Workshop will be held on August 15, 2021, 8am - 5pm PST (11 am - 8 pm EST). All times below are in PST.
| Time | Event |
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| 8:00am-8:10am | epiDAMIK opening Remarks |
| 8:10am-9:00am | Invited Keynote 1. Jeffrey Shaman
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 Jeffrey Shaman is a Professor in the Department of Environmental Health Sciences and Director of the Climate and Health Program at the Columbia University Mailman School of Public Health and Faculty Chair of the Earth Institute at Columbia University. He studies the survival, transmission and ecology of infectious agents, including the effects of meteorological and hydrological conditions on these processes. Work-to-date has primarily focused on mosquito-borne and respiratory pathogens. He uses mathematical and statistical models to describe, understand, and forecast the transmission dynamics of these disease systems, and to investigate the broader effects of climate and weather on human health. He holds a BA in biology from the University of Pennsylvania and an MA, MPhil, and PhD in climate and geophysics from Columbia. Title: Data-driven Inference and Forecasting for Infectious Diseases [expand]
Abstract: Dynamic models of infectious disease systems are often used to study the epidemiological characteristics of disease outbreaks, the ecological mechanisms and environmental conditions affecting transmission, and the suitability of various mitigation and intervention strategies. In recent years these same models have been combined with Bayesian inference methods and used both to estimate critical properties of an infectious disease and to generate probabilistic forecasts of future incidence at the population scale. Here I present research from my own group describing application of such combined model-inference frameworks to the study of influenza, SARS-CoV-2, and other infectious diseases.
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| 9:00am-9:40am | Lightning Talks
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3 min per each paper in the following order:
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| 9:40am-10:00am | Contributed Talk 1 (Long) Paper: Simple Epidemic Models with Segmentation Can Be Better than Complex Ones [PDF] Authors: Geon Lee, Se-eun Yoon and Kijung Shin. |
| 10:00am-10:30am | Stretch, Networking Break |
| 10:30am-11:20am | Invited Keynote 2. Kristina Lerman
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 Kristina Lerman is a Principal Scientist at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Professor in the USC Computer Science Department. Trained as a physicist, she now applies network analysis and machine learning to problems in computational social science, including crowdsourcing, social network and social media analysis. Her recent work on modeling and understanding cognitive biases in social networks has been covered by the Washington Post, Wall Street Journal, and MIT Tech Review. Title: Understanding the Pandemic: from Covid-19 Hot Spots to Mistrust of Science [expand]
Abstract: Taming the Covid-19 pandemic required behavioral changes such as mask wearing and vaccination. However, divergent responses to the pandemic showed that political polarization and mistrust of science reduce public’s willingness to adopt the necessary mitigation measures. To explore this phenomenon, we use a large dataset of tweets to quantify the partisanship of social media users and their propensity for sharing anti-science information. We find that these dimensions are correlated, with conservatives more likely to share anti-science sources. Using geo-located tweets, we study how mistrust of science relates to socioeconomic characteristics of places from which people tweet. Our analysis reveals three types of places with distinct behaviors: large metropolitan counties, smaller metropolitan and suburban counties, and rural counties. We demonstrate the feasibility of using geospatially-resolved social media data to monitor public attitudes on issues of social importance. In the second part of the talk, I explore why COVID-19's impact is highly unequal: many regions had nearly zero infections, while others became hot spots. This effect can be attributed to a Reed-Hughes-like mechanism in which the disease arrives to regions at different times and grows at different rates. Faster growing regions create hot spots that dominate spatially aggregated statistics. This leads to aggregation-bias, which skews estimated growth rates at larger spatial scales. Epidemic modeling and public health policy makers need to account for potential distortions introduced by spatial aggregation.
