A COVID-19 transmission model informing medication development and supply chain needs

  1. Annabelle Lemenuel-Diot
  2. Barry Clinch
  3. Aeron C. Hurt
  4. Paul Boutry
  5. Johann Laurent
  6. Mathias Leddin
  7. Stefan Frings
  8. Jean Eric Charoin

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Abstract

Accurate prediction of COVID-19 cases can optimize clinical trial recruitment, inform mitigation strategies and facilitate rapid medication development. Here we present a country-specific, modified Susceptible, Exposed, Infectious, Removed (SEIR) model of SARS-CoV-2 transmission using data from the Johns Hopkins University COVID-19 Dashboard. Inter-country differences in initial exposure, cultural/environmental factors, reporting requirements and stringency of mitigation strategies were incorporated. Asymptomatic patients and super-spreaders were also factored into our model. Using these data, our model estimated 65.8% of cases as asymptomatic; symptomatic and asymptomatic people were estimated to infect 2.12 and 5.83 other people, respectively. An estimated 9.55% of cases were super-spreaders with a 2.11-fold higher transmission rate than average. Our model estimated a mean maximum infection rate of 0.927 cases/day (inter-country range, 0.63–1.41) without mitigation strategies. Mitigation strategies with a stringency index value of ≥60% were estimated to be required to reduce the reproduction ratio below 1. It was predicted that cases over the next 2 months would differ between countries, with certain countries likely to experience an accelerated accumulation of cases. Together, results from our model can guide distribution of diagnostic tests, impact clinical trial development, support medication development and distribution and inform mitigation strategies to reduce COVID-19 spread.

Key Findings

  • Predicting COVID-19 cases can inform medication development and mitigation strategies

  • We created a modified SEIR model of SARS-CoV-2 transmission

  • We integrated asymptomatic cases, super-spreaders and hotspots that drive viral spread

  • Mitigation strategies with a stringency index of ≥60% are required to reduce the RR below 1

  • Some countries may experience an accelerated accumulation of cases in the coming months

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  1. ScreenIT

    SciScore for 10.1101/2020.11.23.20237404: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our model does have certain limitations. First, it was based on observed infected cases. Given that some patients are asymptomatic or show only mild symptoms, however, it is likely that the true number of cases is higher than captured here. In addition, changes in the reporting rate because of local testing policies and potential seasonality differences are not incorporated or investigated in this model. As data on these factors are gathered over time, it may be possible to integrate them into our model to support future forecasting. Nevertheless, it is worth noting that this model has remained relatively stable since September 2020, with bi-weekly updates only minimally impacting the forecasting results. Finally, the model does not account for the availability of a vaccine. When an efficacious vaccine is widely available, our model must be adjusted to account for the reduced size of the susceptible population and the limited possibility of transmission. As with all predictive models, additional data on each of these factors will improve our understanding of SARS-CoV-2 transmission; integrating these data into our model can support more accurate forecasting in the future. In summary, our model can support and inform the development of clinical trials and the supply and distribution of future medications. By updating and adjusting the model as new data are received, our model could potentially inform longer-term considerations as well. Finally, our model also represents a poss...

    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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  2. Version published to 10.1101/2020.11.23.20237404v1 on medRxiv