Explaining the “Bomb-Like” Dynamics of COVID-19 with Modeling and the Implications for Policy

  1. Gary Lin
  2. Alexandra T. Strauss
  3. Maxwell Pinz
  4. Diego A. Martinez
  5. Katie K. Tseng
  6. Emily Schueller
  7. Oliver Gatalo
  8. Yupeng Yang
  9. Simon A. Levin
  10. Eili Y. Klein
  11. For the CDC MInD-Healthcare Program

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Abstract

Using a Bayesian approach to epidemiological compartmental modeling, we demonstrate the “bomb-like” behavior of exponential growth in COVID-19 cases can be explained by transmission of asymptomatic and mild cases that are typically unreported at the beginning of pandemic events due to lower prevalence of testing. We studied the exponential phase of the pandemic in Italy, Spain, and South Korea, and found the R 0 to be 2.56 (95% CrI, 2.41-2.71), 3.23 (95% CrI, 3.06-3.4), and 2.36 (95% CrI, 2.22-2.5) if we use Bayesian priors that assume a large portion of cases are not detected. Weaker priors regarding the detection rate resulted in R 0 values of 9.22 (95% CrI, 9.01-9.43), 9.14 (95% CrI, 8.99-9.29), and 8.06 (95% CrI, 7.82-8.3) and assumes nearly 90% of infected patients are identified. Given the mounting evidence that potentially large fractions of the population are asymptomatic, the weaker priors that generate the high R 0 values to fit the data required assumptions about the epidemiology of COVID-19 that do not fit with the biology, particularly regarding the timeframe that people remain infectious. Our results suggest that models of transmission assuming a relatively lower R 0 value that do not consider a large number of asymptomatic cases can result in misunderstanding of the underlying dynamics, leading to poor policy decisions and outcomes.

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  1. Version published to 10.1101/2020.04.05.20054338v2 on medRxiv
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    SciScore for 10.1101/2020.04.05.20054338: (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
    Biological Parameters and Prior Beliefs: In order to assess fit, determine model parameter priors, and provide context for the analysis, we conducted a literature search of the biology and transmission of SARS-CoV-2 using peer-reviewed literature and non-peer-reviewed literature available on pre-print servers medRxiv, bioRxiv and SSRN’s First Look (Table 1).
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    As with most COVID-19 modeling studies, the limitations of epidemiological models have been constrained by data and testing quality regarding the true prevalence of SARS-CoV-2 infections. We assumed for the beginning stages of the pandemic, the confirmed cases only reflected cases that were mostly moderate and severely symptomatic, while asymptomatic and mild cases were mostly overlooked due to the limited supply of testing kits. The uncertainty of these testing differences was implicitly captured in the Bayesian framework. The population size for each country are simplified and assumed to be static. Nevertheless, the parsimonious model provides a conservative estimate of undetected cases since traveling individuals make up a negligible proportion population, and if these travelers are infected and undetected in early stages of the pandemic, it will further support our claim. Furthermore, the estimation of transmission is assumed to be homogenous population mixing, while in reality contact networks are heterogeneous with varying contact patterns.

    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.

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  3. Version published to 10.1101/2020.04.05.20054338v1 on medRxiv