Nishiura Hiroshi
Department of Medical Biometry, University of Tübingen, Tübingen, Germany.
Theor Biol Med Model. 2007 Jun 4;4:20. doi: 10.1186/1742-4682-4-20.
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.
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.
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.
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日普鲁士(德国)大流行性流感的每日死亡人数(总计8911人)以及发病至死亡的时间延迟分布,重新分析了当时大流行性流感的时间分布情况,以估计有效再生数Rt,即给定时间内每个原发病例的实际平均二代病例数。
采用离散时间分支过程对反推的发病率数据进行分析,假设了三种不同的潜伏期(即1天、3天和5天)。估计的再生数在估计值与潜伏期选择之间呈现出明显的关联;即假设的潜伏期越长,再生数越高。此外,估计的再生数并非随时间单调下降,这表明二代传播模式随时间变化。这些趋势与近期研究中对大流行性流感再生数估计的差异一致;高估计值可能源于较长的潜伏期以及对传播率的模型假设,该假设未考虑时间变化且应用于整个流行曲线。
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(如包括潜伏期)以及传播的异质性模式至关重要。此外鉴于人类接触行为可能影响传播性,需要探索个体应对措施(如家庭隔离和佩戴口罩)以构建有效的非药物干预措施。