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Piecewise quadratic growth during the 2019 novel coronavirus epidemic · Around

2020

Piecewise quadratic growth during the 2019 novel coronavirus epidemic

Brandenburg, Axel

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The temporal growth in the number of deaths in the COVID-19 epidemic is subexponential.

Here we show that a piecewise quadratic law provides an excellent fit during the thirty days after the first three fatalities on January 20 and later since the end of March 2020.
There is also a brief intermediate period of exponential growth.
During the second quadratic growth phase, the characteristic time of the growth is about eight times shorter than in the beginning, which can be understood as the occurrence of separate hotspots.

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Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proc. Roy. Soc. Lond Ser. A
1927
Earlier work this paper cites.
Fisher, R. A. (1937). The wave of advance of advantageous genes. Ann. Eugenics
1937
Earlier work this paper cites.
Kolmogorov, A. N., Petrovskii, I. G., & Piskunov, N. S. (1937). Study of the diffusion equation with growth of the quantity of matter and its application to a biology problem. Moscow Univ. Bull. Math
1937
Earlier work this paper cites.
Noble, J. V. (1974). Geographic and temporal development of plagues. Nature
1974
Earlier work this paper cites.
Williamson, J. H. (1980). Low-storage Runge-Kutta schemes. J. Comp. Phys
1980
Earlier work this paper cites.
Källén, A., Arcuri, P., & Murray, J. D. (1985). A simple model for the spatial spread and control of rabies. J. Theor. Biol
1985
Earlier work this paper cites.
Murray, J. D., Stanley, E. A., & Brown, D. L. (1986). On the spatial spread of rabies among foxes. Proc. Roy. Soc. Lond. Ser. B
1986
Earlier work this paper cites.
Brandenburg, A. (2020a). Quadratic growth during the 2019 novel coronavirus epidemic; see also the appendix of version 2 of February 14, arXiv:2002.03638v2
Original
2002
Earlier work this paper cites.
Li, M., Chen, J., & Deng, Y. (2020). Scaling features in the spreading of COVID-19, arXiv:2002.09199
Original
2002
Earlier work this paper cites.
Liang, Y., Xu, D., Fu, S., et al. (2020). A Simple Prediction Model for the Development Trend of 2019-nCoV Epidemics Based on Medical Observations, arXiv:2002.00426
Original
2002
Earlier work this paper cites.
Sanche, S., et al (2020). The Novel Coronavirus, 2019-nCoV, is Highly Contagious and More Infectious Than Initially Estimated, arXiv:2002.03268
Original
2002
Earlier work this paper cites.
Medo, M. (2020). Epidemic spreading on spatial networks with distance-dependent connectivity, arXiv:2003.13160
Original
2003
Earlier work this paper cites.
Murray, J. D. (2003). Mathematical Biology. II: Spatial models and biomedical applications (Springer, New York)
2003
Earlier work this paper cites.

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