RP2: 2nd ANNUAL NSF RAPID PI MEETING
December 8-9, 12pm - 5:30pm ET
It has been just over a year since the PREPARE (Pandemic Research for Preparedness and Resilience virtual organization was created, and we’re intent on building a community focused on pandemic preparedness and resilience. As we work to maximize the collaborative synergies of the outstanding research completed through the NSF RAPID grant program, we’re excited to offer you this opportunity to present your work, learn from your colleagues, and seek collaborative opportunities at RP2: PREPARE 2nd Annual RAPID PI Meeting.
Unlimited opportunities to change the world.
PREPARE has planned three workshops to address some of the most critical challenges of our time.
May 12 & 13 // DATA: Access, Creation, and Maintenance of Data and Computing Resources
June 24 & 25 // SBEG: Social, Behavioral, Economic, and Governance Issues during a Pandemic
Jan/Feb 2022 // HPC: Role of Computing Technology in Anticipating, Controlling, Responding to, and Assessing Pandemics Details coming soon!
Join our virtual organization to stay connected to this vital multi-disciplinary community - message [email protected]
HPC: Role of Computing Technology in Anticipating, Controlling, Responding to, and Assessing Pandemics
Details coming soon!
There will be four focus areas:
- Fostering and promoting effective epidemiology for decision makers
- Three levels of emerging architecture: node level, machine level, large-scale sensor systems
- Data considerations within HPC
- Expanding community's use of HPC
Each session will feature keynote talks followed by a panel discussion.
Madhav Marathe, John Towns, Laxmi Parida, Arvind Ramanathan
Summary Report available here
Poster Session Master List (note that posters are sorted based on the name of the first author)
Keynote Speaker - Professor Sir Roy Anderson FRS FMedSci "Where do people acquire SARS-CoV-2 infection and the challenges in creating herd immunity by mass vaccination"
Watch Sir Roy's complete talk on YouTube
Sir Roy's slides available here
Opening remarks by Dr. Margaret Martonosi, NSF Assistant Director for CISE and by Dr. Gurdip Singh, NSF Division Director of CNS, can be viewed on our YouTube channel
The goal of the workshop is to bring together the pandemic research community, discuss the challenges and opportunities associated with access, creation, and maintenance of data and computing resources for pandemic research, and foster collaborations for future research. The workshop will feature speakers from academia, industry, and government to share their perspectives as data providers, data consumers (domain researchers, health agencies/operations), and data researchers (infrastructure, privacy, security, and ethics).
Together, we will examine questions including but not limited to the following. The expected outcome is a better understanding from the community and a workshop report that contributes to the research roadmap of PREPARE.
- What data can be collected? What are the hurdles to collect the data?
- What data can be shared? What are the hurdles to share the data?
- What data is most useful? What are the hurdles to access the data?
- What technology/infrastructure has worked well, and what is still needed?
- What policy/operational mechanisms have worked well, and what are still needed?
- What are the key differences (if any) and issues for supporting research use and operational use of the data for decision making?
- What are the key privacy, security, and ethical issues for access and use of the data?
Each synchronous session (1~1.5 hours) will feature keynote talks followed by a mini-panel. Asynchronous discussion sessions will continue on YouTube after the synchronous sessions.
Day 1, May 12, 11am EDT: Mobility, Search, and Social Network Data
Public Health @ Google // Evgeniy Gabrilovich, Google Health
Abstract: In this talk we will discuss the use of anonymized and aggregated online signals to improve public health.
Bio: Dr. Evgeniy Gabrilovich is a research director at Google Health where he leads the Public & Environmental Health team. Prior to joining Google in 2012, he was a director of research and head of the natural language processing and information retrieval group at Yahoo! Research. Evgeniy is an IEEE Fellow and ACM Distinguished Scientist. He is a recipient of the 2014 IJCAI-JAIR Best Paper Prize and the 2010 Karen Sparck Jones Award for his contributions to natural language processing and information retrieval. Evgeniy has served as a technical program chair for WSDM 2021, WWW 2017, and WSDM 2015. He earned his PhD in computer science from the Technion - Israel Institute of Technology. He also graduated (with extra credit) from the Executive MD training program at Harvard Medical School.
