Theoretical investigation of pre-symptomatic SARS-CoV-2 person-to-person transmission in households

  1. Yehuda Arav
  2. Ziv Klausner
  3. Eyal Fattal

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Abstract

Since its emergence, the phenomenon of SARS-CoV-2 transmission by seemingly healthy individuals has become a major challenge in the effort to achieve control of the pandemic. Identifying the modes of transmission that drive this phenomenon is a perquisite in devising effective control measures, but to date it is still under debate. To address this problem, we have formulated a detailed mathematical model of discrete human actions (such as coughs, sneezes, and touching) and the continuous decay of the virus in the environment. To take into account those discrete and continuous events we have extended the common modelling approach and employed a hybrid stochastic mathematical framework. This allowed us to calculate higher order statistics which are crucial for the reconstruction of the observed distributions. We focused on transmission within a household, the venue with the highest risk of infection and validated the model results against the observed secondary attack rate and the serial interval distribution. Detailed analysis of the model results identified the dominant driver of pre-symptomatic transmission as the contact route via hand-face transfer and showed that wearing masks and avoiding physical contact are an effective prevention strategy. These results provide a sound scientific basis to the present recommendations of the WHO and the CDC.

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  1. Version published to 10.1038/s41598-021-93579-w
  2. Version published to 10.1101/2020.05.12.20099085v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.05.12.20099085: (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
    The model was implemented in python version 3.6.5.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our analysis, as with all modeling exercises, has several limitations and requires certain assumptions. The model does not account for contact patterns that prevail in households with young children and does not take into account the diurnal cycle of activity. The model parameters, such as the dose response curve, the viral shedding coefficients and transfer coefficients were chosen on the basis of knowledge of the SARS, other strains of coronavirus, or other bacteria26, 39. Although the model is stable with regard to variations in these parameters, more information on the key characteristic of the disease may reduce some uncertainties. In conclusion, our findings can provide an important tool for decision makers while advising the public of the HBMs that are necessary to impede the progression of the epidemic. As it seems, recurrent outbreaks are expected to occur, as many countries will have to establish a fine balance between posing restrictions on society and allowing citizens to lead their life as normally as possible40. Under such a reality, the model presented in this study can be used to quantify the contribution of different HBM measures in order to devise guidelines that mitigate the risk of infection in scenarios of workplaces or schools, sports and cultural events, and mass transportation.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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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  4. Version published to 10.1101/2020.05.12.20099085v1 on medRxiv