The COVID-19 Pandemic International Symposium (Online)

THEME COVID-19 Pandemic Forecasting Accelerator: Trailblazing New Frontier of Prediction Science and AI

DATE 8:30 AM - 1:00 PM on Wednesday, March 31, 2021 (Korea) 7:30 PM - 12:00 AM (EDT) | 4:30 PM - 9:00 PM (PDT) | 11:30 PM - 4:00 AM (WET) on Tuesday, March 30, 2021

Advanced Registration (Required): The 46th KAST International Symposium (google.com) Virtual Participation (YouTube): The 46th KAST International Symposium (YouTube.com)

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Biographies and Abstracts Session 1

Presenter/Author Title Sung-il Cho strategy in Korea in the year of expanding COVID-19 (Professor, Graduate School of Public vaccination Health, Seoul National University) Eunok Jung Prediction of Transmission Dynamics and Vaccination Priority (Professor, Department of Strategy for the COVID-19 Pandemic in Korea using Mathematical Mathematics, Konkuk University) Modeling David Fisman What you see is what you test: the impact of test volume on (Professor, Dalla Lana School of perceived risk during the SARS-CoV-2 pandemic , ) Christopher Murray (Professor, Health Metrics Sciences Long-range COVID-19 Scenarios at the University of Washington) Discussant Gabriel Leung (Dean, Medicine, University of ) Simon Johnson (Professor, MIT, Sloan School of Management)

Session 2

Presenter/Author Title Asaph Young Chun Pandemic Forecasting and Nudging Science-Based Policymaking: (Director-General, Statistics Research Genesis and Progress in Prediction Science Institute of Statistics Korea) Taesung Park Which implemented government policies and national indicators are (Professor, Department of Statistics, most influential in the spread of COVID-19? Seoul National University) Marc Lipsitch Accounting for uncertainties in prediction when making vaccine (Professor, , Harvard policy School of Public Health) Laura Rosella Applications of machine learning approaching on large-scale routinely (Professor, Dalla Lana School of collected population health and mobility data to inform COVID-19 Public Health, University of Toronto) management Discussant (President, CIFAR, Canada) Jun Wook Kwon (Director, the National Institute of Health)

Sung-Il Cho Professor of Epidemiology Seoul National University Graduate School of Public Health [email protected]

Prof. Cho is currently Professor at Seoul National University, Seoul, Korea. His research field of interest is epidemiology including infectious disease surveillance and modelling. He started his professional career in 2001 as an Instructor in Graduate School of Public Health at Seoul National University, where he has continued to serve as faculty. He is a member of the Advisory Committee on Epidemic Emergency Response for the Korea Center for Disease Control and Prevention Agency. He served as the Director of the Infectious Disease Control Supporting Center at Seoul Metropolitan Government during 2017-2019. Prof. Cho received the degree from the College of Medicine, Seoul National University, Korea in 1986. He then received the M.P.H. degree from the Graduate School of Public Health at Seoul National University in 1990 and Sc.D degree from the Department of Epidemiology at Harvard School of Public Health in 1999.

Social distancing strategy in Korea in the year of expanding COVID-19 vaccination

Kyung-Duk Min1, Sangwoo Tak1, Sung-il Cho1,2 1Institute of Health and Environment, Graduate School of Public Health, Seoul National University. 08826, Seoul, Republic of Korea; 2Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, 08826, Seoul, Republic of Korea