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| 11:20am-11:40am | Contributed Talk 2 (Long) Paper: Diffusion Source Identification on Networks with Statistical Confidence [PDF] Authors: Tianxi Li, Haifeng Xu and Quinlan Dawkins. |
| 11:40am-11:50am | Contributed Talk 3 (Short) Paper: Predicting the Impact of Covid-19 with Modified Epidemiological Model Using Deep Learning [PDF] Authors: Yixian Chen, Jialu You, Lei Ji and Prakhar Mehrotra. |
| 11:50am-12:00pm | Contributed Talk 4 (Short) Paper: An Integrated Epidemic Simulation Workflow for Submodular Intervention Strategies [PDF] Authors: Reet Barik, Marco Minutoli, Mahantesh Halappanavar and Ananth Kalyanaraman. |
| 12:00pm-1:00pm | Lunch Break |
| 1:00pm-1:50pm | Invited Keynote 3. Matt Biggerstaff
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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. Title: Improving Pandemic Response: Employing Mathematical Modeling to Confront COVID-19 [expand]
Abstract: Modeling has been integral to the COVID-19 response. This talk will review how CDC utilized modeling to complement surveillance data to inform COVID-19 public health decision making and policy development, including the use of modeling and forecasting to improve situational awareness and to inform the evidence base for mitigation strategies.
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| 1:50pm-2:10pm | Contributed Talk 5 (Long) Paper: Neural Relational Autoregression for High-Resolution COVID-19 Forecasting [PDF] Authors: Matthew Le, Mark Ibrahim, Levent Sagun, Timothee Lacroix and Maximilian Nickel. |
| 2:10pm-2:20pm | Contributed Talk 6 (Short) Paper: Agent-based Modeling to Evaluate Nosocomial COVID-19 Infections and Related Policies [PDF] Authors: Yoonyoung Park, Issa Sylla, Amar Das and James Codella. |
| 2:20pm-2:30pm | Contributed Talk 7 (Short) Paper: Spreading Power of Key Nodes in Online Social Networks with Community Structure [PDF] Authors: Stephany Rajeh, Marinette Savonnet, Eric Leclercq and Hocine Cherifi. |
| 2:30pm-3pm | Stretch, Networking Break |
| 3pm-3:50 pm | Invited Keynote 4. Ajay Deshpande
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Ajay Deshpande is a Senior Manager and a Principal Data Scientist in Data Science Innovation team in IBM Watson Health since late 2019. Prior to that, he spent 9 years in IBM Research in various roles. He developed many AI/ML-driven novel innovation solutions in various areas including Smarter Cities, mortgage servicing, Smarter Commerce, supply chain and most recently healthcare. He is a Master Inventor and has received Corporate Technical Award and a couple of Outstanding Technical Achievement Awards. Prior to joining IBM, he obtained his PhD and MS from MIT. Title: AI-augmented framework for epidemiological modeling of COVID-19 [expand]
Abstract: As the COVID-19 pandemic is unraveling globally with devastating impacts, we at IBM Watson Health encountered a number of questions from our clients. Employers want to know when and if it's safe to reopen workplaces from the perspective of workplace transmission and community transmission risks. A large hospital system wants to know projections of hospital and ICU beds in coming weeks to plan its resources. A vaccine maker wanted to know in which countries to conduct vaccine trials in a certain timeframe. In this talk, I present the AI-augmented epidemiological modeling framework that we built at IBM to generate county and state level projections periodically and discuss its performance. We built a series of models as new data became available leveraging cases, deaths, mobility, hospitalization and vaccination data. I discuss how we used these models to address our clients' questions.
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| 3:50pm-4:00pm | Contributed Talk 8 (Short) Paper: Model Calibration in Network Models of HIV [PDF] Authors: Sally Slipher and Nicole Carnegie. |
| 4:10pm-4:20pm | Contributed Talk 9 (Short) Paper: Analysis of COVID-19 Misinformation: Origin and Cure Narratives [PDF] Authors: Anika Halappanavar. |
| 4:20pm-4:30pm | Contributed Talk 10 (Short) Paper: COVID-19 or Flu? Discriminative Knowledge Discovery of COVID-19 Symptoms from Google Trends Data [PDF] Authors: Md Imrul Kaish, Md Jakir Hossain, Evangelos Papalexakis and Jia Chen. |
| 4:30pm-4:40pm | Contributed Talk 11 (Short) Paper: Modelling Major Disease Outbreaks in the 21st Century: A Causal Approach [PDF] Authors: Aboli Marathe, Saloni Parekh and Harsh Sakhrani. |
| 4:40pm-4:50pm | Closing Remaks |