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

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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.