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Statistical studies of infectious disease incidence

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  • N. G. Becker
  • T. Britton

Abstract

Methods for the analysis of data on the incidence of an infectious disease are reviewed, with an emphasis on important objectives that such analyses should address and identifying areas where further work is required. Recent statistical work has adapted methods for constructing estimating functions from martingale theory, methods of data augmentation and methods developed for studying the human immunodeficiency virus–acquired immune deficiency syndrome epidemic. Infectious disease data seem particularly suited to analysis by Markov chain Monte Carlo methods. Epidemic modellers have recently made substantial progress in allowing for community structure and heterogeneity among individuals when studying the requirements for preventing major epidemics. This has stimulated interest in making statistical inferences about crucial parameters from infectious disease data for such community settings.

Suggested Citation

  • N. G. Becker & T. Britton, 1999. "Statistical studies of infectious disease incidence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 287-307, April.
    • Handle: RePEc:bla:jorssb:v:61:y:1999:i:2:p:287-307
    DOI: 10.1111/1467-9868.00177
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    Cited by:

    1. Akira Endo & Mitsuo Uchida & Adam J Kucharski & Sebastian Funk, 2019. "Fine-scale family structure shapes influenza transmission risk in households: Insights from primary schools in Matsumoto city, 2014/15," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-18, December.
    2. Zhang, Zhibin, 2007. "The outbreak pattern of SARS cases in China as revealed by a mathematical model," Ecological Modelling, Elsevier, vol. 204(3), pages 420-426.
    3. Joe Meagher & Nial Friel, 2022. "Assessing epidemic curves for evidence of superspreading," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2179-2202, October.
    4. Qingxia Zhang & Dingcheng Wang, 2015. "Assessing the Role of Voluntary Self-Isolation in the Control of Pandemic Influenza Using a Household Epidemic Model," IJERPH, MDPI, vol. 12(8), pages 1-18, August.
    5. Ángel Berihuete & Marta Sánchez-Sánchez & Alfonso Suárez-Llorens, 2021. "A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    6. Karen M Ong & Michael S Phillips & Charles S Peskin, 2020. "A mathematical model and inference method for bacterial colonization in hospital units applied to active surveillance data for carbapenem-resistant enterobacteriaceae," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-32, November.
    7. Tobias S Brett & Eamon B O’Dea & Éric Marty & Paige B Miller & Andrew W Park & John M Drake & Pejman Rohani, 2018. "Anticipating epidemic transitions with imperfect data," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-18, June.
    8. Apiwat Budwong & Sansanee Auephanwiriyakul & Nipon Theera-Umpon, 2021. "Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means," IJERPH, MDPI, vol. 18(15), pages 1-18, August.
    9. Artalejo, J.R. & Lopez-Herrero, M.J., 2011. "The SIS and SIR stochastic epidemic models: A maximum entropy approach," Theoretical Population Biology, Elsevier, vol. 80(4), pages 256-264.
    10. David Lunn & Robert J B Goudie & Chen Wei & Oliver Kaltz & Olivier Restif, 2013. "Modelling the Dynamics of an Experimental Host-Pathogen Microcosm within a Hierarchical Bayesian Framework," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-15, August.
    11. Sifat Sharmin & Md. Israt Rayhan, 2012. "Spatio-temporal modeling of infectious disease dynamics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 875-882, September.
    12. Lu Tang & Yiwang Zhou & Lili Wang & Soumik Purkayastha & Leyao Zhang & Jie He & Fei Wang & Peter X.‐K. Song, 2020. "A Review of Multi‐Compartment Infectious Disease Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 462-513, August.
    13. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.

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