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Dynamic generalized linear models with application to environmental epidemiology

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  • Monica Chiogna and Carlo Gaetan
  • Carlo Gaetan

Abstract

Summary. We propose modelling short‐term pollutant exposure effects on health by using dynamic generalized linear models. The time series of count data are modelled by a Poisson distribution having mean driven by a latent Markov process; estimation is performed by the extended Kalman filter and smoother. This modelling strategy allows us to take into account possible overdispersion and time‐varying effects of the covariates. These ideas are illustrated by reanalysing data on the relationship between daily non‐accidental deaths and air pollution in the city of Birmingham, Alabama.

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  • Monica Chiogna and Carlo Gaetan & Carlo Gaetan, 2002. "Dynamic generalized linear models with application to environmental epidemiology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 453-468, October.
    • Handle: RePEc:bla:jorssc:v:51:y:2002:i:4:p:453-468
    DOI: 10.1111/1467-9876.00280
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    Cited by:

    1. Kostas Triantafyllopoulos, 2009. "Inference of Dynamic Generalized Linear Models: On‐Line Computation and Appraisal," International Statistical Review, International Statistical Institute, vol. 77(3), pages 430-450, December.
    2. Dani Gamerman & Thiago Rezende Santos & Glaura C. Franco, 2013. "A Non-Gaussian Family Of State-Space Models With Exact Marginal Likelihood," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 625-645, November.
    3. Marco Bonetti & Ugofilippo Basellini, 2021. "Epilocal: A real-time tool for local epidemic monitoring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(12), pages 307-332.
    4. Marco Bonetti & Ugofilippo Basellini, 2020. "Epilocal: a real-time tool for local epidemic monitoring," Working Papers axhbndayuclqnee2wf7y, French Institute for Demographic Studies.
    5. Daniel Adyro Martínez-Bello & Antonio López-Quílez & Alexander Torres-Prieto, 2017. "Bayesian dynamic modeling of time series of dengue disease case counts," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(7), pages 1-19, July.
    6. Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
    7. Duncan Lee & Gavin Shaddick, 2007. "Time-Varying Coefficient Models for the Analysis of Air Pollution and Health Outcome Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1253-1261, December.
    8. Vurukonda Sathish & Siuli Mukhopadhyay & Rashmi Tiwari, 2022. "Autoregressive and moving average models for zero‐inflated count time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 190-218, May.

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