Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates

  1. Rhys P.D. Inward
  2. Felix Jackson
  3. Abhishek Dasgupta
  4. Graham Lee
  5. Anya Lindström Battle
  6. Kris V. Parag
  7. Moritz U.G. Kraemer

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Abstract

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  1. Version published to 10.1016/j.epidem.2022.100627
  2. Version published to 10.1101/2022.03.31.22273230v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2022.03.31.22273230: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Posterior samples of the parameters are generated using Hamiltonian Monte Carlo (HMC) (Hoffman and Gelman, 2014) in Stan (Carpenter et al., 2017) using PyStan (v.2.19.0.0: https://mc-stan.org/users/interfaces/pystan).
    PyStan
    suggested: None
    Correlation analysis: Spearman’s rank-order correlation coefficient (rs) was calculated for delays between symptom-onset-to-confirmation, -hospitalisation and -death as well as hospitalisation-to-death for each state, using the scipy.stats ‘spearmanr’ function (scipy version 1.7.3).
    scipy
    suggested: (SciPy, RRID:SCR_008058)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    While our results provide a rigorous underpinning and insight into delay distributions and impact of these on epidemiological parameters estimation, we acknowledge several limitations. The Global.health database which contains line lists that our distributions have been estimated from, though extensive, contains typing errors, and the degree to which these bias our estimates are unknown. Our data ingestion pipeline is mostly automated and only occasionally are we able to manually verify the accuracy of the data. Further, when comparing line list data between and within countries we note disparities in notification systems and differences in case definitions. Further work should evaluate the demographic biases in these data and how that may affect transmission dynamics (longer delays for less severe cases in younger age groups may impact transmission substantially). Lastly, there is a low testing rate for the countries analysed (Hasell et al., 2020) and heterogeneities in testing rates in both time and space (Vandenberg et al., 2021) which can influence the results for both cases and rt. Future epidemiological work is needed to compare parameters estimated from case data, death data and excess death data across different settings (Gostic et al., 2020) and more intensive monitoring and/or the use of alternative data sources such as genomic data (Inward, Faria and Parag, 2022) is needed to improve the reliability of estimations. Few countries report highly detailed epidemiologic...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  4. Version published to 10.1101/2022.03.31.22273230v1 on medRxiv