A new model of unreported COVID-19 cases outperforms three known epidemic-growth models in describing data from Cuba and Spain

  1. Erick E. Ramirez-Torres
  2. Antonio R. Selva Castañeda
  3. Luis Randez
  4. Luis E. Valdés García
  5. Luis E. Bergues Cabrales
  6. Scott A. Sisson
  7. Juan I. Montijano

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Abstract

Estimating the unreported cases of Covid-19 in a region/country is a complicated problem. We propose a new mathematical model that, combined with a deterministic model of the total growth of cases, describes the time evolution of the unreported cases for each reported Covid-19 case. The new model considers the growth of unreported cases in plateau periods and the decrease towards the end of an epidemic wave. We combined the new model with a Gompertz-growth model, a generalized logistic model, and a susceptible-infectious-removed (SIR) model; and fitted them via Bayesian methods to data from Cuba and Spain. The combined-model fits yielded better Bayesian-Information-Criterion values than the Gompertz, logistic, and SIR models alone. This suggests the new model can achieve improved descriptions of the evolution of a Covid-19 epidemic wave. The new model is also able to provide reliable predictions of the epidemic evolution in a short period of time. We include in the paper the steps that researchers should take to use the new model for predictions with other data.

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  1. Version published to 10.1101/2021.06.29.21259707v2 on medRxiv
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    SciScore for 10.1101/2021.06.29.21259707: (What is this?)

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

    Table 1: Rigor

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    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In the Bayesian inference carried out, the samples of the posterior distributions of the parameters were obtained using the Delayed-Rejection-Adaptive-Metropolis (DRAM) algorithm, implemented via the Python Package pymcmcstat [32], with 5 × 105 samples and burn-in number of 2.5 × 105.
    Python
    suggested: (IPython, RRID:SCR_001658)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The forecast limitations are directly related to the limitations of the chosen deterministic model. In our case, the deterministic models Gompertz and GRM allow predicting the end of a wave, but they do not predict peaks of active infections or epidemic rebounds. Nevertheless, the Gompertz+U model shows good results in making short-term predictions. In some trajectories drawn from posterior distributions of the new-model parameters, a growth of reported cases was observed towards the final days when the deterministic model already showed a plateau shape. This was not a reason to discard these regions in the analysis because we consider this phenomenon to be plausible, because sometimes samples are accumulated without being tested by real-time polymerase chain reaction, due to laboratory availability or priority of the authorities of one region over another. Some current limitations of the new model are: The influence of the third limitation is reduced in this work by imposing limits on initial conditions of the model. Despite the above, we consider that the new model is a powerful tool to complement the information that decision-makers need to establish the appropriate policies to contain Covid-19.

    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.

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  3. Version published to 10.1101/2021.06.29.21259707v1 on medRxiv