IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v67y2018i5p1379-1398.html
   My bibliography  Save this article

Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China

Author

Listed:
  • Cici Bauer
  • Jon Wakefield

Abstract

We extend an interesting class of space–time models for infectious disease data proposed by Held and co‐workers, to analyse data on hand, foot and mouth disease, collected in the central north region of China over 2009–2011. We provide a careful derivation of the model and extend the model class in two directions. First, we model the disease transmission between age–gender strata, in addition to space and time. Second, we use our model for inference on effective local reproductive numbers. For the hand, foot and mouth data, for each of the six age–gender strata we consider that transmission is greatest between individuals within the same strata, with also relatively high transmission between individuals of the same age group but the opposite gender. The local reproductive numbers show strong seasonality, and between‐area differences.

Suggested Citation

  • Cici Bauer & Jon Wakefield, 2018. "Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1379-1398, November.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:5:p:1379-1398
    DOI: 10.1111/rssc.12284
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12284
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12284?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Meyer, Sebastian & Held, Leonhard & Höhle, Michael, 2017. "Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i11).
    2. McKinley Trevelyan & Cook Alex R & Deardon Robert, 2009. "Inference in Epidemic Models without Likelihoods," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-40, July.
    3. Phenyo E. Lekone & Bärbel F. Finkenstädt, 2006. "Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study," Biometrics, The International Biometric Society, vol. 62(4), pages 1170-1177, December.
    4. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
    5. Alexander Morton & Bärbel F. Finkenstädt, 2005. "Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 575-594, June.
    6. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    7. Leonhard Knorr‐Held & Sylvia Richardson, 2003. "A hierarchical model for space–time surveillance data on meningococcal disease incidence," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(2), pages 169-183, May.
    8. Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
    9. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
    10. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    11. Roger D. Peng & Francesca Dominici & Thomas A. Louis, 2006. "Model choice in time series studies of air pollution and mortality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 179-203, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maria Victoria Ibañez & Marina Martínez-Garcia & Amelia Simó, 2021. "A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data," Mathematics, MDPI, vol. 9(13), pages 1-23, July.
    2. Johannes Bracher & Leonhard Held, 2021. "A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts," Biometrics, The International Biometric Society, vol. 77(4), pages 1202-1214, December.
    3. Giacomo De Nicola & Marc Schneble & Göran Kauermann & Ursula Berger, 2022. "Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 407-426, September.
    4. Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held & the SUSPend modelling consortium, 2022. "Assessing the effect of school closures on the spread of COVID‐19 in Zurich," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 131-142, November.
    5. Dirk Douwes‐Schultz & Alexandra M. Schmidt, 2022. "Zero‐state coupled Markov switching count models for spatio‐temporal infectious disease spread," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 589-612, June.
    6. Bracher, Johannes & Held, Leonhard, 2022. "Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1221-1233.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    2. Duncan Lee & Alastair Rushworth & Sujit K. Sahu, 2014. "A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution," Biometrics, The International Biometric Society, vol. 70(2), pages 419-429, June.
    3. Joshua P. Keller & Adam A. Szpiro, 2020. "Selecting a scale for spatial confounding adjustment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1121-1143, June.
    4. Duncan Lee & Richard Mitchell, 2013. "Locally adaptive spatial smoothing using conditional auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 593-608, August.
    5. Leigh Fisher & Jon Wakefield & Cici Bauer & Steve Self, 2017. "Time series modeling of pathogen-specific disease probabilities with subsampled data," Biometrics, The International Biometric Society, vol. 73(1), pages 283-293, March.
    6. Daniel A. Griffith & Richard E. Plant, 2022. "Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects," Stats, MDPI, vol. 5(4), pages 1-20, December.
    7. Panczak, Radoslaw & Moser, André & Held, Leonhard & Jones, Philip A. & Rühli, Frank J. & Staub, Kaspar, 2017. "A tall order: Small area mapping and modelling of adult height among Swiss male conscripts," Economics & Human Biology, Elsevier, vol. 26(C), pages 61-69.
    8. Philip A. White & Durban G. Keeler & Daniel Sheanshang & Summer Rupper, 2022. "Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
    9. João B. M. Pereira & Widemberg S. Nobre & Igor F. L. Silva & Alexandra M. Schmidt, 2020. "Spatial confounding in hurdle multilevel beta models: the case of the Brazilian Mathematical Olympics for Public Schools," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1051-1073, June.
    10. G. Vicente & T. Goicoa & P. Fernandez‐Rasines & M. D. Ugarte, 2020. "Crime against women in India: unveiling spatial patterns and temporal trends of dowry deaths in the districts of Uttar Pradesh," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 655-679, February.
    11. Duncan Lee & Craig Anderson, 2023. "Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data," Biometrics, The International Biometric Society, vol. 79(3), pages 2691-2704, September.
    12. Daniel A. Griffith, 2019. "Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics," Stats, MDPI, vol. 2(3), pages 1-28, August.
    13. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    14. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    15. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    16. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    17. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
    18. Ephraim M. Hanks, 2017. "Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 497-507, April.
    19. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
    20. Francisca Corpas-Burgos & Miguel A. Martinez-Beneito, 2021. "An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting," Mathematics, MDPI, vol. 9(4), pages 1-17, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:67:y:2018:i:5:p:1379-1398. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.