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Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011

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  • Michael Höhle
  • Matthias an der Heiden

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  • Michael Höhle & Matthias an der Heiden, 2014. "Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011," Biometrics, The International Biometric Society, vol. 70(4), pages 993-1002, December.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:4:p:993-1002
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    File URL: http://hdl.handle.net/10.1111/biom.12194
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    References listed on IDEAS

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    1. England, P.D. & Verrall, R.J., 2002. "Stochastic Claims Reserving in General Insurance," British Actuarial Journal, Cambridge University Press, vol. 8(3), pages 443-518, August.
    2. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    3. Douglas N. Midthune & Michael P. Fay & Limin X. Clegg & Eric J. Feuer, 2005. "Modeling Reporting Delays and Reporting Corrections in Cancer Registry Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 61-70, March.
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    1. Salmon, Maëlle & Schumacher, Dirk & Höhle, Michael, 2016. "Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i10).
    2. Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held, 2022. "Session 3 of the RSS Special Topic Meeting on Covid‐19 Transmission: Replies to the discussion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 158-164, November.
    3. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    4. Angela Noufaily & Paddy Farrington & Paul Garthwaite & Doyo Gragn Enki & Nick Andrews & Andre Charlett, 2016. "Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 488-499, April.
    5. Shaun R. Seaman & Pantelis Samartsidis & Meaghan Kall & Daniela De Angelis, 2022. "Nowcasting COVID‐19 deaths in England by age and region," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1266-1281, November.
    6. Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2018. "A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases," IJERPH, MDPI, vol. 15(8), pages 1-13, August.
    7. Oliver Stoner & Alba Halliday & Theo Economou, 2023. "Correcting delayed reporting of COVID‐19 using the generalized‐Dirichlet‐multinomial method," Biometrics, The International Biometric Society, vol. 79(3), pages 2537-2550, September.
    8. Oliver Stoner & Theo Economou, 2020. "Multivariate hierarchical frameworks for modeling delayed reporting in count data," Biometrics, The International Biometric Society, vol. 76(3), pages 789-798, September.
    9. Reese Richardson & Emile Jorgensen & Philip Arevalo & Tobias M. Holden & Katelyn M. Gostic & Massimo Pacilli & Isaac Ghinai & Shannon Lightner & Sarah Cobey & Jaline Gerardin, 2022. "Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    10. Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2019. "Addendum: Wang et al. A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases. Int. J. Environ. Res. Public Health , 2018, 15(8):1740; doi:10.3390/ijerph15081740," IJERPH, MDPI, vol. 16(8), pages 1-3, April.

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