Social Mixing And Respiratory Transmission in Schools

Web Survey

In addition to our school-based activities in Pittsburgh, PA, we have an internet-based survey for anyone in the United States to take. The objective of this study is to collect information about daily contacts and mixing patterns from a large cross-section of the U.S. population. We aim to collect enough information to detect variation in age groups and geographical location across the country.
How do I participate?
Anyone in the United States is eligible to participate in this study. First, follow this link to the survey. Once you agree to participate, you will be asked a few questions about yourself and about your interactions with other people (your contacts). The survey will take 15-45 minutes to fill out. Your participation is voluntary and you may withdraw at any time by exiting the window. Once you have completed the survey, you will have a chance to win a $10 gift card to iTunes or Amazon. You will also be able to create a username so you can return and complete our survey again.
This survey has already been administered successfully to approximately 2000 school-aged children through the SMART in Schools study. We have slightly modified surveys for adult and schoolchildren populations.
Who will see my answers?
Only our study team will have access to the data collected from this survey. The data will not include any names entered or any information that can link the data back to you. Your data will be combined with everyone else's data and analyzed in aggregate.
Why do we need this information?
In short, this knowledge will also allow researchers to refine models of infectious disease transmission and seek better ways for disease prevention and control.
The longer version:
When a new or existing infection emerges, health planners require early prediction of the likely course and impact. Mathematical models of infectious spread permit the exploration of large-scale interventions and their consequences; however, they demand highly precise empirical estimates of critical factors or processes. A key unknown is the mixing rates and patterns of encounters relevant to the spread of infections. Thus, to determine the efficacy of a proposed control intervention or where best to target limited prophylactic resources, precise quantification of mixing patterns are required.
The data collected from these large studies on social mixing and contact patterns can improve models to advise prevention strategies and interventions for respiratory interventions. In Europe, information from contact surveys has been found very useful in parameterizing models of respiratory disease transmission, and these models have been used to guide policy for response to pandemic influenza and respiratory infectious diseases of childhood. However, no large study of these contact patterns has been done in the United States. Dynamical infectious disease models were used extensively during the H1N1 influenza pandemic in 2009 to understand the natural history of infection, the rate of growth of national epidemics, the patterns of incidence likely to develop and the efficacy of various control options. In the absence of any domestic data, modelers within the US relied upon estimates of mixing patterns generated in a European study to parameterize their models.
A central dilemma in vaccination policy is where best to deploy limited stocks of vaccines. Vaccination strategies which attempt to depress epidemic spread through targeting high incidence groups (rather than induce full herd immunity or protect high risk individuals) have recently been proposed which would seek to vaccinate school-age children due to the high mixing rates (Medlock & Galvani 2009; Keeling & White 2011). Accurate parameterization of contact patterns, specific to US school-age kids, as well as the general public, is crucial to determine if such strategies are viable or effective. This study will provide significant information with which to assess and fine-tune such control options.
Finally, understanding contact patterns or behaviors found to a risk of transmission will improve health interventions to be designed. Some of these may include individual- scale behaviors (such as particular mixing behaviors that are associated with increased risk) or may be due to the architectural design of schools / workplaces or the management of particular activities. Many aspects of disease control would benefit from precise quantification of social contact patterns of the general public.
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