Comprehensive Profiling of Social Mixing Patterns in Resource Poor Countries

项目概要/摘要 传染病的动态传播模型越来越有影响力， 制定干预措施并为政策提供信息。传染病的传染性和 因此，控制策略的有效性受到社会因素的强烈影响。 交互.因此，关于社会接触率和混合模式的准确数据 是计算感染力的基本参数（即， 易受感染的个体）。尽管社会融合的强大作用 模式在数学模型的精确参数化中发挥作用，这些数据 特别是在低收入和中等收入国家。也有 关于太小而不能被社会化的婴儿的社会互动的数据有限。 农村和城市人口之间的模式多样性， 社区水平，这是了解传染病的重要因素 传输 我们提出了第一个多中心研究，其总体目标是使用标准化方法， 收集低收入国家城市和农村人口的社会接触数据。特别重点将 研究不到六个月大的婴儿的社会互动。数据将 从四个不同的LMIC严格收集：危地马拉，巴基斯坦，印度和 莫桑比克.我们将使用标准化的社交日记来描述这些模式 在城市和农村低收入国家环境中，社会接触和混合的年龄范围。我们 还将全面分析婴儿与其家庭的社会联系， 通过分析使用可穿戴设备收集的高分辨率测量结果， 接近感应装置。 此外，通过这个项目，我们将创建一个关于LMIC的社会混合数据库 人口数量。我们将使用当代标准公开这个数据库 开放获取数据共享和文档。这些数据可以用于传染病 疾病建模者和其他生物医学和社会科学研究人员 社区.

PROJECT SUMMARY/ABSTRACT Dynamic transmission models of infectious diseases are increasingly influential for developing interventions and informing policy. Infectious disease transmissibility and hence, the effectiveness of control strategies, is strongly influenced by social interactions. Consequently, accurate data on social contact rates and mixing patterns are fundamental parameters in the calculation of the force of infection (i.e. the rate of susceptible individuals becoming infected). Despite the strong role social mixing patterns play in the accurate parameterization of mathematical models, these data remain limited, particularly in low and middle-income countries (LMICs). There are also limited data on the social interactions of young infants that are too young to be vaccinated or the diversity in patterns between rural and urban populations at the community level, which are important factors for understanding infectious disease transmission. We propose the first multi-site study with the overall goal to use standardized methods to collect social contact data from urban and rural populations in LMICs. Special focus will be given to study the social interactions of infants less than six months of age. Data will be rigorously collected from four different LMICs: Guatemala, Pakistan, India and Mozambique. We will use standardized social contact diaries to characterize the patterns of social contacts and mixing across the age range in urban and rural LMIC settings. We will also comprehensively profile the social contacts of infants with their household members in LMICs by analyzing high resolution measurements collected using wearable proximity-sensing devices. Moreover, through this project, we will create a database of social mixing data on LMIC populations. We will make this database publicly available using contemporary standards in Open Access data sharing and documentation. These data can be used by infectious disease modelers and other researchers in the biomedical and social science communities.