Global impacts from Coronavirus disease 2019 (COVID-19) have been tremendous, in terms of both public health and economy. As of March 22, 2021, about 123 million cases have been reported worldwide, including 2.7 million deaths.1 The global loss of gross domestic product was estimated as 2% due to the pandemics. 2 To cope with the public health crisis, social distancing intervention has been implemented. Especially, the social distancing in South Korea seems to be successful along with other non-pharmaceutical interventions (NPIs), such as contact tracing combined with large scale testing, 3 given that the incidence rate was relatively lower than other OECD countries. However, the number of confirmed cases has surged since November 2020 in South Korea, called “the third wave”. Medical systems in South Korea did not meet the need for surge capacity temporarily, causing deaths from inaccessibility of proper medical treatment. Even in the end of March, the third wave has not been fully controlled. Consequently, the role of nationwide COVID-19 vaccination is becoming increasingly critical. Fortunately, the vaccine development for COVID-19 was unprecedentedly rapid and some countries, such as Israel, already vaccinated large population.4 In South Korea, vaccinations started from the end of February 2021, and Health authorities developed a strategy, expecting that 12 million people will receive the first dose of the vaccines until the end of June.5 Considering the obvious effectiveness of vaccination shown by other countries, it seems clear that vaccination will significantly reduce the number of confirmed cases. However, social distancing should be maintained until herd immunity is reached. In this study, we examined the effectiveness of social distancing strategy along with the current vaccination plan in South Korea. A mathematical model with vaccinated–susceptible– latent–infectious–recovered compartments stratified into metropolitan and non-metropolitan regions (a meta-population model), was used to this end. In our model, parameters such as latent period, infectious period, vaccination rate and effectiveness of vaccine were obtained from published literatures and reports. Assuming effective contact rate is time-varying, we estimated the parameter by calibrating the model outputs with reported daily confirmed cases. Based on the model, we developed several scenarios to predict COVID-19 epidemics in South Korea 2021; 1) predicted COVID-19 epidemics with current vaccination plan without social distancing, 2) predicted COVID-19 epidemics with current vaccination plan and social distancing, 3) predicted COVID-19 epidemics with current vaccination plan and social distancing but with lower compliance for vaccination. The effectiveness of integrated social distancing and vaccination was suggested by comparing the results.

References 1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020;20:533- 534. 2. Maliszewska M, Mattoo A, Van Der Mensbrugghe D. The Potential Impact of COVID-19 on GDP and Trade: a Preliminary Assessment. Washington, D.C.: The World Bank; 2020 3. Min KD, Kang H, Lee JY, Jeon S, Cho SI. Estimating the Effectiveness of Non-Pharmaceutical Interventions on COVID-19 Control in Korea. J Korean Med Sci 2020;35(35):e321. 4. Ritchie H, Ortiz-Ospina E, Beltekian D, Mathieu E, Hasell J, Macdonald B, et al. Coronavirus (COVID-19) Vaccinations; 2020 [cited 2021 Jan 31]. Available from: https://ourworldindata.org/covid-vaccinations. Korea Disease Control and Prevention Agency. The 2nd quarter implementation plan for COVID-19 vaccination; 2021 [cited 2021, March 15]. Available from: http://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015& list_no=712724&cg_code=&act=view&nPage=3. (Korean)

Eunok Jung Professor Konkuk University [email protected]

Prof. Jung is currently a Professor at Konkuk University, Seoul, Korea. Her research field of interest is mathematical modeling of infectious diseases, medical applications and optimization. She started her professional career as a researcher in Oak Ridge National Laboratory, USA (1999-2002) and moved to Konkuk University in 2002. She became board members of Korean Society for Industrial and Applied Mathematics (KSIAM), Korean Mathematical Society, and Korean Society for Mathematical Biology and served as 7th president of KSIAM (2017-2018). She received prestige government award, Science and Technology Promotion (President of Korea) in 2020. Recently, she is an advisory committee member of policy planning that is an organization directly under the President and Chairman of COVID-19 mathematical modeling test force team. She received the B.S. degree from the department of Mathematics Education at the Korea University, Korea in 1988. She then received the M.S. degree from the department of Mathematics at the Korea University in 1991 and Ph.D. degree from the department of Applied Mathematics at the New York University in 1999.

Prediction of Transmission Dynamics and Vaccination Priority Strategy for the COVID-19 Pandemic in Korea using Mathematical Modeling

Youngseok Go, Jongmin Lee, Eunok Jung Department of Mathematics, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea

Korea has been experiencing the third epidemic of COVID-19 over a year. In this talk, we present the prediction of COVID-19 epidemic and the effective vaccination strategy in Korea. First, we introduce a mathematical modeling of COVID-19 considering behavior changes based on the reported data since Feb. 16, 2020. Using mathematical modeling, the reproductive number according to the government social distancing changes and prediction of COVID-19 epidemic are discussed. Second, we construct a heterogenous population model considering the transmission matrix using maximum likelihood estimation (MLE) based on the epidemiological records of individual COVID-19 cases in Korea, and then investigate the vaccine priorities for minimizing mortality or incidence. Simulation results show that prioritizing healthcare workers (HCWs) and elderly groups is the best vaccination strategy for minimizing deaths if the reproductive number is below the threshold which may be changed depending on the scenarios. This result supports the current Korean government vaccination strategy since Feb 26, 2021. We learned through mathematical modeling that for effective vaccine strategy, it is important to carry out non-pharmaceutical interventions such as social distancing so that the outbreak does not increase.

