Approach: Conducting multiple waves of a survey that provides regular snapshots of contact patterns in the US population. The survey is conducted on an established panel of individuals that is nationally representative by age, gender, and race/ethnicity. For each participant, the survey collects detailed information about the number and characteristics of their contacts.
Advancements: Quantifying US contact patterns in different socio-demographic and economic contexts for all ages and racial/ethnic groups. The obtained estimates will be integrated into mathematical models to provide situational awareness of human behavior relevant for transmission of the respiratory and other close-contact pathogens.
Contact between individuals are not only the fabric of our social environment but also the means used by a pathogen to spread in the population. Modeling the transmission processes of infectious diseases requires understanding of the complex social contact dynamics of modern societies, which result in highly heterogeneous risks of infection. Our approach will provide novel and crucial information to inform modeling and analytic techniques, thus resulting in higher forecasting and prediction capabilities as well as more accurate “in-silico” evaluations of public health interventions.
Conceptual figure of the contact survey.
Estimates cover the number of contacts across four primary settings: household, school, workplace, and other environments. Contact characteristics are captured along multiple dimensions, enabling the integration of new socio-demographic factors into the modeling tools during the planning phase.Data collection will be conducted repeatedly across different time periods, ensuring the information remains up-to-date and capturing seasonal patterns and holidays behavior. Furthermore, the survey instrument is prepared for rapid deployment, offering real-time estimates and detecting potential shifts in contact patterns during an outbreak.
We developed a cross-sectional, diary-based survey that collects data on the contact patterns of the US population. The first wave of the survey included 963 respondents and was completed in May 2024. The obtained sample is statistically representative of the US population by age, gender, and race/ethnicity. The survey was administered online by Ipsos, a widely used data supplier with experience in health-related surveys[1], to their established panel of survey responders (panelists). A contact was defined as a person with whom the participant had an in-person, two-way conversation, exchanging 5 or more words, and/or had physical contact[2],[3]. Individuals of all ages were invited to participate. For participants 0-8 years old, parents/guardians completed the survey. Participants 9-12 years old completed the survey with their parent/guardian. Participants 13-17 years old were asked to complete the survey on their own with parent/guardian consent. To address potential language barriers, the survey instrument was offered in English and Spanish, allowing participants to complete the questionnaire in their preferred language. Using statistical modeling, we assessed the relationship between the sociodemographic characteristics of the participants and the total number and characteristics of the contacts they reported.
We observe an average number of 6.2 contacts per day. The number of contacts varies with age. School-age individuals report the highest number of contacts, followed by working-age individuals. Specifically, individuals ages 5-9 report the largest number of contacts with 10.4 contacts per day, and older adults (80+ years) report the lowest number, with 3.1 contacts per day. The number of contacts is not statistically different between males and females (6.0 vs. 6.4 contacts per day, respectively). We did find differences in the number of reported contacts by race/ethnicity; Not Hispanic (NH) Black individuals reported a statistically significantly lower number of contacts than NH White individuals (5.2 vs. 6.3 contacts per day), Hispanic individuals reported the largest number of contacts (7.0 contacts per day).
Number of contacts per day reported by study participants and disaggregated by participant’s characteristics (age, gender, and race/ethnicity).
We will analyze the relationship between the sociodemographic characteristics of participants and those of their contacts, generating urgently needed contact matrices stratified by factors such as age, race/ethnicity, and gender. These contact matrices are crucial for informing modeling studies, enhancing their predictive accuracy and supporting more informed public health decision-making. Additionally, the contact matrices will be compared to synthetic estimates[4,5], paving the way for improved analytical approaches to integrating contact patterns into modeling frameworks.
• Participants reported an average of 6.2 contacts per day, lower than pre-COVID estimates for other high-income countries. This reflects social changes brought by the COVID-19 pandemic such as changes in workplace organization, school and education changes, increased use of home delivery services, changes in transportation, and shift in social behavior.
• The number of contacts varied with age, with school age individuals reporting the largest number of contacts and older adults reporting the lowest.
• No statistically significant difference in thenumber of contacts by gender was found.
• The number of contacts was different by race/ethnicity with NH Black reporting the lowest average number of contacts and Hispanic reporting the highest.
Our proposed application will develop innovative methodologies to integrate advanced statistical and analytical frameworks and machine intelligence with mechanistic modeling techniques, identifying new approaches that improve local, state, and regional forecasting and modeling capabilities and analytics tools. The proposed activities involve integrating novel data sources—including high-resolution mobility, airline travel, genomic and wastewater surveillance data—with agent-based, statistical, and deep learning forecasting models to increase the accuracy of outbreak analytic products. Importantly, our approach will consider population heterogeneities/disparities and will deliver outbreak analytic tools for rural/underserved populations and for diseases/locations with low prevalence. Finally, we will identify best practices for how to transfer and maintain the needed methodology, technical expertise, and data sources, and establish a comprehensive training program for public health workforce and emergency response decision makers (which includes embedding co-op students into public health/healthcare delivery agencies and developing collabathons).
1. Ipsos M. Healthy Ireland survey 2015: summary of findings. Department of Health (DoH); 2016.
2. Zhang J, Litvinova M, Liang Y, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science.2020;368(6498):1481-1486.
3. Litvinova M, Liu Q-H, Kulikov ES, Ajelli M. Reactive school closure weakens the network of social interactions and reduces the spread of influenza. Proceedings of the National Academy of Sciences. 2019;116(27):13174-13181.
4. Fumanelli L, et al. (2012) Inferring the Structure of Social Contacts from Demographic Data in the Analysis of Infectious Diseases Spread. PLoS Comput Biol 8(9): e1002673.
5. Mistry, D, et al. "Inferring high-resolution human mixing patterns for disease modeling." Nature communications 12.1 (2021): 323.
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