The i-sense response to COVID-19 surveillance using Web search // Ingemar Cox, Vasileios Lampos, University College London
Abstract: Established in 2013, i-sense is a large, multi-institutional, multi-disciplinary research collaboration to develop early warning sensing systems for infectious diseases. One focus of this work has been in digital epidemiology, with a particular emphasis on influenza surveillance. Our work has encompassed disease surveillance at mass gathering, measuring the effectiveness of vaccination programs, and estimating the serial attack rate and serial interval of influenza. Our national influenza surveillance estimates are incorporated into Public Health England’s (PHE) weekly influenza surveillance reports beginning in 2017. In 2020, PHE initially requested that we modify this surveillance methodology to support national COVID-19 surveillance. Later, PHE requested information at a subnational level to identify regional anomalies in COVID-19 prevalence. This talk will describe the solutions we developed, the technical, legal, and ethical issues we needed to address, and reflects on what needs to be done to facilitate a better to response to a future pandemic.
Bio: Ingemar J. Cox is currently a Professor in the Department of Computer Science at University College London (UCL). He is also a Professor in the Department of Computer Science at the University of Copenhagen. He is Head of the Future Media Group at UCL and a deputy director of a £15M EPSRC Interdisciplinary Research Collaboration on "Early Warning Sensor Systems for Infectious Diseases", called i-sense. He is a Fellow of the ACM, IEEE, the IET (formerly IEE), and the British Computer Society. He has been a recipient of a Royal Society Wolfson Fellowship (2002-2007), a 2015 IEEE Signal Processing Society Sustained Impact Paper Award, and the Tony Kent Strix Prize for contributions to information retrieval (2019).
Facebook Data for Good // Alex Dow, Facebook
Abstract: Since 2016, the Facebook Data for Good program has leveraged Facebook data and technology to empower NGOs and researchers for social impact while protecting user privacy. In this talk, I’ll describe several Data for Good offerings related to disease prevention and the Covid19 pandemic, and I’ll discuss how we approach privacy, security, efficacy, and access.
Bio: Alex Dow - I lead the Interaction Science team at Facebook, which is part of the Computational Social Science and Core Data Science groups. We study people’s interactions on and with Facebook products and what effect those products have on them, their relationships, and their community. We leverage this understanding to design new and better products and guide the larger product teams. Our research includes topics such as technology use and well-being, crisis informatics, data for social good, information diffusion, and the structure of online prosocial and antisocial behavior. We also develop the methodologies and technologies that power Facebook Data for Good, a program focused on empowering partners with privacy-preserving data products that strengthen communities and make progress on social issues. I received my PhD in computer science from UCLA, where I did research in artificial intelligence and heuristic search algorithms.
Day 1, May 12, 2pm EDT: Contact Tracing, Syndromic Surveillance, and Social Media
Developing and Deploying Digital Contact Tracing: Lessons learned // Marcel Salathé, EPFL
Bio: Marcel Salathé is a digital epidemiologist working at the interface of health and computer science. He obtained his PhD at ETH Zurich and spent two years as a postdoc in Stanford before joining the faculty at Penn State University in 2010 at the Center for Infectious Disease Dynamics. In 2014, he spent half a year at Stanford as visiting assistant professor. In the summer of 2015, he became an Associate Professor at EPFL where he heads the Digital Epidemiology Lab at the Campus Biotech in Geneva. In 2016, he founded the EPFL Extension School, whose mission is to provide high quality online education in digital technology, and where he is the Academic Director.