David N. Fisman Professor Dalla Lana School of Public Health, University of Toronto [email protected]

Dr. Fisman is a -epidemiologist with research interests that fall at the intersection of applied epidemiology, mathematical modeling, and applied health economics. He is interested in developing and applying novel methodological tools that allow and public health experts to make the best possible decisions around communicable disease control, using the best available data. Dr. Fisman completed a residency in Internal Medicine at McGill and Brown Universities, before completing a fellowship at the Beth Israel Deaconess Medical Centre in Boston, and an MPH at Harvard School of Public Health. He was also an AHRQ fellow in Health Policy (1998-2001) at Harvard Centre for Risk Analysis. He is currently a Professor of Epidemiology at the University of Toronto and is the recipient of funding for the study of COVID-19 epidemiology in Canada from the Canadian Institutes for Health Research.

What you see is what you test: the impact of test volume on perceived risk during the SARS- CoV-2 pandemic

David N. Fisman Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

Canada is a high-income federal country, and success in control of the SARS-CoV-2 pandemic has varied widely across provinces and territories. The Province of Ontario is Canada’s most populous, and Ontario has also been more severely impacted by the pandemic than other provinces, with the largest absolute number of cases and deaths in Canada. Importantly, Ontario’s testing data are highly centralized, which provides a unique advantage for understanding the dynamics of SARS-CoV-2 infection in the province. In this brief review of current work, including work in progress, I will discuss how differential testing by age and sex has affected perceptions of risk in Ontario during the pandemic, and present a simple method for accounting for variable testing across groups. In related work, I will show how simple, accurate models for forecasting ICU admissions due to SARS-CoV-2 can be constructed by incorporating age and testing data, and show how the performance of these models has been affected by the emergence of SARS-CoV-2 variants of concern such as the B.1.17 lineage.

Christopher J.L. Murray Professor Health Metrics Sciences at the University of Washington [email protected]

Professor Murray’s career has focused on improving health for everyone worldwide by improving health evidence. A physician and health economist, his work has led to the development of a range of new methods and empirical studies to strengthen health measurement, analyze the performance of public health and medical care systems, and assess the cost-effectiveness of health technologies. IHME provides rigorous and comparable measurement of the world’s most important health problems and evaluates the strategies used to address them.

Before founding IHME, Murray served as Executive Director, Evidence and Information for Policy Cluster at the World Health Organization, Director, Harvard Initiative for Global Health and Harvard Center for Population and Development Studies, and Richard Saltonstall Professor of Public Policy at the Harvard School of Public Health. He is an elected member of the National Academy of Medicine (NAM) and 2018 co-recipient of the Canada Gairdner Global Health Award.

Long-range COVID-19 Scenarios

Christopher J.L. Murray Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States

The Institute for Health Metrics and Evaluation (IHME) developed one of the world’s leading models of the COVID-19 pandemic, forecasting and analyzing not just cases and deaths, but also the factors that influence those numbers, such as people’s movements (mobility), mask use, and seasonality. The model, first developed in response to a request for resource planning help from the University of Washington’s hospital system, was scaled to cover all countries and provide weekly updates since March 2020 on the potential future trajectories of the epidemic in each location. Over the past year the IHME Covid-19 Projections have become a vital source of information for policy experts and decision-makers at all levels of government including by the federal government and various state governments in the US, by the European Commission, the Pan American Health Organization, and the European Regional Office of the World Health Organization. IHME Covid-19 Projections: https://covid19.healthdata.org

Gabriel Leung Dean of Medicine The [email protected]

Gabriel Leung is the fortieth Dean of Medicine (2013-), inaugural Helen and Francis Zimmern Professor in Population Health and holds the Chair of Public Health Medicine at the University of Hong Kong (HKU). He was the last Head of Community Medicine (2012-3) at the University as well as Hong Kong's first Under Secretary for Food and Health (2008-11) and fifth Director of the Chief Executive's Office (2011-2) in government.

