The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.
The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.
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DOI：
10.1016/j.epidem.2017.08.002
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发表时间：
2018-03
期刊：
影响因子：
3.8
通讯作者：
RAPIDD Ebola Forecasting Challenge group
中科院分区：
文献类型：
--
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
传染病预测在公共卫生界越来越受欢迎，然而，存在有限的模型性能的系统比较。在这里，我们展示了2014-2015年西非埃博拉危机启发的合成预测挑战的结果，涉及16个国际学术团队和美国政府机构，并比较了8种独立建模方法的预测性能。挑战参与者被邀请预测4次合成埃博拉疫情的5个不同时间点的140个目标，每个时间点都涉及不同级别的干预措施和“战争迷雾”。预测指标包括1-4周前的病例发生率、暴发规模、高峰时间和几个自然史参数。关于每周病例发生率，基于8个参与模型的贝叶斯平均值的集合预测优于任何单个模型，并且比零自回归模型好得多。模型复杂性和预测准确性之间没有关系;然而，短期每周发病率的最佳表现模型是适合短期和最近爆发部分的“轻”反应模型。随着数据准确性和可用性的提高，个体和集合预测得到了改善;到第二个时间点，就在疫情高峰之前，对最终规模的估计在目标的20%以内。第四个挑战场景--反映了一场不受控制的埃博拉疫情，有大量的数据报告噪音--所有建模团队都预测得很差。总体而言，这一综合预测挑战提供了对不同数据和流行病学条件下模型性能的深入理解。我们建议将这种“和平时期”的预测挑战作为关键因素，以改善协调，激发建模小组之间的合作，以应对下一次大流行的威胁，并评估各种已知和假设病原体的模型预测准确性。
Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparison of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014–2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and “fog of war”. Prediction targets included 1–4 week-ahead case incidence, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidences, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were “light” reactive models fitted to a short and recent part of the outbreak. Individual and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario -- mirroring an uncontrolled Ebola outbreak with substantial data reporting noise -- was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under different data and epidemiological conditions. We recommend such “peace time” forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and assess model forecasting accuracy for a variety of known and hypothetical pathogens.
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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
DOI：
10.1126/science.aaa4339
发表时间：
2015-03-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Heesterbeek H;Anderson RM;Andreasen V;Bansal S;De Angelis D;Dye C;Eames KT;Edmunds WJ;Frost SD;Funk S;Hollingsworth TD;House T;Isham V;Klepac P;Lessler J;Lloyd-Smith JO;Metcalf CJ;Mollison D;Pellis L;Pulliam JR;Roberts MG;Viboud C;Isaac Newton Institute IDD Collaboration
通讯作者：
Isaac Newton Institute IDD Collaboration
DOI：
10.1089/bsp.2011.0007
发表时间：
2011-06-01
期刊：
BIOSECURITY AND BIOTERRORISM-BIODEFENSE STRATEGY PRACTICE AND SCIENCE
影响因子：
--
作者：
Lipsitch, Marc;Finelli, Lyn;Redd, Stephen C.
通讯作者：
Redd, Stephen C.
影响因子：
28.3
作者：
Perkins, T. Alex;Siraj, Amir S.;Tatem, Andrew J.
通讯作者：
Tatem, Andrew J.
影响因子：
3.7
作者：
Chretien JP;George D;Shaman J;Chitale RA;McKenzie FE
通讯作者：
McKenzie FE