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A reconstruction of early cryptic COVID spread

To respond better to future pandemics, we must understand how the SARS-CoV-2 virus dispersed so rapidly. A model of COVID-19 spread sheds light on cryptic transmission, undetected by surveillance efforts, in early 2020.
By
  1. Simon Cauchemez

    1. Simon Cauchemez is in the Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, University of Paris, UMR 2000, Paris 75015, France.

  2. Paolo Bosetti

    1. Paolo Bosetti is in the Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, University of Paris, UMR 2000, Paris 75015, France.

Over the past 20 months, the COVID-19 pandemic has caused more than 4.9 million reported deaths (https://coronavirus.jhu.edu), and measures to limit the spread of the SARS-CoV-2 virus have affected the lives of people around the world. Although modelling has helped to reconstruct the early dynamics of the epidemic in some countries, we still lack a coherent picture of how the pandemic unfolded globally. Writing in Nature, Davis et al.1 use a worldwide model to assess early, cryptic transmission of SARS-CoV-2 — the spread of the virus that was not detected by initial surveillance efforts — in the United States and Europe.

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Nature 600, 40-41 (2021)

doi: https://doi.org/10.1038/d41586-021-02989-3

References

  1. Davis, J. T. et al. Nature 600, 127–132 (2021).

    Article  Google Scholar 

  2. Balcan, D. et al. J. Comput. Sci. 1, 132–145 (2010).

    Article  PubMed  Google Scholar 

  3. Fauver, J. R. et al. Cell 181, 990–996 (2020).

    Article  PubMed  Google Scholar 

  4. du Plessis, L. et al. Science 371, 708–712 (2021).

    Article  PubMed  Google Scholar 

  5. CDC Stands Up New Disease Forecasting Center. https://www.cdc.gov/media/releases/2021/p0818-disease-forecasting-center.html (CDC, 2021).

  6. Hale, T. et al. Nature Hum. Behav. 5, 529–538 (2021).

    Article  PubMed  Google Scholar 

  7. Hadfield, J. et al. Bioinformatics 34, 4121–4123 (2018).

    Article  PubMed  Google Scholar 

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Competing Interests

The PhD supervisor of P.B. was Stefano Merler.

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