Leung is one of Asia's leading epidemiologists and global health exponents. His research defined the epidemiology of three novel viral epidemics, namely SARS in 2003, influenza A(H7N9) in 2013 and most recently COVID-19. He led Hong Kong government's efforts against pandemic A(H1N1) in 2009. He was founding co-director of HKU's World Health Organization (WHO) Collaborating Centre for Infectious Disease Epidemiology and Control (2014-8) and currently directs the Laboratory of Data Discovery for Health at the Hong Kong Science and Technology Park (2020-). In addition to serving on the Board of Governors at The Wellcome Trust, Leung regularly advises national and international agencies including the World Health Organisation, World Bank, Asian Development Bank, Boao Forum for Asia, Institut Pasteur, Japan Center for International Exchange and China Centers for Disease Control and Prevention. He is an Adjunct Professor of Peking Union Medical College Hospital and Adjunct Professorial Researcher of the China National Health Development Research Center. For COVID-19 specifically, he is an official advisor to both the Hong Kong and mainland Chinese governments, as well as providing expert input for numerous overseas jurisdictions.

He co-edited the Journal of Public Health (2007-14), was inaugural co-editor of Epidemics, associate editor of Health Policy and is founding deputy editor-in-chief of China CDC Weekly. He currently serves on the editorial boards of eight journals, including the British Medical Journal. After reading medicine at the University of Western Ontario, he completed residency training in Toronto. He earned a master's from Harvard University and research doctorate from HKU. He is an elected member of the US National Academy of Medicine and was awarded the (second highest civilian honour) by the Hong Kong government for distinguished service in protecting and promoting population health.

Simon Johnson Ronald A. Kurtz (1954) Professor of Entrepreneurship Massachusetts Institute of Technology, Sloan School of Management [email protected]

SIMON JOHNSON is the Ronald A. Kurtz (1954) Professor of Entrepreneurship at the MIT Sloan School of Management, where he is also head of the Global Economics and Management group. He co-founded and currently leads the popular Global Entrepreneurship Lab (GLAB) course – over the past 20 years. MBA students in GLAB have worked on more than 500 projects with start-up companies around the world. In February 2021, he joined the Board of Directors of Fannie Mae.

Johnson is the coauthor most recently of Jump-Starting America: How Breakthrough Science Can Revive Economic Growth and the American Dream (with Jon Gruber), as well as 13 Bankers and White House Burning (with James Kwak). His academic work on economic and financial development is widely cited.

Johnson has been a member of the private sector Systemic Risk Council since it was founded in 2012. From 2012 to 2019, he was a member of the FDIC’s Systemic Resolution Advisory Committee. From July 2014 to 2017, he was a member of the Financial Research Advisory Committee of the U.S. Treasury’s Office of Financial Research (OFR), within which he chaired the Global Vulnerabilities Working Group. From April 2009 to April 2015, he was a member of the Congressional Budget Office's Panel of Economic Advisers. In March 2016, Johnson was the third distinguished visiting fellow at the Central Bank of Barbados.

“For his articulate and outspoken support for public policies to end too-big-to-fail”, Johnson was named a Main Street Hero by the Independent Community Bankers of America (ICBA) in 2013. Johnson holds a BA in economics and politics from the , an MA in economics from the University of Manchester, and a PhD in economics from MIT.

Asaph Young Chun, PhD Director-General Statistics Research Institute | Statistics Korea [email protected]

Asaph Young Chun, PhD is the Director-General of Statistics Research Institute, the state-run think tank of data innovation and evidence-based decision making in Korea and beyond. He is Faculty Chair of the PSI International for Data Science, Survey Methodology, and Interdisciplinary Research. He advises a number of governments, including Korea, Canada, and the United States, to cope with the COVID-19 pandemic by leveraging science and data-based policymaking.

Prior to his service in the Korean government beginning in April, 2019, Young had served four Presidents of the United States since 1991 with his devotion to evidence-based policymaking in science, economy, labor, education and public health, as well as methodological innovation with data science, survey methodology and transdisciplinary nudging. He has led large-scale interdisciplinary research funded by U.S. federal agencies, such as the U.S. Department of Health and Human Services, Department of Labor, Department of Education, and Department of Commerce.

A journalist-turned-sociologist, Young joined interdisciplinary research career beginning in 1989 at the University of Michigan Institute for Social Research in concert with the UM School of Medicine. He served most recently as the Research Chief for Decennial Directorate at the U.S. Census Bureau. He also led a number of transdisciplinary research teams at the University of Chicago NORC. In 2013, he delivered an invited keynote speech on “science diplomacy” in the Royal Society of the . From 2013 to 2015, he served as Vice President of the Pyongyang University of Science and Technology (PUST) in North Korea, leading the PUST’s R & D programs and planning to open the PUST School of Medicine. He published over 110 papers that appear in Journal of Business and Economic Statistics, Public Opinion Quarterly, and Business Survey Methods by Wiley, among others. Young is the editor-in-chief of a Wiley book hot off the press, “Administrative Records for Survey Methodology,” a source of massive data innovation and data-based policymaking.

