Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility
Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility
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DOI：
10.1186/1741-7015-7-45
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发表时间：
2009-09-10
期刊：
影响因子：
9.3
通讯作者：
Vespignani, Alessandro
中科院分区：
文献类型：
--
作者：
Balcan, Duygu;Hu, Hao;Vespignani, Alessandro
背景资料：6月11日，世界卫生组织正式将大流行病警戒级别（针对新的H1N1流感毒株）提高到6级。截至7月19日，在142个不同的国家正式确认了137，232例H1N1流感病毒株，目前正在密切关注南半球的大流行，以了解北方的下一波冬季浪潮。一个主要的挑战是先发制人的需要估计的病毒的传播潜力，并评估其依赖于季节性方面，以便能够使用数值模型能够预测的时空模式的patient-temporal pattern of the pandemic.Methods：在目前的工作中，我们使用一个全球性的结构化集合种群模型，整合全球范围内的流动性和运输数据。该模型考虑了220个不同国家的3 362个亚群的数据以及这些亚群之间的个人流动性。该模型生成了全球60亿人的流行病演变的随机实现，从中我们可以收集每个亚群的流行率、发病率、继发病例数以及输入病例数和日期等信息，所有这些信息的时间分辨率均为1天。为了估计传播潜力和相关模型参数，我们使用了2009年新型甲型H1N1流感的时间表数据。该方法是基于最大似然分析的到达时间分布模型在12个国家播种墨西哥使用100万计算模拟流行病。一个扩展的年表，包括全球93个国家在6月18日之前播种被用来确定季节性effects.Results：我们发现最好的估计R-0 = 1.75（95%置信区间（CI）1.64至1.88）的基本生殖数。相关性分析允许根据观察到的模式选择最可能的季节性行为，从而识别大流行未来发展的合理情景，并估计不同半球的大流行活动峰值。我们提供了下一波的住院人数和发病率的估计，以及对疾病参数值的广泛敏感性分析。我们还研究了系统的治疗使用抗病毒药物的流行时间轴的效果。结论：分析显示，可能发生在10月/11月在北方半球的早期流行高峰，可能在大规模的疫苗接种运动可以进行。基线结果是指不考虑额外缓解政策的最坏情况。我们认为，计划额外的缓解政策，如系统的抗病毒治疗可能是延迟活动高峰，以恢复疫苗接种计划的有效性的关键。
Background: On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. As of 19 July, 137,232 cases of the H1N1 influenza strain have been officially confirmed in 142 different countries, and the pandemic unfolding in the Southern hemisphere is now under scrutiny to gain insights about the next winter wave in the Northern hemisphere. A major challenge is pre-empted by the need to estimate the transmission potential of the virus and to assess its dependence on seasonality aspects in order to be able to use numerical models capable of projecting the spatiotemporal pattern of the pandemic.Methods: In the present work, we use a global structured metapopulation model integrating mobility and transportation data worldwide. The model considers data on 3,362 subpopulations in 220 different countries and individual mobility across them. The model generates stochastic realizations of the epidemic evolution worldwide considering 6 billion individuals, from which we can gather information such as prevalence, morbidity, number of secondary cases and number and date of imported cases for each subpopulation, all with a time resolution of 1 day. In order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum likelihood analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1 million computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects.Results: We found the best estimate R-0 = 1.75 (95% confidence interval (CI) 1.64 to 1.88) for the basic reproductive number. Correlation analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline.Conclusion: The analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. The baseline results refer to a worst-case scenario in which additional mitigation policies are not considered. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak in order to restore the effectiveness of the vaccination programs.