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Carroll C, Bhattacharjee S, Chen Y, et al. Time dynamics of COVID-19. Sci Rep. 2020;10(1):21040doi: 10.1038/s41598-020-77709-4.
Carroll, C., Bhattacharjee, S., Chen, Y., Dubey, P., Fan, J., Gajardo, �. �., Zhou, X., Müller, H. G., & Wang, J. L. (2020). Time dynamics of COVID-19. Scientific reports, 10(1), 21040. https://doi.org/10.1038/s41598-020-77709-4
Carroll, Cody, et al. "Time dynamics of COVID-19." Scientific reports vol. 10,1 (2020): 21040. doi: https://doi.org/10.1038/s41598-020-77709-4
Carroll C, Bhattacharjee S, Chen Y, Dubey P, Fan J, Gajardo Á, Zhou X, Müller HG, Wang JL. Time dynamics of COVID-19. Sci Rep. 2020 Dec 03;10(1):21040. doi: 10.1038/s41598-020-77709-4. PMID: 33273598; PMCID: PMC7712909.
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Sci Rep. 2020 Dec 03;10(1):21040. doi: 10.1038/s41598-020-77709-4.

Time dynamics of COVID-19.

Scientific reports

Cody Carroll, Satarupa Bhattacharjee, Yaqing Chen, Paromita Dubey, Jianing Fan, Álvaro Gajardo, Xiner Zhou, Hans-Georg Müller, Jane-Ling Wang

Affiliations

  1. Department of Statistics, University of California, Davis, Davis, CA, 95616, USA.
  2. Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
  3. Department of Statistics, University of California, Davis, Davis, CA, 95616, USA. [email protected].

PMID: 33273598 PMCID: PMC7712909 DOI: 10.1038/s41598-020-77709-4

Abstract

We apply tools from functional data analysis to model cumulative trajectories of COVID-19 cases across countries, establishing a framework for quantifying and comparing cases and deaths across countries longitudinally. It emerges that a country's trajectory during an initial first month "priming period" largely determines how the situation unfolds subsequently. We also propose a method for forecasting case counts, which takes advantage of the common, latent information in the entire sample of curves, instead of just the history of a single country. Our framework facilitates to quantify the effects of demographic covariates and social mobility on doubling rates and case fatality rates through a time-varying regression model. Decreased workplace mobility is associated with lower doubling rates with a roughly 2 week delay, and case fatality rates exhibit a positive feedback pattern.

References

  1. Math Biosci Eng. 2020 May 21;17(4):3710-3720 - PubMed
  2. Science. 2020 May 8;368(6491):638-642 - PubMed
  3. Stat (Int Stat Inst). 2020 Jul 13;:e302 - PubMed
  4. J Gerontol A Biol Sci Med Sci. 2020 Sep 16;75(9):e30-e33 - PubMed
  5. Infect Control Hosp Epidemiol. 2020 Jul;41(7):857-859 - PubMed
  6. J Travel Med. 2020 May 18;27(3): - PubMed
  7. Biometrics. 2016 Sep;72(3):846-54 - PubMed
  8. Cell. 2020 Apr 16;181(2):271-280.e8 - PubMed
  9. J Clin Med. 2020 Mar 31;9(4): - PubMed
  10. PLoS One. 2020 Mar 31;15(3):e0231236 - PubMed
  11. J Med Virol. 2020 Jul;92(7):863-867 - PubMed
  12. Lancet Infect Dis. 2014 Jan;14(1):50-56 - PubMed
  13. Osong Public Health Res Perspect. 2020 Apr;11(2):74-80 - PubMed
  14. PLoS One. 2018 Nov 12;13(11):e0207073 - PubMed
  15. PLoS One. 2020 Mar 31;15(3):e0230405 - PubMed
  16. Emerg Infect Dis. 2020 May;26(5):1049-1052 - PubMed
  17. Lancet Infect Dis. 2020 May;20(5):553-558 - PubMed

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