PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 3, Population Level Theme

Grenfell, Bryan; Yu, Bin; Peccia, Jordan

Title:

PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 3, Population Level Theme

dc.contributor.author	Grenfell, Bryan	en_US
dc.contributor.author	Yu, Bin	en_US
dc.contributor.author	Peccia, Jordan	en_US
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Data Engineering and Science	en_US
dc.contributor.corporatename	Princeton University	en_US
dc.contributor.corporatename	University of California, Berkeley	en_US
dc.contributor.corporatename	Yale University	en_US
dc.date.accessioned	2021-03-23T04:08:38Z
dc.date.available	2021-03-23T04:08:38Z
dc.date.issued	2021-02-23
dc.description	Presented online February 23, 2021, 10:30 a.m.-1:10 p.m.	en_US
dc.description	National Symposium on Predicting Emergence of Virulent Entities by Novel Technologies (PREVENT) : What Advances In Science, Technology, And Human Behavior Will Enable Prediction And Prevention Of Future Pandemics?	en_US
dc.description	Chairs: B. Aditya Prakash and Paul Torrens	en_US
dc.description	Bryan Grenfell is a population biologist, distinguished for his investigation into the spatiotemporal dynamics of pathogens and other populations. Bryan studies processes that occur in populations at different scales and how infections move through such groups of organisms. His work is crucial in helping to control disease in humans and animals. His research is theoretical as well as based on large datasets, demonstrating how the density of a population and randomness interact to change the size and composition of populations. Alongside colleagues from the National University of Singapore, he studied measles in developed countries and is now extending his investigations to whooping cough and other infectious diseases. Bryan is currently Professor of Ecology and Evolutionary Biology and Public Affairs at Princeton University in New Jersey. He was awarded the T. H. Huxley Medal from Imperial College London in 1991, and the Scientific Medal of the Zoological Society of London in 1995.	en_US
dc.description	Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley and a former chair of Statistics at UC Berkeley. Yu's research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine. In order to augment empirical evidence for decision-making, they are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contextual decomposition (ACD) for interpretation of Deep Neural Networks (DNNs). Yu is a member of the U.S. National Academy of Sciences and a fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She received the E. L. Scott Award from COPSS (Committee of Presidents of Statistical Societies) in 2018. Moreover, Yu was a founding co-director of the Microsoft Research Asia (MSR) Lab at Peking University and is a member of the scientific advisory board at the UK Alan Turing Institute for data science and AI.	en_US
dc.description	Jordan Peccia is the Thomas E. Golden Jr. Professor of environmental engineering at Yale University. His research mixes genetics with engineering to study childhood exposure to bacteria, fungi and viruses in buildings. Peccia is a member of Connecticut Academy of Science and Engineering and associate editor for the journal Indoor Air. He earned his PhD in environmental engineering from the University of Colorado.	en_US
dc.description	Runtime: 60:02 minutes	en_US
dc.description.abstract	Bryan Grenfell - Plenary Talk TITLE: "What Cross-Scale Research Can Tell Us About Predicting, Understanding And Mitigating Future Pandemics?" We briefly review the epidemic and evolutionary dynamics of directly-transmitted infections and their transition from pandemics to endemicity. We discuss how cross-scale dynamics, from protein to pandemic, determine key issues in understanding, predicting and mitigating outbreaks, then build on this to discuss future cross-scale research and public health priorities.	en_US
dc.description.abstract	Bin Yu - Presentation TITLE: "Curating a COVID-19 Data Repository and Forecasting County-Level Death Counts in the United States". As the COVID-19 outbreak continues to evolve, accurate forecasting continues to play an extremely important role in informing policy decisions. In this talk, I will describe a large data repository containing COVID-19 information curated from a range of different sources. This data is then used to develop several predictors and prediction intervals for forecasting the short-term (e.g., over the next week) trajectory of COVID-19-related recorded deaths at the county-level in the United States.	en_US
dc.description.abstract	Jordan Peccia - Presentation TITLE: "Tracking Epidemics at the Population Level Through Wastewater-Based Epidemiology". Throughout the world, wastewater is continually collected from human populations and conveyed to central locations for treatment and/or discharge. The chemical and biological features of wastewater contain insight into the disease state and behavior of a community. This talk reports on the Yale COVID-19 wastewater project, where daily samples were collected from eight different wastewater treatment facilities representing 20 Connecticut towns and cities and covering a population of more than one million. Tracking SARS-CoV-2 concentrations in these treatment facilities during the COVID-19 pandemic and linking these concentrations to public health data demonstrate how wastewater-based epidemiology can be a rapid, cost effective, and accurate measure of disease dynamics within a community.	en_US
dc.description.sponsorship	National Science Foundation (U.S.)	en_US
dc.format.extent	60:02 minutes
dc.identifier.uri	http://hdl.handle.net/1853/64400
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	PREVENT Symposium
dc.subject	COVID-19	en_US
dc.subject	Cross-scale research	en_US
dc.subject	Data	en_US
dc.subject	Epidemic	en_US
dc.subject	Forecasting	en_US
dc.subject	Pandemic	en_US
dc.subject	Public health	en_US
dc.subject	SARS-CoV-2	en_US
dc.subject	Wastewater	en_US
dc.subject	Wastewater-based epidemiology	en_US
dc.title	PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 3, Population Level Theme	en_US
dc.title.alternative	PREVENT Symposium - Session 3, Population Level Theme	en_US
dc.type	Moving Image
dc.type.genre	Presentation
dspace.entity.type	Publication
local.contributor.corporatename	Institute for Data Engineering and Science
local.relation.ispartofseries	IDEaS Conferences
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