IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0269306.html

Estimating the basic reproduction number at the beginning of an outbreak

Author

Listed:
  • Sawitree Boonpatcharanon
  • Jane M Heffernan
  • Hanna Jankowski

Abstract

We compare several popular methods of estimating the basic reproduction number, R0, focusing on the early stages of an epidemic, and assuming weekly reports of new infecteds. We study the situation when data is generated by one of three standard epidemiological compartmental models: SIR, SEIR, and SEAIR; and examine the sensitivity of the estimators to the model structure. As some methods are developed assuming specific epidemiological models, our work adds a study of their performance in both a well-specified (data generating model and method model are the same) and miss-specified (data generating model and method model differ) settings. We also study R0 estimation using Canadian COVID-19 case report data. In this study we focus on examples of influenza and COVID-19, though the general approach is easily extendable to other scenarios. Our simulation study reveals that some estimation methods tend to work better than others, however, no singular best method was clearly detected. In the discussion, we provide recommendations for practitioners based on our results.

Suggested Citation

  • Sawitree Boonpatcharanon & Jane M Heffernan & Hanna Jankowski, 2022. "Estimating the basic reproduction number at the beginning of an outbreak," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-24, June.
    • Handle: RePEc:plo:pone00:0269306
    DOI: 10.1371/journal.pone.0269306
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269306
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0269306&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0269306?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. P. D. O’Neill & G. O. Roberts, 1999. "Bayesian inference for partially observed stochastic epidemics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 121-129.
    2. King, Aaron A. & Nguyen, Dao & Ionides, Edward L., 2016. "Statistical Inference for Partially Observed Markov Processes via the R Package pomp," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i12).
    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. Kim, Suhyeon & Park, Junpyo, 2025. "A double-edged aspect of basin entropy for predicting biodiversity in spatial rock–paper–scissors games," Chaos, Solitons & Fractals, Elsevier, vol. 197(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. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    2. Hamid Maroufy & Abdelati Lagzini, 2025. "Bayesian Inference for SIS Type Epidemic Model by Skellam’s Distribution with Real Application to COVID-19," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(3), pages 657-682, December.
    3. Guido España & Zulma M Cucunubá & Hernando Diaz & Sean Cavany & Nelson Castañeda & Laura Rodriguez, 2022. "Designing school reopening in the COVID-19 pre-vaccination period in Bogotá, Colombia: A modeling study," PLOS Global Public Health, Public Library of Science, vol. 2(6), pages 1-18, June.
    4. Helske, Jouni, 2017. "KFAS: Exponential Family State Space Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i10).
    5. Oscar García, 2019. "Estimating reducible stochastic differential equations by conversion to a least-squares problem," Computational Statistics, Springer, vol. 34(1), pages 23-46, March.
    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. repec:plo:pcbi00:1003587 is not listed on IDEAS
    8. Driver, Charles C. & Oud, Johan H. L. & Voelkle, Manuel C., 2017. "Continuous Time Structural Equation Modeling with R Package ctsem," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i05).
    9. Maria Masotti & Lin Zhang & Ethan Leng & Gregory J. Metzger & Joseph S. Koopmeiners, 2023. "A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI," Biometrics, The International Biometric Society, vol. 79(2), pages 604-615, June.
    10. Golightly, Andrew & Bradley, Emma & Lowe, Tom & Gillespie, Colin S., 2019. "Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 92-107.
    11. Teague R. Henry & Lindley R. Slipetz & Ami Falk & Jiaxing Qiu & Meng Chen, 2024. "Ordinal Outcome State-Space Models for Intensive Longitudinal Data," Psychometrika, Springer;The Psychometric Society, vol. 89(4), pages 1203-1229, December.
    12. Quentin Clairon & Adeline Samson, 2020. "Optimal control for estimation in partially observed elliptic and hypoelliptic linear stochastic differential equations," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 105-127, April.
    13. Xiang, Fei & Neal, Peter, 2014. "Efficient MCMC for temporal epidemics via parameter reduction," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 240-250.
    14. King, Aaron A. & Lin, Qianying & Ionides, Edward L., 2022. "Markov genealogy processes," Theoretical Population Biology, Elsevier, vol. 143(C), pages 77-91.
    15. Annemarie Bouma & Ivo Claassen & Ketut Natih & Don Klinkenberg & Christl A Donnelly & Guus Koch & Michiel van Boven, 2009. "Estimation of Transmission Parameters of H5N1 Avian Influenza Virus in Chickens," PLOS Pathogens, Public Library of Science, vol. 5(1), pages 1-13, January.
    16. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CESifo Working Paper Series 8977, CESifo.
    17. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.
    18. repec:plo:pcbi00:1005642 is not listed on IDEAS
    19. Elizabeth Goult & Laura Andrea Barrero Guevara & Michael Briga & Matthieu Domenech de Cellès, 2024. "Estimating the optimal age for infant measles vaccination," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    20. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.
    21. Ross, J.V., 2012. "On parameter estimation in population models III: Time-inhomogeneous processes and observation error," Theoretical Population Biology, Elsevier, vol. 82(1), pages 1-17.
    22. Sarah C. Kramer & Sarah Pirikahu & Jean-Sébastien Casalegno & Matthieu Domenech de Cellès, 2024. "Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    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:plo:pone00:0269306. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.