Forecasting the 2013-2014 influenza season using Wikipedia.
Forecasting the 2013-2014 influenza season using Wikipedia.
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DOI：
10.1371/journal.pcbi.1004239
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发表时间：
2015-05
影响因子：
4.3
通讯作者：
Del Valle SY
中科院分区：
文献类型：
--
作者：
Hickmann KS;Fairchild G;Priedhorsky R;Generous N;Hyman JM;Deshpande A;Del Valle SY
传染病是世界各地发病率和死亡率的主要原因之一；因此，预测其影响对于规划有效的应对战略至关重要。根据疾病控制和预防中心(CDC)的数据，季节性流感影响了5%至20%的美国人口，并因住院和旷工造成重大经济影响。了解流感动态并预测其影响是制定预防和缓解战略的基础。我们将现代数据同化方法与维基百科访问日志和疾控中心流感样疾病(ILI)报告相结合，创建了季节性流感的每周预报。这些方法适用于2013-2014年流感季节，但在给定发病率或病例计数数据的情况下，足以预测任何疾病的爆发。我们调整了疾病模型的初始化和参数化，并表明这允许我们确定系统的模型偏差。此外，我们还提供了一种方法来确定模型与观测的偏差，并评估预测精度。维基百科的文章访问日志与ILI历史记录高度相关，并允许在ILI数据可用前几周对其进行准确预测。结果表明，在流感季节高峰期之前，我们的预测方法为2013-2014年ILI观测产生了50%和95%的可信区间，其中包含了预测中大部分周的实际观测。然而，由于我们的模型没有考虑再次感染或多种流感病毒株，因此在流感季节高峰期过去后，不能很好地预测疫情的尾部。我们使用现代方法将当前数据注入流行病学模型，以提供对美国人口未来流感状态的概率评估。这种类型的疾病预测仍处于初级阶段，但随着这些方法变得更加成熟，它将允许采取越来越强有力的控制措施来应对和预防大规模疾病暴发。尽管天气预报在过去的半个世纪里得到了稳步的改进，并在现代生活中变得无处不在，但令人惊讶的是，关于传染病预报的工作很少。尽管在疾病动力学建模方面已经做了大量工作，但考虑到当前的公共卫生数据，这些工作很少被用来生成对预期未来动态的概率描述。此外，随着新数据的出现，更新预期疾病结果的机制才刚刚开始受到公共卫生界的关注。使用疾控中心类似流感的疾病报告和数字监测来源，如对维基百科文章访问日志的观察，我们现在处于这样一个点，即对流感季节的预测可以开始为疾病监测和缓解提供有用的信息。
Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed. We use modern methods for injecting current data into epidemiological models in order to offer a probabilistic evaluation of the future influenza state in the U.S. population. This type of disease forecasting is still in its infancy, but as these methods become more developed it will allow for increasingly robust control measures to react to and prevent large disease outbreaks. While weather forecasting has steadily improved over the last half century and become ubiquitous in modern life, there is surprisingly little work on infectious disease forecasting. Although there has been a great deal of work in modeling disease dynamics, these have seldom been used to generate a probabilistic description of expected future dynamics, given current public health data. Moreover, the mechanism to update expected disease outcomes as new data becomes available is just beginning to receive attention from the public health community. Using CDC influenza-like illness reports and digital monitoring sources, such as observations of Wikipedia article access logs, we are now at a point where forecasting for the influenza season can begin to offer useful information for disease monitoring and mitigation.
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影响因子：
1.8
作者：
Breto, Carles;He, Daihai;King, Aaron A.
通讯作者：
King, Aaron A.
影响因子：
3.7
作者：
Bettencourt LM;Ribeiro RM
通讯作者：
Ribeiro RM
影响因子：
2.8
作者：
Alfaro-Murillo JA;Towers S;Feng Z
通讯作者：
Feng Z
影响因子：
1.2
作者：
Creal, Drew
通讯作者：
Creal, Drew
DOI：
10.1016/s0140-6736(03)13410-1
发表时间：
2003-05-24
期刊：
Lancet (London, England)
影响因子：
--
作者：
Donnelly CA;Ghani AC;Leung GM;Hedley AJ;Fraser C;Riley S;Abu-Raddad LJ;Ho LM;Thach TQ;Chau P;Chan KP;Lam TH;Tse LY;Tsang T;Liu SH;Kong JH;Lau EM;Ferguson NM;Anderson RM
通讯作者：
Anderson RM