Kiti Moses Chapa, Melegaro Alessia, Cattuto Ciro, Nokes David James
Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya.
Department of Social and Political Sciences, Bocconi University, Milan, Italy.
Wellcome Open Res. 2019 Aug 22;4:84. doi: 10.12688/wellcomeopenres.15268.2. eCollection 2019.
Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.
社交接触模式塑造了通过密切互动传播的呼吸道感染的传播方式。来自学校和家庭的观察数据匮乏，尤其是在发展中国家。便携式无线传感器可以记录个体之间面对面的无偏差接近事件，从而揭示感染传播途径。目的是描述可能影响肯尼亚基利菲学校和家庭中呼吸道感染传播的面对面接触模式。将有目的地选择两所学校，一所来自农村地区，一所来自城市地区。从每所学校中，将按班级规模和性别比例随机选择350名学生参与。将从每所学校中随机选择9名索引学生，并跟踪到他们的家庭。所有索引家庭的居民都将被纳入研究。还将招募另外3 - 5个相邻家庭，使每个家庭环境中的参与者最多达到350人。每个地点的样本量受可用于数据收集的传感器数量限制。每位参与者将在胸部区域佩戴一个无线接近传感器，连续佩戴7天。传感器仅会自动收集面对面的参与者之间近端二元互动的数据。感兴趣的关键特征包括度数分布、接触频率和持续时间以及它们在农村和城市地区的差异。这些将按年龄、性别、角色和星期几进行分层。所得数据将为一个此前未被研究的人群的农村和城市地区的社交接触模式提供信息。后续数据将用于参数化一系列呼吸道病毒（包括呼吸道合胞病毒）传播的数学模拟模型，并用于探索疫苗接种和社交距离等干预措施的影响。
