IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v185y2022is1ps65-s85.html
   My bibliography  Save this article

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

Author

Listed:
  • Yee Whye Teh
  • Bryn Elesedy
  • Bobby He
  • Michael Hutchinson
  • Sheheryar Zaidi
  • Avishkar Bhoopchand
  • Ulrich Paquet
  • Nenad Tomasev
  • Jonathan Read
  • Peter J. Diggle

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssa:v:185:y:2022:i:s1:p:s65-s85
    DOI: 10.1111/rssa.12971
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12971
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rssa.12971?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. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Xiangrong & Hou, Hongru & Lu, Dan & Wu, Zongze & Moreno, Yamir, 2024. "Unveiling the reproduction number scaling in characterizing social contagion coverage," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).

    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. Amanda C. Perofsky & Chelsea L. Hansen & Roy Burstein & Shanda Boyle & Robin Prentice & Cooper Marshall & David Reinhart & Ben Capodanno & Melissa Truong & Kristen Schwabe-Fry & Kayla Kuchta & Brian P, 2024. "Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Massimo Bilancia & Domenico Vitale & Fabio Manca & Paola Perchinunno & Luigi Santacroce, 2024. "A dynamic causal modeling of the second outbreak of COVID-19 in Italy," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(1), pages 1-30, March.
    3. 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.
    4. Carl P. Schmertmann & Marcos R. Gonzaga, 2018. "Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas With Defective Vital Records," Demography, Springer;Population Association of America (PAA), vol. 55(4), pages 1363-1388, August.
    5. Jose Pina-Sánchez & John Paul Gosling, 2020. "Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(3), pages 1047-1073, June.
    6. Sally Paganin & Christopher J. Paciorek & Claudia Wehrhahn & Abel Rodríguez & Sophia Rabe-Hesketh & Perry de Valpine, 2023. "Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models," Journal of Educational and Behavioral Statistics, , vol. 48(2), pages 147-188, April.
    7. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    8. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    9. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    10. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," Papers 2012.14503, arXiv.org.
    11. 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.
    12. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    13. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    14. Matthias Trendtel & Alexander Robitzsch, 2021. "A Bayesian Item Response Model for Examining Item Position Effects in Complex Survey Data," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 34-57, February.
    15. Spilker Finn & Ötting Marius, 2024. "No cheering in the background? Individual performance in professional darts during COVID-19," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 20(3), pages 219-234.
    16. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    17. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    18. Jared Coopersmith & Thomas D. Cook & Jelena Zurovac & Duncan Chaplin & Lauren V. Forrow, 2022. "Internal And External Validity Of The Comparative Interrupted Time‐Series Design: A Meta‐Analysis," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(1), pages 252-277, January.
    19. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.
    20. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.

    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:s65-s85. 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.