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Emerging health threats journal最新文献

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Inferring travel from social media 从社交媒体推断旅行
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11126
Alessio Signorini, P. Polgreen, Alberto Maria Segre
Introduction The spread of infectious diseases is facilitated by human travel. Disease is often introduced by travelers and then spread among susceptible individuals. Likewise, uninfected susceptible travelers can move into populations sustaining the spread of an infectious disease. Several disease-modeling efforts have incorporated travel and census data in an effort to better understand the spread of disease. Unfortunately, most travel data are not fine grained enough to capture individual movements over long periods and large spaces. Alternative methods (e.g., tracking currency movements or cell phone signals) have been suggested to measure how people move with higher resolution but these are often sparse, expensive and not readily available to researchers. FourSquare is a social media application that permits users to ‘check-in’ (i.e., record their currentlocation at stores, restaurants, etc.) via their mobile telephones in exchange for incentives (e.g., location-specific coupons). FourSquare and similar applications (Gowalla, Yelp, etc.) generally broadcast each check-in via Twitter or Facebook; in addition, some GPS-enabled mobile Twitter clients add explicit geocodes to individual tweets. Here, we propose the use of geocoded social media data as a real-time fine-grained proxy for human travel.
人类的旅行促进了传染病的传播。疾病通常由旅行者引入,然后在易感人群中传播。同样,未受感染的易感旅行者可以进入维持传染病传播的人群。为了更好地了解疾病的传播,一些疾病建模工作已经将旅行和人口普查数据纳入其中。不幸的是,大多数旅行数据的粒度不够细,无法捕捉长时间和大空间内的个人移动。替代方法(例如,跟踪货币运动或手机信号)已被建议以更高的分辨率测量人们如何运动,但这些方法通常是稀疏的,昂贵的,并且不易为研究人员所用。FourSquare是一个社交媒体应用程序,允许用户通过手机“签到”(例如,记录他们在商店、餐馆等的当前位置),以换取奖励(例如,特定位置的优惠券)。FourSquare和类似的应用程序(Gowalla、Yelp等)通常会通过Twitter或Facebook广播每次签到;此外,一些支持gps的移动Twitter客户端向单个tweet添加了显式的地理编码。在这里,我们建议使用地理编码的社交媒体数据作为人类旅行的实时细粒度代理。
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引用次数: 0
Defining emergency department asthma visits for public health surveillance 确定急诊哮喘就诊的公共卫生监测
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11042
D. Travers, K. Lich, Steven J. Lippmann, A. Waller, M. Weinberger, K. Yeatts
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引用次数: 4
Surveillance of poison center data using the National Poison Data System web service 使用国家毒物数据系统网络服务监测毒物中心数据
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11036
Melissa Powell, K. Ryff, S. Giffin
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引用次数: 0
School absenteeism surveillance data during the 2009 influenza A/H1N1 pandemic 2009年甲型H1N1流感大流行期间学校缺勤监测数据
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11112
Kenneth Dufault, E. Daly, S. Bascom, Christopher Taylor, Paul Lakevicius, S. alroy-Preis
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引用次数: 0
Disease profile development methodology for syndromic surveillance of biological threat agents 生物威胁剂综合征监测的疾病概况编制方法
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11129
Julio C. Silva, D. Rumoro, Marilyn M. Hallock, S. Shah, G. Gibbs, M. Waddell
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引用次数: 4
Monitoring winter-seasonal acute gastroenteritis emergency department visits by age 按年龄监测冬季急性肠胃炎急诊就诊情况
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11113
D. Olson, I. Painter
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引用次数: 0
An evaluation of electronic laboratory data quality and a health information exchange 电子实验室数据质量评价与卫生信息交换
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11104
B. Dixon, S. Grannis
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引用次数: 2
Changes in the spatial distribution of syphilis 梅毒的空间分布变化
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11093
S. Tolentino, S. Pemmaraju, P. Polgreen, Anson Tai YatHo, M. Monsalve, Alberto Maria Segre
Introduction Public health officials and epidemiologists have been attempting to eradicate syphilis for decades, but national incidence rates are again on the rise. It has been suggested that the syphilis epidemic in the United States is a ‘rare example of unforced, endogenous oscillations in disease incidence, with an 8 11-year period that is predicted by the natural dynamics of syphilis infection, to which there is partially protective immunity’ (1). While the time series of aggregate case counts seems to support this claim, between 1990 and 2010, there seems to have been a significant change in the spatial distribution of the syphilis epidemic. It is unclear if this change can also be attributed to ‘endogenous’ factors or whether it is due to exogenous factors such as behavioral changes (e.g., the widespread use of the internet for anonymous sexual encounters). For example, it is pointed out that levels of syphilis in 1989 were abnormally high in counties in North Carolina (NC) immediately adjacent to highways (2). The hypothesis was that this may be due to truck drivers and prostitution and/or the emerging cocaine market (1). Our results indicate that syphilis distribution in NC has changed since 1989, diffusing away from highway counties (see Fig. 1).
几十年来,公共卫生官员和流行病学家一直试图根除梅毒,但全国发病率再次上升。有人认为,美国的梅毒流行是“疾病发病率非强制性内源性振荡的罕见例子,梅毒感染的自然动态预测了8 - 11年的周期,其中存在部分保护性免疫”(1)。虽然总病例数的时间序列似乎支持这一说法,但在1990年至2010年之间,梅毒流行的空间分布似乎发生了重大变化。目前尚不清楚这种变化是否也可以归因于“内生”因素,还是由于行为改变等外生因素(例如,广泛使用互联网进行匿名性接触)。例如,有人指出,1989年,北卡罗来纳州(NC)紧靠高速公路的县的梅毒水平异常高(2)。假设这可能是由于卡车司机、卖淫和/或新兴的可卡因市场(1)。我们的结果表明,自1989年以来,北卡罗来纳州的梅毒分布发生了变化,从高速公路县向外扩散(见图1)。
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引用次数: 0
Significant multiple high-and low-risk regions in event data maps 事件数据图中多个重要的高风险和低风险区域
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11131
Emerson C. Bodevan, L. Duczmal, Gladston J. P. Moreira, A. Duarte, F. C. O. Magalhães
Introduction The Voronoi Based Scan (VBScan) (1) is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases’ points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates subtrees, which are the potential space-time clusters, which are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work, we modify VBScan to find the best partition dividing the map into multiple lowand high-risk regions.
基于Voronoi的扫描(VBScan)(1)是一种快速检测和推断点数据集时空疾病聚类的方法。为代表群体个体(病例和对照)的点建立了Voronoi图。连接两个案例点的线段截取的Voronoi细胞边界的数量定义了这些点之间的Voronoi距离。该距离用于估计异质种群的密度,并建立连接案例的Voronoi距离最小生成树(MST)。连续去除其边缘产生子树,这些子树是潜在的时空簇,通过扫描统计量对其进行评估。原始数据的蒙特卡罗复制用于评估聚类显著性。在本工作中,我们修改了VBScan,以找到将地图划分为多个低高风险区域的最佳分区。
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引用次数: 0
A zero-inflated Poisson-based spatial scan statistic 零膨胀的基于泊松的空间扫描统计量
Pub Date : 2011-12-06 DOI: 10.3402/EHTJ.V4I0.11116
A. Cançado, C. da-Silva, M. F. Silva
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引用次数: 5
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Emerging health threats journal
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