IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v185y2022is1ps55-s60.html
   My bibliography  Save this article

Authors’ reply to the discussion of ‘Are epidemic growth rates more informative than reproduction numbers?’ by Parag et al. in Session 1 of the Royal Statistical Society’s Special Topic Meeting on COVID‐19 transmission: 9 June 2021

Author

Listed:
  • Kris V. Parag
  • Robin N. Thompson
  • Christl A. Donnelly

Abstract

No abstract is available for this item.

Suggested Citation

  • Kris V. Parag & Robin N. Thompson & Christl A. Donnelly, 2022. "Authors’ reply to the discussion of ‘Are epidemic growth rates more informative than reproduction numbers?’ by Parag et al. in Session 1 of the Royal Statistical Society’s Special Topic Meeting on COV," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 55-60, November.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:s1:p:s55-s60
    DOI: 10.1111/rssa.12892
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12892
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12892?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. Katelyn M Gostic & Lauren McGough & Edward B Baskerville & Sam Abbott & Keya Joshi & Christine Tedijanto & Rebecca Kahn & Rene Niehus & James A Hay & Pablo M De Salazar & Joel Hellewell & Sophie Meaki, 2020. "Practical considerations for measuring the effective reproductive number, Rt," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-21, December.
    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. Aldo Carranza & Marcel Goic & Eduardo Lara & Marcelo Olivares & Gabriel Y. Weintraub & Julio Covarrubia & Cristian Escobedo & Natalia Jara & Leonardo J. Basso, 2022. "The Social Divide of Social Distancing: Shelter-in-Place Behavior in Santiago During the Covid-19 Pandemic," Management Science, INFORMS, vol. 68(3), pages 2016-2027, March.
    2. 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.
    3. 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.
    4. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    5. Palatella, Luigi & Vanni, Fabio & Lambert, David, 2021. "A phenomenological estimate of the true scale of CoViD-19 from primary data," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    6. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    7. Diana Rose E. Ranoa & Robin L. Holland & Fadi G. Alnaji & Kelsie J. Green & Leyi Wang & Richard L. Fredrickson & Tong Wang & George N. Wong & Johnny Uelmen & Sergei Maslov & Zachary J. Weiner & Alexei, 2022. "Mitigation of SARS-CoV-2 transmission at a large public university," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    8. Bradley S Price & Maryam Khodaverdi & Adam Halasz & Brian Hendricks & Wesley Kimble & Gordon S Smith & Sally L Hodder, 2021. "Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-16, November.
    9. Sabah Bushaj & Xuecheng Yin & Arjeta Beqiri & Donald Andrews & İ. Esra Büyüktahtakın, 2023. "A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization," Annals of Operations Research, Springer, vol. 328(1), pages 245-277, September.
    10. Mohammad Reza Davahli & Krzysztof Fiok & Waldemar Karwowski & Awad M. Aljuaid & Redha Taiar, 2021. "Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks," IJERPH, MDPI, vol. 18(7), pages 1-12, April.
    11. Yee Whye Teh & Bryn Elesedy & Bobby He & Michael Hutchinson & Sheheryar Zaidi & Avishkar Bhoopchand & Ulrich Paquet & Nenad Tomasev & Jonathan Read & Peter J. Diggle, 2022. "Efficient Bayesian inference of instantaneous reproduction numbers at fine spatial scales, with an application to mapping and nowcasting the Covid‐19 epidemic in British local authorities," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 65-85, November.
    12. Yun Lin & Bingyi Yang & Sarah Cobey & Eric H. Y. Lau & Dillon C. Adam & Jessica Y. Wong & Helen S. Bond & Justin K. Cheung & Faith Ho & Huizhi Gao & Sheikh Taslim Ali & Nancy H. L. Leung & Tim K. Tsan, 2022. "Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    13. Publio Darío Cortés-Carvajal & Mitzi Cubilla-Montilla & David Ricardo González-Cortés, 2022. "Estimation of the Instantaneous Reproduction Number and Its Confidence Interval for Modeling the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(2), pages 1-30, January.
    14. Kimberly M. Fornace & Hillary M. Topazian & Isobel Routledge & Syafie Asyraf & Jenarun Jelip & Kim A. Lindblade & Mohammad Saffree Jeffree & Pablo Ruiz Cuenca & Samir Bhatt & Kamruddin Ahmed & Azra C., 2023. "No evidence of sustained nonzoonotic Plasmodium knowlesi transmission in Malaysia from modelling malaria case data," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    15. Dongxuan Chen & Yiu-Chung Lau & Xiao-Ke Xu & Lin Wang & Zhanwei Du & Tim K. Tsang & Peng Wu & Eric H. Y. Lau & Jacco Wallinga & Benjamin J. Cowling & Sheikh Taslim Ali, 2022. "Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    16. Joe Meagher & Nial Friel, 2022. "Assessing epidemic curves for evidence of superspreading," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2179-2202, October.
    17. Lorenzo Pellis & Paul J. Birrell & Joshua Blake & Christopher E. Overton & Francesca Scarabel & Helena B. Stage & Ellen Brooks‐Pollock & Leon Danon & Ian Hall & Thomas A. House & Matt J. Keeling & Jon, 2022. "Estimation of reproduction numbers in real time: Conceptual and statistical challenges," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 112-130, November.
    18. Haogao Gu & Ruopeng Xie & Dillon C. Adam & Joseph L.-H. Tsui & Daniel K. Chu & Lydia D. J. Chang & Sammi S. Y. Cheuk & Shreya Gurung & Pavithra Krishnan & Daisy Y. M. Ng & Gigi Y. Z. Liu & Carrie K. C, 2022. "Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    19. Chang, Joseph T. & Kaplan, Edward H., 2023. "Modeling local coronavirus outbreaks," European Journal of Operational Research, Elsevier, vol. 304(1), pages 57-68.
    20. Nicholas P. Jewell & Joseph A. Lewnard, 2022. "On the use of the reproduction number for SARS‐CoV‐2: Estimation, misinterpretations and relationships with other ecological measures," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 16-27, November.

    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:jorssa:v:185:y:2022:i:s1:p:s55-s60. 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.