Measuring Contact Rates, Mixing Patterns, and Influenza Transmission in Schools

描述（由申请人提供）：预防和控制传染病在人类中的传播是公共卫生的一个关键目标。数学和计算模型可用于预测疾病动态和评估控制工作的效果，但为了做出可靠的预测和建议，模型需要建立在对疾病传播和人类接触模式的清晰认识的基础上。由于学龄儿童是许多传染病，特别是流感的哨点人群，因此关于学龄儿童在其社区——学校、家庭和其他地方——的接触率和混合模式的数据非常重要。不幸的是，这类数据的缺乏严重增加了模型预测的不确定性。此外，学龄儿童的疾病负担在多大程度上是由校内疾病传播造成的，而不是由校外传播造成的，这在很大程度上是未知的。这些知识对于评估关闭学校等社会距离措施至关重要。我们将通过收集学龄儿童及其接触者的主客观互动和位置数据，以及实施监测系统研究学校疾病动态来解决这些问题。我们将结合使用调查、移动电话和无线传感器网络（WSN）技术来测量各种环境下学龄儿童的接触网络和混合模式。我们还将在学校流感爆发期间对病毒基因组进行测序，以便可靠地建立学校内的传播链。我们拥有一支在测量方法、无线传感器网络技术、移动电话应用开发、流感分子遗传学以及疾病动力学数学和计算建模方面经验丰富的多学科团队，我们将以前所未有的水平生成一个案例研究，捕捉流感在学校人群中的传播。这些数据将使我们能够㈠切实地参数化传染病在学校和相关人群中的传播模型，㈡高可信度地评估和确定有效的缓解战略，㈢比较接触者数据收集的主观和客观方法，以及㈣确定学龄儿童疾病负担的校内传播水平。总的来说，这项研究将提供新的见解，将显著改善疾病预防和控制策略的发展。

DESCRIPTION (provided by applicant): Preventing and controlling infectious disease spread in humans is a key objective of public health. Mathematical and computational models can be used to predict disease dynamics and to assess the efficacy of control efforts, but in order to make reliable predictions and recommendations, models need to based on a clear understanding of both disease transmission and human contact patterns. Because school-aged children are sentinel cohorts for many infectious diseases, particularly influenza, data on contact rates and mixing patterns of school-aged children within their communities - schools, households and others - are of great importance. Unfortunately, there is a paucity of such data, severely adding to the uncertainty of model predictions. Furthermore, the extent to which disease burden in school-aged children is due to within-school disease transmission, rather than to transmission outside of the school, is largely unknown. Such knowledge is essential to evaluate social distancing measures such as school closure. We will address these issues by collecting subjective and objective interaction and location data of school-aged children and their contacts, and by implementing a surveillance system to study disease dynamics at schools. We will use a combination of surveys, mobile phones, and wireless sensor network (WSN) technology to measure contact networks and mixing patterns of school-aged children in a wide range of settings. We will furthermore sequence the complete viral genomes during influenza outbreaks at schools in order to reliably establish chains of transmission within the school. With a multi-disciplinary team experienced in surveying methods, wireless sensor network technology, mobile phone application development, molecular genetics of influenza, and mathematical and computational modeling of disease dynamics, we will generate a case study capturing influenza spread in a school population at an unprecedented level. The data will allow us to i) realistically parameterize infectious disease spread models in schools and linked populations, ii) evaluate and identify effective mitigation strategies with high confidence, iii) compare subjective and objective methods of contact data collection, and iv) identify the level of within-school transmission on disease burden in school-aged children. Overall, the study will provide novel insights that will significantly improve the development of disease prevention and control strategies.