IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v78y2022i3p1127-1140.html
   My bibliography  Save this article

Inferring UK COVID‐19 fatal infection trajectories from daily mortality data: Were infections already in decline before the UK lockdowns?

Author

Listed:
  • Simon N. Wood

Abstract

The number of new infections per day is a key quantity for effective epidemic management. It can be estimated relatively directly by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen‐effective reproduction number, R, using data gathered from the clinical response to the disease. For coronavirus disease 2019 (Covid‐19/SARS‐Cov‐2), such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty, it is useful to retrospectively reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on first‐wave Covid‐19 deaths and the disease duration distribution suggests that fatal infections were in decline before full UK lockdown (24 March 2020), and that fatal infections in Sweden started to decline only a day or two later. An analysis of UK data using the model of Flaxman et al. gives the same result under relaxation of its prior assumptions on R, suggesting an enhanced role for non‐pharmaceutical interventions short of full lockdown in the UK context. Similar patterns appear to have occurred in the subsequent two lockdowns.

Suggested Citation

  • Simon N. Wood, 2022. "Inferring UK COVID‐19 fatal infection trajectories from daily mortality data: Were infections already in decline before the UK lockdowns?," Biometrics, The International Biometric Society, vol. 78(3), pages 1127-1140, September.
  • Handle: RePEc:bla:biomet:v:78:y:2022:i:3:p:1127-1140
    DOI: 10.1111/biom.13462
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13462
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.13462?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. Dietrich Domanski & Michela Scatigna & Anna Zabai, 2016. "Wealth inequality and monetary policy," BIS Quarterly Review, Bank for International Settlements, March.
    2. Simon N. Wood & Matteo Fasiolo, 2017. "A generalized Fellner‐Schall method for smoothing parameter optimization with application to Tweedie location, scale and shape models," Biometrics, The International Biometric Society, vol. 73(4), pages 1071-1081, December.
    3. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    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. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.

    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. Lambert, Philippe, 2021. "Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    2. 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.
    3. Kaiqiong Zhao & Karim Oualkacha & Lajmi Lakhal‐Chaieb & Aurélie Labbe & Kathleen Klein & Antonio Ciampi & Marie Hudson & Inés Colmegna & Tomi Pastinen & Tieyuan Zhang & Denise Daley & Celia M.T. Green, 2021. "A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation," Biometrics, The International Biometric Society, vol. 77(2), pages 424-438, June.
    4. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    5. Anastasios Evgenidis & Apostolos Fasianos, 2019. "Monetary Policy and Wealth Inequalities in Great Britain: Assessing the role of unconventional policies for a decade of household data," Papers 1912.09702, arXiv.org.
    6. Kuhn, Moritz & Bartscher, Alina & Schularick, Moritz & Wachtel, Paul, 2021. "Monetary policy and racial inequality," CEPR Discussion Papers 15734, C.E.P.R. Discussion Papers.
    7. Casiraghi, Marco & Gaiotti, Eugenio & Rodano, Lisa & Secchi, Alessandro, 2018. "A “reverse Robin Hood”? The distributional implications of non-standard monetary policy for Italian households," Journal of International Money and Finance, Elsevier, vol. 85(C), pages 215-235.
    8. Domonkos, Tomas & Fisera, Boris & Siranova, Maria, 2023. "Income inequality as long-term conditioning factor of monetary transmission to bank rates," Economic Modelling, Elsevier, vol. 128(C).
    9. Hohberger, Stefan & Priftis, Romanos & Vogel, Lukas, 2020. "The distributional effects of conventional monetary policy and quantitative easing: Evidence from an estimated DSGE model," Journal of Banking & Finance, Elsevier, vol. 113(C).
    10. Tarne, Ruben & Bezemer, Dirk & Theobald, Thomas, 2022. "The effect of borrower-specific loan-to-value policies on household debt, wealth inequality and consumption volatility: An agent-based analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    11. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    12. Sarah Goldman & Shouyi Zhang, 2022. "Quantitative Easing, Households’ Savings and Growth: A Luxembourgish Case Study," Economic Research Guardian, Weissberg Publishing, vol. 12(1), pages 45-54, June.
    13. Benjamin Säfken & Thomas Kneib, 2020. "Conditional covariance penalties for mixed models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 990-1010, September.
    14. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    15. Massimiliano Mazzanti & Antonio Musolesi, 2020. "Modeling Green Knowledge Production and Environmental Policies with Semiparametric Panel Data Regression models," SEEDS Working Papers 1420, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Sep 2020.
    16. Andrew Leroux & Junrui Di & Ekaterina Smirnova & Elizabeth J Mcguffey & Quy Cao & Elham Bayatmokhtari & Lucia Tabacu & Vadim Zipunnikov & Jacek K Urbanek & Ciprian Crainiceanu, 2019. "Organizing and Analyzing the Activity Data in NHANES," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 262-287, July.
    17. Claudio Borio & Boris Hofmann, 2017. "Is Monetary Policy Less Effective When Interest Rates Are Persistently Low?," RBA Annual Conference Volume (Discontinued), in: Jonathan Hambur & John Simon (ed.),Monetary Policy and Financial Stability in a World of Low Interest Rates, Reserve Bank of Australia.
    18. Yong Liu & Alan P. Ker, 2021. "Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 231-257, March.
    19. Stefano Cabras & J. D. Tena, 2023. "Implicit institutional incentives and individual decisions: Causal inference with deep learning models," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(6), pages 3739-3754, September.
    20. Hervé Cardot & Antonio Musolesi, 2018. "Modeling temporal treatment effects with zero inflated semi-parametric regression models: the case of local development policies in France," SEEDS Working Papers 0718, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Mar 2018.

    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:biomet:v:78:y:2022:i:3:p:1127-1140. 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.