[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v71y2022i5p1266-1281.html
   My bibliography  Save this article

Nowcasting COVID‐19 deaths in England by age and region

Author

Listed:
  • Shaun R. Seaman
  • Pantelis Samartsidis
  • Meaghan Kall
  • Daniela De Angelis
Abstract
Understanding the trajectory of the daily number of COVID‐19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting‐day effects and longer‐term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1266-1281
    DOI: 10.1111/rssc.12576
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12576
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12576?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. Wood, Simon N., 2016. "Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i07).
    2. 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.
    3. 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.
    4. Sarah F McGough & Michael A Johansson & Marc Lipsitch & Nicolas A Menzies, 2020. "Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-20, April.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Oliver Stoner & Gavin Shaddick & Theo Economou & Sophie Gumy & Jessica Lewis & Itzel Lucio & Giulia Ruggeri & Heather Adair‐Rohani, 2020. "Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 815-839, August.
    7. Radka Jersakova & James Lomax & James Hetherington & Brieuc Lehmann & George Nicholson & Mark Briers & Chris Holmes, 2022. "Bayesian imputation of COVID‐19 positive test counts for nowcasting under reporting lag," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 834-860, August.
    8. 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.
    9. 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.
    10. Adam Altmejd & Joacim Rocklöv & Jonas Wallin, 2023. "Nowcasting COVID-19 Statistics Reported with Delay: A Case-Study of Sweden and the UK," IJERPH, MDPI, vol. 20(4), pages 1-14, February.
    11. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
    12. Anna Vážná & Jana Vignerová & Marek Brabec & Jan Novák & Bohuslav Procházka & Antonín Gabera & Petr Sedlak, 2022. "Influence of COVID-19-Related Restrictions on the Prevalence of Overweight and Obese Czech Children," IJERPH, MDPI, vol. 19(19), pages 1-14, September.
    13. 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.
    14. Tenglong Li & Laura F White, 2021. "Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-22, July.
    15. 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).
    16. Chen, Kefei & O'Leary, Rebecca A. & Evans, Fiona H., 2019. "A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool," Agricultural Systems, Elsevier, vol. 173(C), pages 140-150.
    17. Francesco Brizzi & Paul J. Birrell & Martyn T. Plummer & Peter Kirwan & Alison E. Brown & Valerie C. Delpech & O. Noel Gill & Daniela Angelis, 2019. "Extending Bayesian back-calculation to estimate age and time specific HIV incidence," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 757-780, October.
    18. Stoner, Oliver & Economou, Theo, 2020. "An advanced hidden Markov model for hourly rainfall time series," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).

    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:71:y:2022:i:5:p:1266-1281. 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.