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：
10610730
负责人：
Benjamin A Lopman
金额：
$ 50.45万
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 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10610730
关键词：
Africa South of the Sahara Age Age Months Asia Birth Order Central America Cessation of life Child Childhood Collaborations 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 Inequity 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 Predisposition Recording of previous events Research Personnel Resolution Resources Respiratory Disease Role Rural Rural Population Seasons Site Social Interaction Social Sciences Standardization Subgroup System Urban Population Vaccinated Vaccines Vulnerable Populations age group 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 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.

项目摘要/摘要传染病的动态传播模型对制定干预措施并为政策提供信息。传染病的传播性和因此，控制策略的有效性受到社会的强烈影响互动。因此，有关社会接触率和混合模式的准确数据是感染力量计算中的基本参数（即易感人被感染）。尽管社会混合起着强烈的作用模式在数学模型的准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）。也有关于年轻婴儿的社交互动的数据有限在农村人口和城市人口之间的疫苗接种或多样性社区层面，这是了解传染病的重要因素传播。我们提出了第一个多站点研究，其总体目标是使用标准化方法从LMIC中收集来自城市和农村人口的社会联系数据。特殊的重点可以研究不到六个月大的婴儿的社交互动。数据将严格从四个不同的LMIC中收集：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接触日记来表征模式在城市和农村LMIC环境中，社会接触和混合范围的混合。我们还将全面介绍婴儿与家庭的社交联系 LMIC的成员通过分析使用可穿戴的高分辨率测量值接近感应设备。此外，通过这个项目，我们将创建一个有关LMIC的社交混合数据数据库人群。我们将使用当代标准公开提供此数据库在开放访问数据共享和文档中。这些数据可以由传染性使用疾病建模者和生物医学和社会科学的其他研究人员社区。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol.

资源匮乏国家社会混合模式的综合分析：混合方法研究协议。

DOI：
10.1101/2023.12.05.23299472
发表时间：
2023
期刊：
medRxiv : the preprint server for health sciences
影响因子：
0
作者：
Aguolu,ObianujuGenevieve;Kiti,MosesChapa;Nelson,Kristin;Liu,CarolY;Sundaram,Maria;Gramacho,Sergio;Jenness,Samuel;Melegaro,Alessia;Sacoor,Charfudin;Bardaji,Azucena;Macicame,Ivalda;Jose,Americo;Cavele,Nilzio;Amosse,Felizarda;U
通讯作者：
U

Rapid Review of Social Contact Patterns During the COVID-19 Pandemic.

DOI：
10.1097/ede.0000000000001412
发表时间：
2021-11-01
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
0
作者：
Liu CY;Berlin J;Kiti MC;Del Fava E;Grow A;Zagheni E;Melegaro A;Jenness SM;Omer SB;Lopman B;Nelson K
通讯作者：
Nelson K

Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021.

COVID-19 大流行期间美国工人社交接触模式的变化：2020 年 4 月至 2021 年 12 月。

DOI：
10.1101/2022.12.19.22283700
发表时间：
2022
期刊：
medRxiv : the preprint server for health sciences
影响因子：
0
作者：
Kiti,MosesC;Aguolu,ObianujuG;Zelaya,Alana;Chen,HolinY;Ahmed,Noureen;Battross,Jonathan;Liu,CarolY;Nelson,KristinN;Jenness,SamuelM;Melegaro,Alessia;Ahmed,Faruque;Malik,Fauzia;Omer,SaadB;Lopman,BenA
通讯作者：
Lopman,BenA

Dynamic network strategies for SARS-CoV-2 control on a cruise ship.

DOI：
10.1016/j.epidem.2021.100488
发表时间：
2021-12
期刊：
Epidemics
影响因子：
3.8
作者：
Jenness SM;Willebrand KS;Malik AA;Lopman BA;Omer SB
通讯作者：
Omer SB

Social contact patterns among employees in 3 U.S. companies during early phases of the COVID-19 pandemic, April to June 2020.

DOI：
10.1016/j.epidem.2021.100481
发表时间：
2021-09
期刊：
Epidemics
影响因子：
3.8
作者：
Kiti MC;Aguolu OG;Liu CY;Mesa AR;Regina R;Woody M;Willebrand K;Couzens C;Bartelsmeyer T;Nelson KN;Jenness S;Riley S;Melegaro A;Ahmed F;Malik F;Lopman BA;Omer SB
通讯作者：
Omer SB