Chen T M, Chen Q P, Liu R C, Szot A, Chen S L, Zhao J, Zhou S S
Department of Malaria,National Institute of Parasitic Diseases,Chinese Center for Disease Control and Prevention,Shanghai,People's Republic of China.
Hospital,Shanghai Normal University,Shanghai,People's Republic of China.
Epidemiol Infect. 2017 Feb;145(3):424-433. doi: 10.1017/S0950268816002508. Epub 2016 Nov 11.
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.
中国大陆每年报告数百起学校小规模流感疫情,导致沉重的疾病负担,严重影响受影响学校的运作。在早期阶段了解每起疫情的传播能力已成为公共卫生政策制定者和基层医疗服务提供者的主要关切。在本研究中,我们收集了2005年1月至2013年12月长沙(中国中南部的一个大城市,人口约704万)的所有小规模疫情。采用了四种简单且常用的模型来计算这些疫情的再生数(R)。假设一代间隔时间Tc = 2.7且标准差(s.d.)σ = 1.1,通过流行模型、正态分布和δ分布估计的平均R分别为2.51(s.d. = 0.73)、4.11(s.d. = 2.20)和5.88(s.d. = 5.00)。当Tc = 2.9且σ = 1.4时,三种模型估计的平均R分别为2.62(s.d. = 0.78)、4.72(s.d. = 2.82)和6.86(s.d. = 6.34)。通过伽马分布估计的平均R为4.32(s.d. = 2.47)。我们发现学校小规模疫情中的R值高于社区、城市或省份的大规模疫情。与流行模型相比,正态分布、δ分布和伽马分布模型似乎更容易高估流感疫情的R值。