Influenza forecasting in human populations: a scoping review.
Influenza forecasting in human populations: a scoping review.
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DOI：
10.1371/journal.pone.0094130
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
McKenzie FE
中科院分区：
文献类型：
--
作者：
Chretien JP;George D;Shaman J;Chitale RA;McKenzie FE
对人群中流感活动的预测可以帮助指导关键的准备工作。我们进行了范围审查，以描述这些方法的特点，并确定研究差距。采用PRISMA系统评价方法，我们检索了PubMed、CINAHL、Project欧几里得和科克伦系统评价数据库中自2000年1月1日以来的英文文献，检索词为“influenza AND（forecast* OR predict*）"，不包括没有根据独立数据验证预测或没有纳入流感的研究-应用预测的季节或大流行的相关监测数据。我们纳入了35篇描述基于人口（N = 27）、基于医疗机构（N = 4）和区域或全球流行病传播（N = 4）预测的出版物。 包括北美（N = 15）、欧洲（N = 14）和/或亚太地区（N = 4），或全球范围（N = 3）。 预测模型为统计学模型（N = 18）或流行病学模型（N = 17）。 五项研究使用数据同化方法，用新的监测数据更新预报。模型使用病毒学（N = 14）、症状学（N = 13）、气象学（N = 6）、互联网搜索查询（N = 4）和/或其他监测数据作为输入。 预测结果和验证指标差异很大。两项研究比较了不同的建模方法，使用共同的数据，2评估模型校准，1系统地纳入专家输入。在使用流行病学模型的17项研究中，8项包括敏感性分析。这一审查表明，需要在流感预测中采用良好做法（例如，敏感性分析）;直接比较各种方法;评估模型校准;综合主观专家投入;在试点和实际应用中进行业务研究;以及增进建模者和公共卫生官员之间的相互了解。
Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.
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影响因子：
3.7
作者：
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通讯作者：
Leung B
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