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Using GPS Technology to Quantify Human Mobility Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment

机译：在资源匮乏的城市环境中使用GPS技术来量化人员流动动态联系和传染病动态

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Empiric quantification of human mobility patterns is paramount for better urban planning, understanding social network structure and responding to infectious disease threats, especially in light of rapid growth in urbanization and globalization. This need is of particular relevance for developing countries, since they host the majority of the global urban population and are disproportionally affected by the burden of disease. We used Global Positioning System (GPS) data-loggers to track the fine-scale (within city) mobility patterns of 582 residents from two neighborhoods from the city of Iquitos, Peru. We used ∼2.3 million GPS data-points to quantify age-specific mobility parameters and dynamic co-location networks among all tracked individuals. Geographic space significantly affected human mobility, giving rise to highly local mobility kernels. Most (∼80%) movements occurred within 1 km of an individual’s home. Potential hourly contacts among individuals were highly irregular and temporally unstructured. Only up to 38% of the tracked participants showed a regular and predictable mobility routine, a sharp contrast to the situation in the developed world. As a case study, we quantified the impact of spatially and temporally unstructured routines on the dynamics of transmission of an influenza-like pathogen within an Iquitos neighborhood. Temporally unstructured daily routines (e.g., not dominated by a single location, such as a workplace, where an individual repeatedly spent significant amount of time) increased an epidemic’s final size and effective reproduction number by 20% in comparison to scenarios modeling temporally structured contacts. Our findings provide a mechanistic description of the basic rules that shape human mobility within a resource-poor urban center, and contribute to the understanding of the role of fine-scale patterns of individual movement and co-location in infectious disease dynamics. More generally, this study emphasizes the need for careful consideration of human social interactions when designing infectious disease mitigation strategies, particularly within resource-poor urban environments.
机译：对人类出行方式进行经验性量化对于更好的城市规划，了解社会网络结构以及应对传染病威胁至关重要，尤其是在城市化和全球化迅速发展的情况下。这种需求对发展中国家特别重要，因为它们拥有全球大多数城市人口，并受到疾病负担的不成比例的影响。我们使用全球定位系统（GPS）数据记录器来跟踪来自秘鲁伊基托斯市两个社区的582名居民的精细规模（城市内）流动性模式。我们使用了约230万个GPS数据点来量化所有被跟踪个体之间特定年龄段的移动性参数和动态共置网络。地理空间极大地影响了人类的机动性，从而产生了高度局部的机动性内核。大多数（〜80％）动作发生在距家1公里以内的地方。个体之间每小时的潜在接触高度不规则且在时间上没有组织。只有高达38％的受跟踪参与者显示出规律且可预测的出行习惯，这与发达国家的情况形成鲜明对比。作为案例研究，我们量化了时空非常规程序对伊基托斯社区内流感样病原体传播动力学的影响。相较于用时间结构化人际关系进行建模的情景，暂时性的非结构化日常活动（例如，不受工作场所等单个地点的支配，一个人反复花费大量时间）使流行病的最终规模和有效繁殖数增加了20％。我们的发现提供了对在资源匮乏的城市中心内塑造人类流动性的基本规则的机械描述，并有助于理解个体活动和共址的精细模式在传染病动态中的作用。更普遍地说，这项研究强调在设计减轻传染病的策略时，尤其是在资源贫乏的城市环境中，需要仔细考虑人类社会的相互作用。

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