Young studied Communications for his A.B. and M.A. at the University of Michigan, Sociology for his PhD at the University of Maryland, and Public Policy as a senior executive fellow at the Harvard Kennedy School of Government. An enthusiastic fan of art by Albrecht Durer and Marc Chagall, Young turns to art and music as inspiration of innovation by his interdisciplinary research mentees.

Pandemic Forecasting and Nudging Science-Based Policymaking: Genesis and Progress in Prediction Science

Paul Choi, Eunjeong Jung, Asaph Young Chun Statistics Research Institute | Statistics Korea

Today, nearcasting the course of COVID-19 pandemic faces a Herculean task as challenging as forecasting the 1918 Spanish flu that claimed over 55 million lives across continents. The promise of vaccines that have been rapidly developed has been compromised by multiple variants of COVID- 19 that is caused by noble Coronavirus called SARS-CoV-2. In the case of Korea, science-and-data-based modeling has been a cornerstone of policymaking since COVID-19 infected the Korean population in February, 2019. The purpose of this paper is to unravel the coevolution of prediction science and policy throughout the course of COVID-19 pandemic in Korea. We had initially used the model of Incidence Decay with Exponential Adjustment (IDEA) based on a well-established model that classifies population into the three groups: Susceptibles, Infected and Recovered (SIR). The IDEA model outcomes have nudged Korea to develop and adjust measures of non-pharmaceutical interventions (NPI), such as masking, social distancing, and business and school closures. We will show the extent to which model outcomes have been aligned with transmission speed and scope in a number of crucial junctures, and point out, when necessary, the nature of gaps between prediction science and policy implementation. In the middle of August when Korea was inflicted by the 2nd wave of COVID-19, we turned to developing and integrating models of SIR-variants that are grounded on mathematics, statistics, and data science in a transdisciplinary manner. The data used in modeling included official organic data and commercial big data, both of which enhanced the rigor of scientific modeling. The outcomes of big data science-based modeling in Korea include as follows: nearcasting models quantifying the impact of NPI; predicting infection cases and mortality to harness the impact of COVID1-19; and applying modeling results to policy navigation. The paper points out conditions for further innovation in prediction science and issues for future research.

Taesung Park Professor Seoul National University [email protected]

Prof. Park received his B.S. and M.S. degrees in Statistics from Seoul National University (SNU), Korea in 1984 and 1986, respectively and received his Ph.D. degree in Biostatistics from the University of Michigan in 1990. From Aug. 1991 to Aug. 1992, he worked as a visiting scientist at the NIH, USA. From Sep. 2002 to Aug. 2003, he was a visiting professor at the University of Pittsburgh. From Sep. 2009 to Aug. 2010, he was a visiting professor in Department of Biostatistics at the University of Washington. He served as the chair of the bioinformatics Program, from Apr. 2005 to Mar. 2008 and the chair of department of statistics of SNU from Sep. 2007 and Aug. 2009. He is currently the Professor and Director of the Bioinformatics and Biostatistics Lab. at the Dept. of Statistics, and as the chair of the bioinformatics Program at SNU. His research areas include microarray data analysis, GWAS, gene-gene interaction analysis, and statistical genetics.

Which implemented government policies and national indicators are most influential in the spread of COVID-19?

Taesung Park Department of Statistics, Seoul National University, Seoul 08826, Korea

The outbreak of novel COVID-19 disease elicited a wide range of anti-contagion and economic policies like school closure, income support, contact tracing etc., in the mitigation and suppression of the spread of SARS-COV-2 virus. However, a systematic evaluation of these policies has not been made. Here, 17 implemented policies from the Oxford COVID-19 Government Response Tracker dataset employed in 90 countries from December 31, 2019 to August 31, 2020 are analyzed. We applied Poisson regression model to analyze the relationship between polices and daily confirmed cases using generalized estimating equations approach. Lagging (0, 3, 7, 10 and 14 days) in which the effects of policies implemented on a given day would affect the number of confirmed cases several days after implementation was also considered during the analysis. The countries were divided into three groups depending on the number of waves observed in each country. Through subgroup analysis, we showed that with and without lagging, contact tracing and containment policies are significant for countries with two waves, while closing, economic and health policies are significant for countries with three waves. Wave-specific analysis for each wave was also performed under the assumption that significant policies vary according to waves of the pandemic. Wave-specific analysis showed significant health, economic and containment polices vary across waves of the pandemic. Emergency investment in healthcare was consistently significant among the three groups while Stringency index among the waves. These findings may help in making informed decisions regarding whether, which, or when of these policies should be intensified or lifted.

