Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis.
Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis.
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DOI：
10.1016/s1473-3099(14)71074-6
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发表时间：
2015-02
影响因子：
56.3
通讯作者：
Vespignani, Alessandro
中科院分区：
文献类型：
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作者：
Merler, Stefano;Ajelli, Marco;Fumanelli, Laura;Gomes, Marcelo F. C.;Pastore y Piontti, Ana;Rossi, Luca;Chao, Dennis L.;Longini, Ira M., Jr.;Halloran, M. Elizabeth;Vespignani, Alessandro
2014年西非埃博拉疫情对健康构成了前所未有的威胁。我们开发了一个埃博拉传播模型，该模型整合了利比里亚详细的地理和人口数据，以克服非空间方法在预测疾病动态和评估非药物控制干预措施方面的局限性。我们使用一个基于空间代理的模型使用马尔可夫链蒙特卡罗方法校准。该模型用于估计埃博拉传播参数，并调查干预措施的有效性，如埃博拉治疗单位的可用性，安全埋葬程序和家庭保护包。截至2014年8月16日，我们估计38.3%（95%CI 17.4 - 76.4）的感染是在医院获得的，30.7%（95%CI 14.1 - 46.4）是在家庭中获得的，8.9%（95%CI 3.3 - 11.8）是在参加葬礼时获得的。在流行病的早期阶段，医院中埃博拉和非埃博拉患者的流动和混合被认为是观察到的空间传播模式的充分驱动因素。随后，国家和县一级的发病率下降，原因是埃博拉治疗单位的供应增加-这反过来又有助于大幅减少医院传播-安全埋葬和家庭防护包的分发。该模型允许评估干预方案，并解开自2014年9月7日以来观察到的发病率下降的作用。需要高质量的数据-例如，用于估计家庭二次发病率、医院内的接触模式以及正在进行的干预措施的效果-以减少模型估计的不确定性。
The 2014 Ebola epidemic in West Africa defines an unprecedented health threat. We developed a model of Ebola transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions. We use a spatial agent-based model calibrated using a Markov chain Monte Carlo approach. The model is used to estimate Ebola transmission parameters and investigate the effectiveness of interventions such as availability of Ebola Treatment Units, safe burials procedures and household protection kits. Through August 16, 2014, we estimate that 38·3% (95%CI 17·4-76·4) of infections were acquired in hospitals, 30·7% (95%CI 14·1-46·4) in households, and 8·9% (95%CI 3·3-11·8) while participating in funerals. The movement and mixing of Ebola and non-Ebola patients in hospitals at the early stage of the epidemic is found to be a sufficient driver of the observed pattern of spatial spread. The subsequent decrease of incidence at country and county level is ascribable to the increasing availability of Ebola treatment units – which in turn contributed to drastically decrease hospital transmission – safe burials, and distribution of household protection kits. The model allows evaluating intervention options and disentangling their role in the decrease of incidence observed since September 7, 2014. High-quality data - e.g. to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions - are needed to reduce uncertainty in model estimates.