Time variations in the transmissibility of pandemic influenza in Prussia, Germany, from 1918-19
Time variations in the transmissibility of pandemic influenza in Prussia, Germany, from 1918-19
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DOI：
10.1186/1742-4682-4-20
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发表时间：
2007-01-01
影响因子：
--
通讯作者：
Nishiura, Hiroshi
中科院分区：
文献类型：
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作者：
Nishiura, Hiroshi
背景：关于大流行性流感，传播潜力的时间变化很少被研究。本文利用1918年9月29日至1919年2月1日期间的每日死亡人数（共计8911人）和从发病到死亡的时间延迟分布，重新分析了1918- 1919年德国普鲁士大流行性流感的时间分布，以估计有效繁殖数Rt，定义为在给定的时间每个原发病例的继发病例的实际平均数。结果：一个离散时间分支过程应用于反算的发病率数据，假设三个不同的序列间隔（即1，3和5天）。估计的繁殖数表现出明显的估计和选择的序列间隔之间的关联，即假设的序列间隔越长，繁殖数越高。此外，估计的繁殖数量并没有随着时间的单调下降，表明二次传播的模式随时间而变化。这些趋势与最近的研究中对大流行性流感的繁殖数量的估计值的差异是一致的;高估计值可能源于较长的序列间隔和一个关于传播率的模型假设，该模型不考虑时间变化并适用于整个流行曲线。目前的研究结果表明，为了提供可靠的评估，详细阐明疾病的自然史至关重要（例如，G.包括串行间隔）以及异构传输模式。此外，考虑到人类接触行为可能会影响传染性，个人的对策（e。G.家庭隔离和戴口罩），以制定有效的非药物干预措施。
Background: Time variations in transmission potential have rarely been examined with regard to pandemic influenza. This paper reanalyzes the temporal distribution of pandemic influenza in Prussia, Germany, from 1918-19 using the daily numbers of deaths, which totaled 8911 from 29 September 1918 to 1 February 1919, and the distribution of the time delay from onset to death in order to estimate the effective reproduction number, Rt, defined as the actual average number of secondary cases per primary case at a given time.Results: A discrete-time branching process was applied to back-calculated incidence data, assuming three different serial intervals (i.e. 1, 3 and 5 days). The estimated reproduction numbers exhibited a clear association between the estimates and choice of serial interval; i.e. the longer the assumed serial interval, the higher the reproduction number. Moreover, the estimated reproduction numbers did not decline monotonically with time, indicating that the patterns of secondary transmission varied with time. These tendencies are consistent with the differences in estimates of the reproduction number of pandemic influenza in recent studies; high estimates probably originate from a long serial interval and a model assumption about transmission rate that takes no account of time variation and is applied to the entire epidemic curve.Conclusion: The present findings suggest that in order to offer robust assessments it is critically important to clarify in detail the natural history of a disease (e. g. including the serial interval) as well as heterogeneous patterns of transmission. In addition, given that human contact behavior probably influences transmissibility, individual countermeasures (e. g. household quarantine and mask-wearing) need to be explored to construct effective non-pharmaceutical interventions.