Marc Lipsitch Professor of Epidemiology, Director, Center for Communicable Disease Dynamics Harvard T.H. Chan School of Public Health [email protected]

Marc Lipsitch, DPhil, leads the Center for Communicable Disease Dynamics, a leading center for research on the spread of diseases, interventions to control them, and the impacts of these policies on the population biology and evolution of pathogens. His research uses epidemiological, experimental, molecular, and phylogenetic approaches to study antimicrobial resistance, pandemic diseases, bacterial evolution, and evaluation of vaccines, among other topics. He serves on the editorial boards of eLife and Epidemiology and is an elected member of the US National Academy of Medicine and the American Academy of Microbiology.

Accounting for uncertainties in prediction when making vaccine policy

Marc Lipsitch1, Kate Bubar2, Kyle Reinholt2, Stephen M. Kissler1, Sarah Cobey3, Yonatan H. Grad1, Daniel Larremore2 1Harvard T.H. Chan School of Public Health, 2University of Colorado, Boulder;3 University of Chicago, USA Limited initial supply of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine raises the question of how to prioritize available doses. We used a mathematical model to compare five age-stratified prioritization strategies. A highly effective transmission-blocking vaccine prioritized to adults ages 20 to 49 years minimized cumulative incidence, but mortality and years of life lost were minimized in most scenarios when the vaccine was prioritized to adults greater than 60 years old. Use of individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID- 19 impact. Although maximum impact prioritization strategies were broadly consistent across countries, transmission rates, vaccination rollout speeds, and estimates of naturally acquired immunity, this framework can be used to compare impacts of prioritization strategies across contexts. This work, pubished in Science 26 Feb 2021:Vol. 371, Issue 6532, pp. 916-921 DOI: 10.1126/science.abe6959 , will be discussed in the context of this session’s theme of vaccines, policy, and prediction and the key question of how to make decisions with uncertainties in mind.

Laura C. Rosella Canada Research Chair in Population Health Analytics and Associate Professor Dalla Lana School of Public Health, University of Toronto [email protected]

Dr. Laura C. Rosella is the Principal Investigator and Scientific Director of the Population Health Analytics Lab. She is an Associate Professor in the Dalla Lana School of Public Health at the University of Toronto, where she holds Canada Research Chair in Population Health Analytics. In 2020, she was made the Inaugural Stephen Family Research Chair in Community Health at the Institute for Better Health, Trillium Health Partners. Her additional scientific appointments include the Vector Institute and Site Director for ICES UofT. Her research interests include population health, predictive models to support public health planning, and population health management. She specializes in using advanced analytic methods to leverage existing population data to support public health decisions. Most recently she has taken the role as Education lead at the newly formed U of T Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), focused on multidisciplinary, collaborative research in artificial intelligence across the medical and health sciences that translates into real-world settings. She has authored over 150 peer-reviewed publications in the areas of epidemiology, population health, health services research and predictive modelling. She has been awarded several national grants, including a CIHR Foundation grant to support her population health analytics research program. Notably, Dr. Rosella was previously awarded the Brian MacMahon Early Career Epidemiology Award by the Society for Epidemiologic Research and was named one of Canada’s Top 40 Under 40. She was president of the Canadian Society for Epidemiology and Biostatistics (CSEB) from 2018-2020. Related to pandemic modelling, she was an epidemiology lead during the H1N1 pandemic at Public Health Ontario, where she led several studies informing the pandemic response. She currently sits on Ontario’s provincial modelling and evidence tables informing coronavirus response and advises on scaling rapid-testing efforts in workplaces across Canada.