Digital Epidemiology and the COVID-19 Pandemic // John Brownstein, Harvard University and Boston Children’s Hospital
Abstract: “Digital Epidemiology and the COVID-19 Pandemic” will address the surveillance, control, and prevention of disease; the development and application of data mining; and citizen science to public health in relation to his work with the COVID-19 pandemic
Bio: John Brownstein, PhD is Professor of Biomedical Informatics at Harvard Medical School and is the Chief Innovation Officer of Boston Children’s Hospital. He directs the Computational Epidemiology Lab and the Innovation and Digital Health Accelerator both at Boston Children’s. He was trained as an epidemiologist at Yale University. Dr. Brownstein is also co-founder of digital health companies Epidemico and Circulation and an ABC News Medical Contributor.
Mobility Networks for Modeling the Spread of COVID-19: Explaining infection rates and informing reopening strategies // Jure Leskovec, Stanford University
Abstract: In this talk I will demonstrate how fine-grained epidemiological modeling of the spread of Coronavirus -- predicting who gets infected at which locations -- can aid the development of policy responses that account for heterogeneous risks of different locations as well as the disparities in infections among different demographic groups. We use U.S. cell phone data to capture the hourly movements of millions of people and model the spread of Coronavirus from among a population of nearly 100 million people in 10 of the largest U.S. metropolitan areas. We show that even a relatively simple epidemiological model can accurately capture the case trajectory despite dramatic changes in population behavior due to the virus. We also estimate the impacts of fine-grained reopening plans: we predict that a small minority of superspreader locations account for a large majority of infections, and that reopening some locations (like restaurants) pose especially large risks. We also explain why infection rates among disadvantaged racial and socioeconomic groups are higher. Overall, our model supports fine-grained analyses that can inform more effective and equitable policy responses to the Coronavirus.
Bio: I am Associate Professor of Computer Science at Stanford University, and investigator at Chan Zuckerberg Biohub. My general research area is applied machine learning for large interconnected systems focusing on modeling complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, computational social science, and computational biology with an emphasis on drug discovery.
Day 2, May 13, 11am EDT: Clinical and Epidemiological Data
Improving Pandemic Response: Employing Mathematical Modeling to Confront COVID-19 // Matt Biggerstaff, CDC
Abstract: Modeling has been integral to the COVID-19 response. This talk will review how CDC utilized epidemiological and laboratory data to inform public health decision making and policy development throughout the COVID-19 response, including the use of modeling to improve situational awareness, to synthesize and assess epidemiological characteristics that were important for understanding the use and impact of mitigation measures, and to inform the evidence base for mitigation strategies.
Bio: Dr. Matt Biggerstaff has been with CDC since 2006 and an epidemiologist with the Influenza Division since 2009. In this role, he leads CDC influenza forecasting and modeling activities and works to understand and evaluate how forecasting and mathematical modeling can complement influenza surveillance and inform seasonal and pandemic influenza public health actions. He has also led and supported CDC’s and U.S. government’s interagency modeling and forecasting response to the COVID-19 pandemic since January 2020.
COVID-19: Data Requirements for Clinical and Logistical Modeling // Nathaniel Hupert, Weill Cornell Medicine
Abstract: Reviewing clinically- and operationally-oriented modeling efforts from the earliest days of the COVID-19 outbreak, this talk will highlight important lingering gaps in epidemiological data streams and analysis that may hinder clinical “sense-making,” and therefore may hamper improved responses, for COVID-19 and future pandemics.
Bio: Dr. Hupert is a physician and researcher at Weill Cornell Medical College and Cornell University whose work has focused on public health emergency response logistics, including the COVID-19 pandemic. He currently serves as the translation and policy lead for the Oxford- and Cornell based COVID-19 International Modeling (CoMo) Consortium (https://como.bmj.com). He served for 10 years as Senior Medical Advisor in the US Centers for Disease Control (CDC) Division of Preparedness and Emerging Infections, and was also both a Medical Advisor for the US Hospital Preparedness Program and member of the Scientific Advisory Board of the US National Institute of Health’s Modeling of Infectious Disease Agent Study (MIDAS). He practices internal medicine as a hospitalist at New York City’s Lower Manhattan Hospital, and trained at the University of Pittsburgh and Harvard Medical School.
