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PREPARE has planned three asynchronous 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 TBD // SBEG: Social, Behavioral, Economic, and Governance Issues during a Pandemic
June TBD // HPC: Role of Computing Technology in Anticipating, Controlling, Responding to, and Assessing Pandemics
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These workshops are not meant to be the final say - keep the discussion alive on our YouTube channel.
DATA: Access, Creation, and Maintenance of Data and Computing Resources
May 12 & 13, 2021 Async Virtual Workshop
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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.
11am EDT: Mobility, Search, and Social Network Data
11:00-11:05 am Opening remarks - James Joshi, NSF & Madhav Marathe, UVA
11:05-11:25am Evgeniy Gabrilovich, Google Health // Bio
11:45-12:05pm Alex Dow, Facebook // Bio
2pm EDT: Contact Tracing, Syndromic Surveillance, and Social Media
2:00-2:20pm Marcel Salathé, EPFL // Bio
2:20-2:40pm John Brownstein, Harvard University and Boston Children’s Hospital // Bio
2:40-3:00pm Jure Leskovec, Stanford University // Bio
11am EDT: Clinical and Epidemiological Data
11:00-11:20am Matthew Biggerstaff, CDC // Bio
11:20-11:40am Nathaniel Hupert, Weill Cornell Medicine // Bio
2pm EDT: Data Curation and Data Sharing
2:00-2:20pm Lucila Ohno-Machado, University of California San Diego // Bio
2:20-2:40pm Salil Vadhan, Harvard University // Bio
3:15pm-3:20pm Closing remarks, Program Committee
Day 1, 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, 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, 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, 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.
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