING;

DISEASES;

EID: 85015571209     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781118630013.ch11     Document Type: Chapter

References (21)

1. Anderson R. and May R. (1991). Infectious Diseases of Humans. Oxford University Press.
2. Bernardo J. M. (1979). Reference posterior distributions for Bayesian inference. Journal of the Royal Statistical Society, Series B, 41:113-147.
3. Casella G. and Berger R. L. (2002). Statistical Inference. Duxbury.
4. Chis-Ster I. and Ferguson N. M. (2007). Transmission parameters of the 2001 foot and mouth epidemic in Great Britain. PLoS One, 2(6):e502.
5. Cook A. R., Otten W., Marion G., Gibson G. J., and Gilligan C. A. (2007). Estimation of multiple transmission rates for epidemics in heterogeneous populations. Proceedings of the National Academy of Sciences of the United States of America, 104(51):20392-20397.
6. Cooper B. S., Medley G. F., Bradley S. J., and Scott G. M. (2008). An augmented data method for the analysis of nosocomial infection data. American Journal of Epidemiology, 168(5):548-557.
7. Deardon R., Brooks S. P., Grenfell B. T., Keeling M. J., Tildesley M. J., Savill N. J., Shaw D. J., and Woolhouse M. E. J. (2010). Inference for individual-level models of infectious diseases in large populations. Statistica Sinica, 20:239-261.
8. Deardon R., Habibzadeh B., and Chung H. Y. (2012). Infectious diseases models incorporating spatial measurement error. Journal of Applied Statistics, 39(5):1139-1150.
9. Doucet A., de Freitas N., and Gordon N. (editors). (2001) Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag.
10. Gardner A., Deardon R., and Darlington G. A. (2011). Bayesian goodness-of-fit measures for individual-level models of infectious disease. Spatial and Spatio-Temporal Epidemiology, 2(4):273-281.
11. Garthwaite P., Jolliffe I., and Jones B. (2002). Statistical Inference. New York: Oxford University Press.
12. Gelman A., Carlin J. B., Stern H. S., and Rubin D. B. (2003). Bayesian Data Analysis. London: Chapman & Hall.
13. Guttman I. (1967). The use of the concept of a future observation in goodness-of-fit problems. Journal of the Royal Statistical Society, Series B, 29:83-100.
14. Jeffreys H. (1946). An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A, 186(1007):453-461.
15. Jewell C. P., Kypraios T., Neal P., and Roberts G. O. (2009). Bayesian analysis of emerging infectious diseases. Bayesian Analysis, 4(2):191-222.
16. Kwong G. P. S. and Deardon R. (2012). Linearized forms of individual-level models for largescale spatial infectious disease systems. Bulletin of Mathematical Biology, 74(8):1912- 1937.
17. Pawitan Y. (2001). In All Likelihood: Statistical Modelling and Inference Using Likelihood. New York: Oxford University Press.
18. Richardson S. and Green P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society, Series B, 59(4):731-792.
19. Rizzo M. L. (2007). Statistical Computing with R. London: Chapman & Hall/CRC.
20. Savill N. J., Shaw D. J., Deardon R., Tildesley M. J., Keeling M. J., Brooks S. P.,Woolhouse M. E. J., and Grenfell B. T. (2006). Topographic determinants of foot and mouth disease transmission in the UK 2001 epidemic. BMC Veterinary Research, 2(3). DOI:10.1186/1746- 6148-2-3.
21. Spiegelhalter D. J., Best N. G., Carlin B. P., and der Linde A. V. (2001). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B, 64(4):583-639.