Day 2, May 13, 2pm EDT: Data Curation and Data Sharing
Clinical Data Network for COVID-19 // Lucila Ohno-Machado, University of California San Diego
Abstract: Data curation and harmonization are critical in observational studies based on Electronic Health Records (EHRs). I will describe how we developed a large clinical data research network based on EHRs from 14 health systems in the US and abroad, including more than 60 million patients, to answer COVID-19 questions. The Reliable Response Data Discovery (R2D2) clinical data network encountered issues related to different mappings to a Common Data Model, as well as other harmonization challenges that were overcome by active participation of data analysts from the various health systems. We used a federated model that protects the privacy of patients, while allowing for the development and evaluation of multivariate predictive models.
Differential Privacy and Pandemic Research // Salil Vadhan, Harvard University
Abstract: Differential privacy is a framework for enabling statistical analysis of sensitive data while providing strong guarantees of privacy to the individuals represented in the data. Since its introduction 15 years ago by Dwork, McSherry, Nissim, and Smith, differential privacy has developed a rich mathematical theory and has seen large-scale deployments by the US Census Bureau and technology companies like Google, Apple, and Microsoft.
In this talk, I will give an introduction to the basic concepts of differential privacy and discuss the state of its transition to practice, including OpenDP, a new community effort to develop trustworthy open-source software for using differential privacy. I will also share thoughts on the ways in which differential privacy can (and already does) help enable data sharing in support of pandemic research.
Bio: Salil Vadhan is the Vicky Joseph Professor of Computer Science and Applied Mathematics at the Harvard John A. Paulson School of Engineering & Applied Sciences. Vadhan’s research in theoretical computer science spans computational complexity, cryptography, and data privacy. Since 2012, he has led the Harvard Privacy Tools Project, a multi-institution research effort on data privacy that brings together computer science, law, social science, and statistics. Together with Gary King, he directs OpenDP, a new open-source software project around differential privacy. His honors include a Harvard College Professorship, a Simons Investigator Award, and a Guggenheim Fellowship.
There will be four focus areas:
- Equity and health disparities
- Governance and economic aspect
- Behavioral modeling
Each synchronous session will feature keynote talks followed by a panel discussion.
Day 1, June 24, 11am EDT: Governance and Economic Aspects
The Politics of Pandemic Othering and Trust: COVID-19 in Historical Perspective // Kim Yi Dionne, UC Riverside
Abstract: In a global politics characterized by racialized inequality, pandemics such as COVID-19 exacerbate the marginalization of already oppressed groups. Published research on previous pandemics historicize what we call pandemic othering and blame, and we enumerate some of the consequences for politics, policy, and public health. We draw on lessons from smallpox outbreaks, the third bubonic plague, the 1918 influenza pandemic, and more recent pandemics, such as HIV/AIDS, SARS, and Ebola to show that COVID-19 continues a long history of othering and blame during disease outbreaks. These earlier pandemics also offer insights into the role of trust in pandemic response and highlight the obstacles posed by racialized marginalization in generating the trust necessary to collectively combat infectious disease outbreaks.
Bio: Kim Yi Dionne (@dadakim) is an Associate Professor of Political Science at UC Riverside. Her research examines health interventions, politics, and public opinion—primarily in African countries. Her book, Doomed Interventions: The Failure of a Global Response to AIDS in Africa (Cambridge University Press), drew significantly on her research during a Fulbright Fellowship to Malawi.
