In response to the COVID-19 pandemic, the mathematical sociology community has been active in contributing its expertise to both combat and better understand the implications of this unfolding disaster. The following is a brief sample of some of the work being undertaken by our community.
Modeling SARS-CoV-2 Diffusion
Models have been central to both predicting the impacts of COVID-19 and developing countermeasures. For instance, as part of the Colorado School of Public Health modeling team advising Governor Polis, Jimi Adams is developing improved estimates of social mixing for contact patterns, and the effects of social distancing on those estimates. Jorge Zazueta and colleagues are working on diffusion models that better account for differing rates of infection and recovery by sex, while John Hipp, Zack Almquist, and Carter Butts, with students Loring Thomas, Peng Huang, and Fan Yin, are applying network models to understand how spatial heterogeneity leads to disparities in impact, timing, and caseloads for COVID-19 at the local level. Ben Cornwell and Kim Weeden are using University administrative data to study the implications of course enrollment patterns for potential disease diffusion on campuses. On the social distancing front, Zack Almquist and Jamie Jones are collecting high-resolution behavioral data relevant to disease transmission, while Tim Liao is modeling the role of collective action in maintaining effective social distancing strategies (see the Methodology Section article in this collection). Research by Arnout van de Rijt uses network models to evaluate post-lockdown policies that focus on targeting long-range and bridging ties, with implications for how a second infection wave can be avoided.
Messaging and Communication
Communication is central to the COVID-19 response. Emma Spiro, with colleagues Jevin West, Kate Starbird, Ryan Calo, and Chris Coward, is working to understand the role that scientific expertise and credentialing play in amplification of content online, while Kathleen Carley and colleagues are employing network analysis, machine learning, and natural language processing to examine public discussion around COVID-19, with an emphasis on politicization, polarization, and misinformation. Carter Butts and Jeannette Sutton, with students Scott Renshaw, Richard Gardner, and Sabrina Mai, are measuring and modeling the messaging and engagement strategies used by public health agencies on social media to identify evidence-based strategies for effective communication during the unfolding pandemic.
Members of our section have also been active in public communication themselves. For instance, Gianluca Manzo is providing commentary for policy makers and the public on the assumptions that underlie epidemiological models, and how these should guide their interpretation and appropriate use. Mikaela Springsteen is creating an educational resource called “Counting COVID-19,” a series of interactive web-based apps which allow exploration of virus trends at the state and county levels, while Martina Morris produced an interactive tool called “Just One Friend” that uses social network models to help communicate how and why social distancing measures work to a non-technical audience.
Epidemiological and Medical Initiatives
Many members of the community are directly involved in efforts to fight the pandemic. For instance, David Schaefer, Derek Kreager, Jacob Young, and Gary Zajac are working with the Pennsylvania Department of Corrections to understand and mitigate the transmission of COVID-19 in the state prison system. James Hollander, Lori Fischbach, and Blaine Tottori are working to develop and test models to estimate types of COVID-19 fatalities over time, as well as systems to communicate this information to help guide social distancing policies. And in the search for treatments, Carter Butts, together with a consortium of colleagues from the biological and physical sciences, is adapting social network methods to aid drug discovery for SARS-CoV-2.
Understanding the Social, Cultural, and Economic Impacts
Our members are heavily engaged in tracking the impact of the pandemic on our society. For example, Jenn Sims and colleagues are applying eye-tracking techniques to examine how the wearing of face masks alters social perception and interacts with perceptions of race. Lynn Smith-Lovin, Robert Freeland, Kimberly Rogers, Jesse Hoey, and Joseph Quinn are collecting pre/post pandemic data on cultural sentiments regarding occupations especially visible in, or impacted by, the pandemic, providing a window into how COVID-19 is changing perceptions of those in critical social roles. On the educational front, David Schaefer and colleagues are investigating how the shift to remote learning is affecting social networks, psychosocial adjustment, and academic outcomes for first-year STEM majors. James Kitts, with collaborators John Sirad, Mark Pachucki, Lindiwe Siebeko, and Krista Gile, is collecting longitudinal data on urban middle schools, investigating the impacts of social distancing interventions on relationships among students, health behaviors, and health outcomes. Turning to the broader community, Kinga Makovi, Malte Reichelt, and Maria Abascal are collecting data on health, employment, personal networks, and behavior in multiple countries, to shed light on the short- and long-term consequences of the pandemic for health and other inequalities. Ben Cornwell and colleagues are studying a nationally representative sample of older adults to understand how their social network connectedness and health care utilization have been affected by the coronavirus pandemic, while Ashton Verdery and colleagues are using computational models of kinship networks to develop estimates of the potential bereavement burden associated with COVID-19 under different epidemiological scenarios.
These projects illustrate both the breadth and depth of the involvement of the mathematical sociology community in the COVID-19 response, as well as the ways in which sociology is making both basic science and life-saving policy contributions during this catastrophic event.