Assessing the potential impact of transmission during prolonged viral shedding on the effect of lockdown relaxation on COVID-19

  1. Burcu Tepekule
  2. Anthony Hauser
  3. Viacheslav N. Kachalov
  4. Sara Andresen
  5. Thomas Scheier
  6. Peter W. Schreiber
  7. Huldrych F. Günthard
  8. Roger D. Kouyos

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Abstract

A key parameter in epidemiological modeling which characterizes the spread of an infectious disease is the generation time, or more generally the distribution of infectiousness as a function of time since infection. There is increasing evidence supporting a prolonged viral shedding window for COVID-19, but the transmissibility in this phase is unclear. Based on this, we develop a generalized Susceptible-Exposed-Infected-Resistant (SEIR) model including an additional compartment of chronically infected individuals who can stay infectious for a longer duration than the reported generation time, but with infectivity reduced to varying degrees. Using the incidence and fatality data from different countries, we first show that such an assumption also yields a plausible model in explaining the data observed prior to the easing of the lockdown measures (relaxation). We then test the predictive power of this model for different durations and levels of prolonged infectiousness using the incidence data after the introduction of relaxation in Switzerland, and compare it with a model without the chronically infected population to represent the models conventionally used. We show that in case of a gradual easing on the lockdown measures, the predictions of the model including the chronically infected population vary considerably from those obtained under a model in which prolonged infectiousness is not taken into account. Although the existence of a chronically infected population still remains largely hypothetical, we believe that our results provide tentative evidence to consider a chronically infected population as an alternative modeling approach to better interpret the transmission dynamics of COVID-19.

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  1. Version published to 10.1371/journal.pcbi.1008609
  2. ScreenIT

    SciScore for 10.1101/2020.06.12.20129213: (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

    No key resources detected.

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Using simplified compartmental models such as the one in this study has certain limitations. First, it does not consider the stochastic effects that the system is subject to, which become more important as the number of infecteds decrease in the community. Second, it assumes a well-mixed population, and does not consider the contact structure and the demographic information which are both relevant to the disease spread. Nevertheless, we believe that these two drawbacks of our modeling approach influence the models with and without the prolonged viral shedding to a similar degree, if not penalizing the model with prolonged viral shedding for producing more pessimistic projections since the number of infecteds will be higher in frequency relative to the null model. It is still debated whether the patients who recover from COVID-19 and test positive for the virus after their recovery are still infectious or not. Nevertheless, it is clear that these positive test results contribute to the data on the number of daily confirmed cases. However, current modeling studies regarding COVID-19 neglect this fact and assume that all positive test results are recorded within the duration of the serial interval only. Our results show that this assumption might lead to an underestimation of both the reproduction number and the effect of the lockdown, leading to a potential underprediction for the prospective data. In conclusion, It is not possible to either prove or disprove the existence of a...

    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.
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    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.06.12.20129213v1 on medRxiv