Title // Bryan Lewis, Biocomplexity Institute, UVA
Bio: Bryan Lewis is a research associate professor in the Network Systems Science and Advanced Computing division. His research has focused on understanding the transmission dynamics of infectious diseases within specific populations through both analysis and simulation. Lewis is a computational epidemiologist with more than 15 years of experience in crafting, analyzing, and interpreting the results of models in the context of real public health problems.
Day 1, June 24, 2pm EDT: Equity and Health Disparities
The Role of Behavior, Mobility, and Social-Network Structure on COVID-19 Epidemics // Sam Scarpino, Northeastern
Abstract: The COVID-19 pandemic has upended our societies and re-shaped the way we go about our day-to-day lives—from how we work and interact to the way we buy groceries and attend school. Leveraging global data sets that represent billions of people, I will present a series of studies exploring how our behavior, mobility patterns, and social networks have altered and been altered by COVID-19 and the non-pharmaceutical interventions implemented to control its spread. Building on these studies, I will discuss work by Global.health, a new collaborative network of researchers, technologists, and public health experts that has developed and built an open access platform for collecting, storing, securing, and sharing anonymized, individual-level COVID-19 data. Currently, our data includes almost 30M individual-level cases from 160 countries, which are tagged with up to 40 fields of meta-data. Writing for The New York Times Magazine, Steven Johnson said the data captured by Global.health, "may well be the single most accurate portrait of the virus’s spread through the human population in existence."
Bio: Samuel V. Scarpino, PhD is an Assistant Professor in the Network Science Institute at Northeastern University and holds academic appointments in Physics, Health Sciences, the Khoury College of Computer Sciences, the Global Resilience Institute, and the Roux Institute. At Northeastern University, he directs the Emergent Epidemics Lab and is a Co-founder of Global.health. Scarpino has 10+ years of experience translating research into decision support and data science/ML tools across diverse sectors from public health and clinical medicine to real estate and energy. From 2017 to 2020, he was Chief Strategy Officer and head of data science at Dharma Platform–a social impact–technology startup. Scarpino has nearly 100 publications in academic journals and books. His expert commentaries on science and technology have appeared in publications such as: Nature, Science, PNAS, and Nature Physics. His research has been covered by the New York Times, Wired, the Boston Globe, NPR, VICE News, National Geographic, and numerous other venues. For his contributions to complex systems science, he was made a Fellow of the ISI Foundation in 2017, an External Faculty member of the Santa Fe Institute in 2020, and an External Faculty member of the Vermont Complex Systems Center in 2021.
Virginia’s COVID-19 Health Equity (Applied) Research Agenda // Justin Crow, VDH
Abstract: Several factors converged in 2020 to bring health equity into the spotlight in Virginia. In September 2019, Governor Ralph S. Northam appointed Dr. Janice Underwood to serve the Commonwealth, as the nation’s first cabinet-level Chief Diversity Officer. Disparities in infections, outcomes, and access to resources such as tests and personal protective equipment emerged early in the COVID-19 pandemic. Similarly, economic fallout from the pandemic and the response hit minority and low-income communities hardest. Over the summer, Richmond and its monuments to the Confederacy became a flashpoint in nationwide protests in response to the murder of George Floyd, highlighting the systemic and historical inequities driving these disparities. Finally, in February of 2021, the Virginia General Assembly passed a resolution declaring racism a public health crisis. The Office of Health Equity, a relatively small office in the Virginia Department of Health focused on data and applied research, played a key role in responding to these crises. Additionally, health equity became a central focus of Federal funding, providing additional resources to meet the challenges of COVID-19 and other emerging public health threats. In this presentation I describe how the Division of Social Epidemiology took an applied research approach during the pandemic, developed a COVID-19 Health Equity Research Agenda to understand the equity impacts of the pandemic and its response, and is building an equity-focused research infrastructure to address current and future public health crises.
