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Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore Pakistan

机译:关于登革热病毒传播时空变化的推论对关于人类活动性的假设很敏感:使用巴基斯坦拉合尔地理定位推文的案例研究

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摘要

Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011–2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time.Electronic Supplementary MaterialThe online version of this article (10.1140/epjds/s13688-018-0144-x) contains supplementary material.
机译:数十亿的手机,社交媒体平台和其他技术的用户生成越来越大量的数据,这些数据有可能被利用来解决公共卫生挑战。在适当的概念框架内加以利用时,这些和其他大数据资源在流行病学应用中往往是最成功的。在这里,我们证明了在大型城市区域之间传播的传染病动态建模框架中,关于宿主流动性假设的重要性。我们的分析集中于2011-2014年巴基斯坦拉合尔发生的一系列季节性大流行期间,登革热病毒(DENV)传播的时空变化。与许多直接传播的疾病类似,由于DENV是由叮咬的蚊子传播的,因此DENV传播主要发生在人们白天花费时间的地方。我们根据拉合尔十个地区的迁移模式的五个不同假设推论出DENV传播的时空变化:地区之间没有移动,地理位置推文遵循以下模式移动,与地区人口规模成正比的移动以及遵循惯常重力和辐射的移动楷模。总体而言,我们发现关于DENV传播时空变化的推论对这一范围内的城市内部人类流动模式的假设高度敏感,尽管允许适度程度的城市内部流动的三个假设在关键方面的表现相似。从流行病学的角度出发,基于我们的分析得出的关于传播方式的不同推论具有重要意义,因为它们对于控制目标应针对哪些目标以及随着时间的推移传播条件是否变得有利或不同具有不同的影响。 10.1140 / epjds / s13688-018-0144-x)包含补充材料。

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