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

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  • 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.

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.

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