Application of the CDC EbolaResponse Modeling tool to disease predictions.
Application of the CDC EbolaResponse Modeling tool to disease predictions.
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CDC 埃博拉响应模型工具在疾病预测中的应用。
DOI：
10.1016/j.epidem.2017.03.001
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发表时间：
2017
期刊：
影响因子：
3.8
通讯作者：
C. Viboud
中科院分区：
文献类型：
--
作者：
Robert Gaffey;C. Viboud
基于模型的预测对于促使国际公共卫生对2014年西非爆发的埃博拉病毒疾病做出强有力的反应至关重要。在这里，我们描述了疾控中心发起的埃博拉响应建模工具的扩展在埃博拉预测挑战中的表现，该工具为流行病学预测提供了一个受控的环境。在埃博拉应对工具中，通过最小二乘拟合将传播风险和受干预措施影响的人口比例与数据进行拟合。对4个综合疫情的5个预报时间点的预报效果进行了评价。一到四周后的发病率预测与综合观察结果有很好的相关性(Rho∼0.8)，在各种误差指标上的平均总排名在参与背景的8支球队中排名第4。EbolaResponse对最终大小、峰值大小和时间做出了较为准确的预测。这一易于适应的机制模型相对成功，每隔一段时间重新评估模型参数，这表明它可以产生相对准确的短期预测，特别是在干预措施交错的情况下。该模型的一个重要缺点包括其当前框架中缺乏不确定性估计。总体而言，我们的结果与这样的结论相一致，即参数较少的简单模型对于流行病轨迹的短期预测效果很好。
Model-based predictions were critical in eliciting a vigorous international public health response to the 2014 Ebola Virus Disease outbreak in West Africa. Here, we describe the performances of an extension of the CDC-initiated EbolaResponse Modeling tool to the Ebola Forecasting Challenge, which offered a controlled environment for epidemiological predictions. In the EbolaResponse tool, transmission risks and proportions of population affected by interventions were fitted to data via least square fitting. Prediction performances were evaluated for 5 prediction time points of 4 synthetic outbreaks. One-to-four week-ahead incidence predictions were well correlated with synthetic observations (rho ∼0.8), and overall ranking averaged over various error metrics was 4th of 8 teams participating in the context. EbolaResponse yielded moderately accurate predictions for final size, peak size and timing. The relative success of this easily adaptable mechanistic model, with reassessment of model parameters at fixed intervals, indicates that it can generate relatively accurate short-term forecasts, especially when interventions are staggered. An important downside of the model includes a lack of uncertainty estimates in its current framework. Overall, our results align with the conclusion that simple models with few parameters perform well for short-term prediction of epidemic trajectories.
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DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
影响因子：
3.8
作者：
Viboud C;Sun K;Gaffey R;Ajelli M;Fumanelli L;Merler S;Zhang Q;Chowell G;Simonsen L;Vespignani A;RAPIDD Ebola Forecasting Challenge group
通讯作者：
RAPIDD Ebola Forecasting Challenge group
影响因子：
3.8
作者：
Ajelli M;Zhang Q;Sun K;Merler S;Fumanelli L;Chowell G;Simonsen L;Viboud C;Vespignani A
通讯作者：
Vespignani A
影响因子：
56.3
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
通讯作者：
Vespignani, Alessandro