Theoretical basis to measure the impact of short-lasting control of an infectious disease on the epidemic peak
Theoretical basis to measure the impact of short-lasting control of an infectious disease on the epidemic peak
复制标题
DOI：
10.1186/1742-4682-8-2
复制
发表时间：
2011-01-26
影响因子：
--
通讯作者：
Nishiura, Hiroshi
中科院分区：
文献类型：
--
作者：
Omori, Ryosuke;Nishiura, Hiroshi
背景资料：虽然许多大流行防范计划促进了疾病控制工作，以降低和推迟流行高峰，但仍需寻求确定所需控制工作和进行统计推断的分析方法。作为解决这个问题的第一步，我们提出了一个理论基础，在此基础上评估的影响，早期干预的流行高峰，采用一个简单的流行病model.Methods：我们专注于估计的影响，早期控制工作（e。G.不成功的遏制），假设当控制中断时传输速率突然增加。我们提供了流行高峰的幅度和时间的解析表达式，采用近似的逻辑和数学形式的解决方案，后者。利用日本H1N1-2009流感疫情数据，分析了暑假对2009年流感高峰的影响。结果：暑假使2009年流感高峰推迟了21天。峰值的下降似乎是控制相关的繁殖数量减少的非线性函数。峰值延迟严重依赖于最初免疫individual.Conclusions的分数：所提出的建模方法提供了方法途径，以评估经验数据和客观地估计所需的控制努力，以降低和延迟的流行高峰。分析结果支持迫切需要进行全民血清学调查，作为估计高峰时间的先决条件。
Background: While many pandemic preparedness plans have promoted disease control effort to lower and delay an epidemic peak, analytical methods for determining the required control effort and making statistical inferences have yet to be sought. As a first step to address this issue, we present a theoretical basis on which to assess the impact of an early intervention on the epidemic peak, employing a simple epidemic model.Methods: We focus on estimating the impact of an early control effort (e. g. unsuccessful containment), assuming that the transmission rate abruptly increases when control is discontinued. We provide analytical expressions for magnitude and time of the epidemic peak, employing approximate logistic and logarithmic-form solutions for the latter. Empirical influenza data (H1N1-2009) in Japan are analyzed to estimate the effect of the summer holiday period in lowering and delaying the peak in 2009.Results: Our model estimates that the epidemic peak of the 2009 pandemic was delayed for 21 days due to summer holiday. Decline in peak appears to be a nonlinear function of control-associated reduction in the reproduction number. Peak delay is shown to critically depend on the fraction of initially immune individuals.Conclusions: The proposed modeling approaches offer methodological avenues to assess empirical data and to objectively estimate required control effort to lower and delay an epidemic peak. Analytical findings support a critical need to conduct population-wide serological survey as a prior requirement for estimating the time of peak.