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Abstract

Mathematical models for infectious disease epidemics often assume, explicitly or implicitly, an initial exponential growth for the number of new infections. Recent studies have highlighted that some historical epidemics actually grew sub-exponentially. Using models that presume exponential growth for such epidemics may not faithfully characterize the epidemiological parameters, especially the reproduction number. Here, using a well-established “generalized-growth” model, we derive analytical expressions of the time-dependent reproduction number and show that this quantity for epidemics with sub-exponential growth decreases and approaches unity over disease generation intervals. We use this theoretical framework to estimate the reproduction number for synthetic and real epidemics. Our findings suggest that estimates of the reproduction number during the early stages of disease outset are subject to substantial uncertainty regardless of the underlying assumptions for the epidemic growth.

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References

  1. R.M. Anderson, R.M. May, Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, Oxford, 1991)

    Google Scholar 

  2. O. Diekmann, J. Heesterbeek, Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis, and Interpretations (Wiley, Hoboken, 2000)

    MATH  Google Scholar 

  3. A.G. McKendrick, Proc. Edinb. Math. Soc. 14, 98–130 (1926)

    Google Scholar 

  4. W.O. Kermak, A.G. McKendrick, Proc. R. Soc. Lond. B 115, 700–721 (1927)

    Article  Google Scholar 

  5. W.O. Kermack, A.G. McKendrick, J. Hyg. (Lond.) 37, 172–187 (1937)

    Google Scholar 

  6. W.H. Hamer, Lancet 1, 733–739 (1906)

    Google Scholar 

  7. R. Ross, The Prevention of Malaria (John Murray, London, 1911)

    Google Scholar 

  8. D. Mollison, Epidemic Models: Their Structure and Relation to Data (Cambridge University Press, Cambridge, 1995)

    MATH  Google Scholar 

  9. N.T.J. Bailey, The Mathematical Theory of Infectious Diseases and Its Applications (Hafner, New York, 1975)

    MATH  Google Scholar 

  10. S.M. Moghadas, Eur. J. Epidemiol. 21, 337–342 (2006)

    Article  Google Scholar 

  11. S.A. Colgate, E.A. Stanley, J.M. Hyman, S.P. Layne, C. Qualls, Proc. Natl. Acad. Sci. U. S. A. 86, 4793–4797 (1989)

    Article  Google Scholar 

  12. B. Szendroi, G. Csányi, Proc. R. Soc. Lond. B 271, S364–S366 (2004)

    Article  Google Scholar 

  13. G. Chowell, C. Viboud, J.M. Hyman, L. Simonsen, PLoS Curr. 7 (2015). https://doi.org/10.1371/currents.outbreaks.8b55f4bad99ac5c5db3663e916803261

  14. C. Viboud, L. Simonsen, G. Chowell, Epidemics 15, 27–37 (2016)

    Article  Google Scholar 

  15. G. Chowell, C. Viboud, L. Simonsen, S.M. Moghadas, J. R. Soc. Interface 13(123), 20160659 (2016)

    Article  Google Scholar 

  16. J. Tolle, Math Gazette 87, 522–525 (2003)

    Article  Google Scholar 

  17. J. Wallinga, M. Lipsitch, Proc. R. Soc. B 274(1609), 599–604 (2007)

    Article  Google Scholar 

  18. M.G. Roberts, H. Nishiura, PLoS One 6(5), p.e17835 (2011)

    Google Scholar 

  19. J. Müller, C. Kuttler, Methods and Models in Mathematical Biology - Deterministic and Stochastic Approaches (Springer, Berlin, 2015)

    Book  Google Scholar 

  20. A.M. Mood, F.A. Graybill, D.C. Boes, Introduction to the Theory of Statistics (McGraw-Hill, Singapore, 1974)

    MATH  Google Scholar 

  21. H. Jeffreys, B.S. Jeffreys, Methods of Mathematical Physics, 3rd edn. (Cambridge University Press, Cambridge, 1999)

    Book  Google Scholar 

  22. J. Bibby, Glasgow Math. J. 15(01), 63–65 (1974)

    Article  MathSciNet  Google Scholar 

  23. M.A. Vink, M.C.J. Bootsma, J. Wallinga, Am. J. Epidemiol. 180(9), 865–875 (2014)

    Article  Google Scholar 

  24. WHO Ebola Response Team, New Engl. J. Med. 371(16), 1481–1495 (2014)

    Article  Google Scholar 

  25. A. Cori, N.M. Ferguson, C. Fraser, S. Cauchemez, Am. J. Epidemiol. 178(9), 1505–1512 (2013)

    Article  Google Scholar 

  26. T. Obadia, R. Haneef, P.Y. Boelle, BMC Med. Inform. Decis. Mak. 12(1) (2012)

    Google Scholar 

  27. D. Champredon, D.J.D. Earn, Phys. Life Rev. 18, 105–108 (2016)

    Article  Google Scholar 

  28. A. Scherer, A. McLean, Br. Med. Bull. 62(1), 187–199 (2002)

    Article  Google Scholar 

  29. C. Fraser, S. Riley, R.M. Anderson, N.M. Ferguson, Proc. Natl. Acad. Sci. U. S. A. 101(16), 6146–6151 (2004)

    Article  Google Scholar 

  30. M. Laskowski, L.C. Mostaço-Guidolin, A.L. Greer, J.Wu, S.M. Moghadas, Sci. Rep. 1(105), 1–7 (2011)

    Google Scholar 

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Acknowledgements

This work is supported by NSERC (Canada) and Mitacs (Canada).

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Correspondence to Seyed M. Moghadas .

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Champredon, D., Moghadas, S.M. (2019). On the Reproduction Number of Epidemics with Sub-exponential Growth. In: Mondaini, R. (eds) Trends in Biomathematics: Mathematical Modeling for Health, Harvesting, and Population Dynamics. Springer, Cham. https://doi.org/10.1007/978-3-030-23433-1_20

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