Bio: Justin Crow is the Director of the Division of Social Epidemiology in the Office of Health Equity of the Virginia Department of Health. Prior to joining the Office of Health Equity, Justin served as the Deputy Director for the Virginia Healthcare Workforce Data Center and the Deputy Executive Director for the Board of Health Professions, both with the Virginia Department of Health Professions. Justin earned his Master in Public Administration from Virginia Commonwealth University and his baccalaureate degree from the University of Mary Washington.
Policing COVID-19 in Queensland, Australia // Katie Hail-Jares, Griffith
Abstract: In March 2020, shortly after the first confirmed COVID-19 case, the state of Queensland-- and many other jurisdictions around Australia-- expanded police powers in an effort to reduce the spread of the virus. This expansion of police powers was unprecedented, including powers to both stop and fine individuals who violated the public health directives (e.g. staying inside; wearing a mask; etc.) as well as created interstate border forces. Throughout the year, the Queensland Police Service regularly put out press releases about their COVID activities. A content analysis of 580 press releases published online between March 2020-August 2020 revealed that 45 mentioned COVID-19. The largest proportion reported on infringements. Looking more closely at these infringements notices, I suggest that public health citations were used to target people who were deemed socially undesirable, such as sex workers, people who were houseless, and gang members. The role of racism and xenophobia in issuing these infringements is also discussed.
Bio: Katie Hail-Jares (@khailjares) is a lecturer in the School of Criminology and Criminal Justice at Griffith University. She is an epidemiological criminologist who is interested in how criminalising behaviour can impact the health of people and their communities. She is the lead editor of Challenging Perspectives on Street-Based Sex Work (Temple University Press), where her chapter discussed the intersection of gentrification, policing, and trans sex work in Washington, DC.
Day 2, June 25, 11am EDT: Misinformation
Socially Influence Campaigns: The Coordination of Events Using Bots and Misinformation // Kathleen Carley, CMU
Abstract: As the pandemic swept through the physical world a disinfodemic swept through the digital world. Misinformation was used to malign individuals, create havoc in civil society, repress minority groups, attack foreign powers, create confusion and so forth. The role of bots, hate-speech and misinformation and how they are used together in influence campaigns is described. This is illustrated using information from the pandemic, various elections, and revolutions with particular emphasis on the re-open campaigns and conspiracy theories. Recent advances in social cybersecurity are described and limitations of current theories and technologies are identified.
Bio: Dr. Carley is a Professor of Computer Science in the Institute for Software Research, IEEE Fellow, and Director of the Center for Computational Analysis of Social and Organizational Systems (CASOS) and Director of the center for Informed DEmocracy And Social‐cybersecurity (IDeaS) of the Center for at Carnegie Mellon University. She joined Carnegie Mellon in 1984 as Assistant Professor Sociology and Information Systems. In 1990 she became Associate Professor of Sociology and Organizations, in 1998 Professor of Sociology, Organizations, and Information Technology, and in 2002, attained her current role as Professor of Computation, Organization, and Society. She is also the CEO of Carley Technologies Inc. aka Netanomics. Dr. Carley’s research combines cognitive science, sociology, and computer science to address complex social and organizational issues. Her most notable research contribution was the establishment of Dynamic Network Analysis (DNA) – and the associated theory and methodology for examining large high‐dimensional time variant networks. Her research on DNA has resulted in tools for analyzing large‐scale dynamic networks and various multi‐agent simulation systems. She has led the development of tools for extracting sentiment, social and semantic networks, and cues from textual data (AutoMap & NetMapper), simulating epidemiological models (BioWar), and simulating changes in beliefs and practice given information campaigns (Construct). Her ORA system is one of the premier network analysis and visualization technologies supporting reasoning about geo‐spatial and dynamic high‐dimensional network data. It includes special features for handling small and big data, social media data, and network dynamics. It is used worldwide. Illustrative projects include assessment of fake news and social cyber‐security threats, IRS outreach, impact of NextGen on airline re‐rerouting, counterterrorism modeling, counter‐narcotics modeling, health analytics, social media analytics of elections, and social media based assessment of crises such as Benghazi, Darfur, the Arab Spring, COVID‐19.
