Comprehensive Profiling of Social Mixing Patterns in Resource Poor Countries

资源匮乏国家社会混合模式的综合分析

基本信息

批准号：
10397072
负责人：
Benjamin A Lopman
金额：
$ 52.93万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-19 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10397072
关键词：
Africa South of the Sahara Age Age-Months Asia Birth Order Central America Cessation of life Child Childhood Communicable Diseases Communities Country Data Databases Decision Making Development Devices Disease Documentation Effectiveness Ensure Enteral European Geographic Locations Geography Goals Government Guatemala Health Policy Herd Immunity Hospitalization Household Immunity Immunization Programs India Individual Infant Infection Influentials Infrastructure Intervention Investments Location Low Income Population Low income Measurement Methods Modeling Morbidity - disease rate Mozambique Pakistan Pattern Persons Play Policies Population Recording of previous events Research Personnel Resolution Resources Respiratory Disease Role Rural Rural Population Site Social Interaction Social Sciences Standardization Subgroup System Urban Population Vaccinated Vaccines Vulnerable Populations age group base data access data sharing diaries disease transmission disorder control infectious disease model innovation low and middle-income countries mathematical model member open data public database rural setting season of birth sex social social contact social determinants social influence tool transmission process urban setting

项目摘要

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.

项目概要/摘要传染病的动态传播模型对人类的影响越来越大制定干预措施并通报政策。传染病的传播能力和因此，控制策略的有效性受到社会的强烈影响。互动。因此，有关社交接触率和混合模式的准确数据是计算感染力的基本参数（即感染率）易感人群被感染）。尽管社会融合发挥着重要作用模式在数学模型的精确参数化中发挥作用，这些数据仍然有限，特别是在低收入和中等收入国家 (LMIC)。还有关于婴儿社交互动的数据有限，因为婴儿太小了，无法进行社交互动。疫苗接种情况或农村和城市人口之间的模式差异社区层面，这是了解传染病的重要因素传播。我们提出了第一个多地点研究，总体目标是使用标准化方法收集中低收入国家城乡人口的社会联系数据。特别关注将用于研究六个月以下婴儿的社交互动。数据会从四个不同的中低收入国家严格收集：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模式城市和农村中低收入国家环境中跨年龄范围的社会接触和混合。我们还将全面描述婴儿与其家庭的社会接触通过分析使用可穿戴设备收集的高分辨率测量结果，中低收入国家的成员接近传感设备。此外，通过这个项目，我们将创建一个 LMIC 上的社交混合数据数据库人口。我们将使用当代标准公开该数据库开放获取数据共享和文档。这些数据可以被感染者使用疾病建模者和生物医学和社会科学领域的其他研究人员社区。