Applications of machine learning approaching on large-scale routinely collected population health and mobility data to inform COVID-19 management

Laura Rosella, in collaboration with (in alphabetical order) Ron Bodkin, Emmalin Buajitti, Avi Goldfarb, Jahir M. Gutierrez, Ethan Jackson, Tomi Poutanen, Andres Rojas, Graham Taylor, Maksims Volkovs, Tristan Watson Dalla Lana School of Public Health, University of Toronto, ON, Canada Layer 6 AI, Toronto, ON, Canada ICES, Toronto, ON, Canada Institute for Better Health, Trillium Health Partners Vector Institute, Toronto, ON Canada University of Guelph, Guelph, ON Canada

Many jurisdictions have access to routinely collected health and increasingly mobility data, which can contribute to population-level prediction models to support pandemic planning and management. These data are larger in scale and significantly more complex, making them more amenable to machine learning methods. This talk aims to present applications of machine-learning based prediction approaches that leverage population data for pandemic planning. The province of Ontario in Canada is one of several jurisdictions globally that has linked medical records on its entire population due to its single-payer health system and robust infrastructure. In addition, there is new availability of a range of non-health information, such as phone-level mobility data, which have the potential to inform emerging areas of risk at a local geographical area. This talk will cover two examples of comprehensive health records and mobility models for two use cases for population- level planning applications. The first use case is a Gradient Boosting model using the XGBoost to predict the risk of COVID-19 hospitalization from a rich source of linked routinely collected health and demographic data. The purpose of this model is population stratification by COVID-19 complication risk to support resource allocation and decision making. Our methodology utilizes general medical and demographic attributes commonly collected in health claims data in other countries, thus facilitating its repurposing in other jurisdictions. The second application involves using phone-level mobility data to predict COVID-19 case trends at a small geographic resolution. We used the data to quantify which mobility metrics were most predictive of COVID-19 cases in different geographies and at different stages during the pandemic, and we used the constructed metrics to predict local areas of emerging risk. The talk will cover both the approach and potential of these approaches to enhance traditional pandemic modeling efforts as well as raise important interpretive cautions and caveats in the context of pandemic planning.

Alan Bernstein President & CEO CIFAR [email protected]

Alan Bernstein has been President & CEO of CIFAR since 2012, responsible for developing and leading the institute’s overall strategic direction. He is one of Canada’s leading scientists and was an early champion of women in science and young scientists.

After receiving his PhD from the University of Toronto and following postdoctoral work at the Imperial Cancer Research Fund in London, Dr. Bernstein joined the Ontario Cancer Institute. In 1985, he joined the Samuel Lunenfeld Research Institute, was named its Associate Director in 1988 and served as Director of Research from 1994 to 2000. In 2000, he was asked to become the founding President of the Canadian Institutes of Health Research (CIHR), Canada’s federal agency for the support of health research. In that capacity, he led the transformation of health research in Canada. In 2010, Bernstein became Executive Director of the Global HIV Vaccine Enterprise in New York, an international alliance of researchers and funders charged with accelerating the search for an HIV vaccine.

Author of over 225 scientific publications, Alan made landmark contributions to the study of stem cells, blood cell formation (hematopoiesis), and cancer. He chairs or is a member of advisory and review boards in Canada, the U.S., U.K., Italy, and Australia. He serves as co-chair of the Scientific Advisory Committee for Stand Up 2 Cancer Canada, is a member of the Sabin-Aspen Vaccine Science and Policy Group, and the Scientific Advisory Committee of the Bill and Melinda Gates Foundation. In May 2020 he was appointed to Canada’s COVID-19 Vaccine Task Force and in February 2021, he was appointed Chair of the Variants of Concern, Scientific Advisory Council.

Alan’s contributions to science and science policy have been recognized with numerous awards and honorary degrees, including Officer of the , the Order of Ontario, the McLaughlin Medal from the , the Award of Excellence from the Genetics Society of Canada, the Gairdner Foundation Wightman Award, induction into the Canadian Medical Hall of Fame, and the 2017 International Prize in Health Research.

Jun-Wook Kwon Director National Institute of Health, NIH Korea Disease Control and Prevention Agency, KDCA [email protected]

Dr. Kwon received his B.S. degree from the College of Medicine, Yonsei University, Korea in 1989. He then received the M.S. degrees from the Graduate School in Public Health, Yonsei University, Korea in 1992 and from School of Public Health, University of Michigan, USA in 1995. He received his Ph.D. degree from School of Public Health, University of Michigan, USA in 1997. Dr. Kwon is currently a Director of the National Institute of Health, Korea Disease Control and Prevention Agency.