Slides available here.
Do they really believe this? Motivations for spreading misinformation and reactions to interventions // Jennifer Golbeck, UMD
Abstract: Why do people share misinformation online and what can we do about it are questions driving many research efforts as we emerge from a year that has been astonishing in both the volume and negative impact of the bad information that has been shared. This talk will integrate a slew of new studies that seek to understand the people and motivations that drive the spread of misinformation and the efficacy of various interventions. Many of these results are consistent and go against conventional wisdom. This talk will bring up new questions raised by this work and suggest policy that may be effective to stop harmful misinformation spread.
Bio: Jennifer Golbeck is a Professor in the College of Information Studies at the University of Maryland, College Park. Her research focuses on artificial intelligence and social media, privacy, malicious online behavior, and trust on the web. She received an AB in Economics and an SB and SM in Computer Science at the University of Chicago, and a Ph.D. in Computer Science from the University of Maryland, College Park.
Slides available here.
Lessons from the COVID States Project // David Lazer, Northeastern
Abstract: I will discuss lessons drawn from the COVID States Project with respect to our understanding of the mechanism of dissemination of misinformation on social media, the impact of misinformation on behaviors/attitudes. The essential takeaways: we have a better understanding of online sharing behavior than exposure; well established methods to examine misbeliefs; but a relatively poor understanding of the relationship between exposure with beliefs and behaviors (like, the decision whether to get vaccinated). A key conclusion is that we need a large-scale, multi-platform approach to understanding the relationship between online behaviors (including and especially exposure) and offline attitudes and behaviors.
Bio: David Lazer is University Distinguished Professor of Political Science and Computer Sciences, Northeastern University, elected Fellow of the National Academy of Public Administration, and visiting scholar at the Institute for Quantitative Social Science at Harvard. His scholarship focuses on computational social science and social networks, with a particular focus on misinformation and political communication. He is co-lead of the COVID states project, which has charted public opinion in all 50 states through the pandemic.
Day 2, June 25, 2pm EDT: Behavioral Modeling
Cognitive Modeling of the Behavioral Effectiveness of Non-Pharmaceutical Interventions // Christian Lebiere, CMU
Abstract: Until the advent of vaccines and effective therapies, non-pharmaceutical interventions (NPIs) such as social distancing and mask wearing are the primary means to control the spread of epidemics. However, NPI mandates are difficult to enforce and largely depend upon the cooperation of the public. Models often make rough assumptions about what percentage of the population behave accordingly in order to estimate the effectiveness of NPIs. However, evidence suggests that behavioral changes vary according to context, are susceptible to personal experiences as well as messaging in mass and social media, and often precede the introduction of mandates. We develop cognitive models of behavioral response to NPIs to better estimate their impact on the course of epidemics. Our cognitive models reflect the cumulative effect of social media messaging, continuously adapt behavior to the rise and fall of the infection counts, and display divergent behavior resulting from distinct attitude clusters. Future work on extending our models to related issues such as vaccine hesitancy and integrating our cognitive models with epidemiological models is discussed.
Bio: Christian Lebiere is a Research Faculty in the Psychology Department at Carnegie Mellon University, having received his Ph.D. from the CMU School of Computer Science. During his graduate career, he studied connectionist models and was the co-developer of the Cascade-Correlation neural network learning algorithm. Since 1991, he has worked on the development of the ACT-R cognitive architecture and its applications to artificial intelligence, human-computer interaction, decision-making, intelligent agents, cognitive robotics, network science, and human-machine teaming. He has recently been involved in defining the Common Model of Cognition, an effort to consolidate and formalize the scientific progress resulting from the 40-year research program in cognitive architectures.
