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The transmissibility estimation of influenza with early stage data of small-scale outbreaks in Changsha, China, 2005–2013

  • T. M. CHEN (a1) (a2), Q. P. CHEN (a3), R. C. LIU (a2), A. SZOT (a4), S. L. CHEN (a2), J. ZHAO (a2) and S. S. ZHOU (a1)...
Summary

Hundreds of small-scale influenza outbreaks in schools are reported in mainland China every year, leading to a heavy disease burden which seriously impacts the operation of affected schools. Knowing the transmissibility of each outbreak in the early stage has become a major concern for public health policy-makers and primary healthcare providers. In this study, we collected all the small-scale outbreaks in Changsha (a large city in south central China with ~7·04 million population) from January 2005 to December 2013. Four simple and popularly used models were employed to calculate the reproduction number (R) of these outbreaks. Given that the duration of a generation interval Tc = 2·7 and the standard deviation (s.d.) σ = 1·1, the mean R estimated by an epidemic model, normal distribution and delta distribution were 2·51 (s.d. = 0·73), 4·11 (s.d. = 2·20) and 5·88 (s.d. = 5·00), respectively. When Tc = 2·9 and σ = 1·4, the mean R estimated by the three models were 2·62 (s.d. = 0·78), 4·72 (s.d. = 2·82) and 6·86 (s.d. = 6·34), respectively. The mean R estimated by gamma distribution was 4·32 (s.d. = 2·47). We found that the values of R in small-scale outbreaks in schools were higher than in large-scale outbreaks in a neighbourhood, city or province. Normal distribution, delta distribution, and gamma distribution models seem to more easily overestimate the R of influenza outbreaks compared to the epidemic model.

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Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
*Author for correspondence: Professor S. S. Zhou, Department of Malaria, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, 207 Ruijin Er Road, Shanghai, People's Republic of China. (Email: zss163@hotmail.com)
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Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
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