What have we learned about COVID-19 conspiracies as barriers to controlling the pandemic in the US? // Dan Romer, Penn
Abstract: Conspiracies about the pandemic proliferated early in the pandemic and were associated with reduced willingness to engage in social distancing, mask wearing, and intentions to vaccinate. I review research conducted by the Annenberg Public Policy Center using a three-wave panel of 883 respondents from March to November 2020 that assessed conspiratorial thinking and specific COVID conspiratorial beliefs, perceived threats of the disease, fears of vaccination, and reliance on different media information sources as predictors of preventive action and trust in national health authorities, such as the CDC. Findings show that belief in conspiracies has undermined preventive action by different segments of the population, but that conservative media and its users were particularly prone to acceptance of conspiracies, producing a type of “echo chamber” that insulated its users from more mainstream media that supported preventive action. The findings show that wide-ranging exposure to mainstream US media is insufficient to counter the role of politically conservative echo chambers that undermined the consensus needed to confront a major public health emergency such as the COVID-19 pandemic.
Bio: Dan Romer is a psychologist and the research director of the Annenberg Public Policy Center of the University of Pennsylvania, where he conducts policy relevant research on social and individual influences on young people’s health and development. His research focuses on the role of the media, peers, and parents in combination with developmental differences as influences on public health outcomes, such as substance use, unintentional and intentional injury, and mental well-being. With the advent of the pandemic, he has focused on social and personality factors that impede effective decision making for preventing the spread of the coronavirus.
The Impact of informational and persuasive communications on behavior relevant to pandemics // Dolores Albarracin, UIUC
Abstract: In this talk, I describe various considerations about how to model the impact of communications and behavioral interventions on epidemics. During the first part of my talk, I review a spectrum that goes from informational communications to the impact of policy and discuss the likely effect of these programs across the board. I then take a more nuanced approach and distinguish formation from change of behavioral patterns, discuss the need to model delayed effects of communications, and review the likely impact of communications and interventions in interaction with recipients’ behavioral responses to those programs. I conclude by summarizing an agenda of questions to investigate to better model pandemics.
Bio: Dr. Dolores Albarracín, Ph.D. is currently a Professor of Psychology and Business Administration at the University of Illinois at Urbana-Champaign, Beginning July 1, 2021, she will be the Alexandra Heyman Nash University professor at the University of Pennsylvania. Trained in social and clinical psychology, she directs the Social Action Lab and the Health Social Media and Technology Group, where she studies social cognition and action, communication, misinformation, as well as attitudinal and behavioral change. She applies her theoretical contributions to the domains of HIV, substance use, vaccines, and COVID-19. Dr. Albarracín received her Ph.D. in Psychology from the University of Illinois at Urbana Champaign, and was previously a tenured professor at the University of Florida and at the University of Pennsylvania. Her publications include 6 books and about 180 journal articles and book chapters. She has been editor-in-chief of Psychological Bulletin (2014-2020) and received awards for Outstanding Mid-Career Contributions to the Psychology of Attitudes and Social Influence from the Attitudes Interest Group in the Society of Personality and Social Psychology and Outstanding Contributions to Social psychology from the Society of Personality and Social Psychology, as well as an Avant Garde Award from the National Interest of Drug Abuse. Her research is funded by the National Institutes of Health and the National Science Foundation.
Networks all around: Social contact patterns and what they can tell us about COVID-19 control and interventions // Dina Mistry, Twitter
Bio: Dina Mistry is a networks and data scientist at Twitter and formerly an infectious disease modeler at the Institute for Disease Modeling, a division of The Bill & Melinda Gates Foundation. Her work has focused on the questions of who we interact with in the physical world, data driven methods of modeling those diverse contact networks to integrate in infectious disease models, and how social mixing patterns and disease awareness change our understanding of disease spreading dynamics. She received a B.Sc. in Physics & Astronomy from the University of Toronto, and an M.Sc. and Ph.D. in Physics from Northeastern University.