THIS IS AN ONLINE EVENT! Registrants will be emailed a Zoom link before the workshop. The event schedule is in Eastern time.

Identifying disease clusters and spatial patterns of disease (from a human/animal/plant) are important to inform policymakers, programs and interventions at both local and global scales. For instance, Canadian health authorities depend on alerts provided by front-line employees or by members of the public when there is an increase in disease or illness (disease cluster). Health authorities need to respond to cluster inquiries to inform the public that: a) no clustering exists, or b) to warn the public and investigate the cause of the cluster. The recent emergence of the coronavirus as a global pandemic is one example of a critical public health threat that challenged management systems. The rapid spread of coronavirus across much of the globe is not well understood yet. Space-time patterns of spread span multiple scales due to complex disease ecological processes and biases from surveillance data generated from multi-jurisdictions with varying sampling protocols are real challenges. These issues, which are also common to high priority diseases in Canada (e.g., coronavirus and Lyme diseases), can be difficult to accommodate in quantitative frameworks, and hamper the ability to use data and modeling products to accurately monitor disease and identify vulnerable populations either spatially or over space-time. We will spearhead innovation in disease modeling by addressing several practical problems related to infectious diseases in environment and health by advancing statistical and mathematical modeling techniques.

Scientific background:

Internationally, the United Nations has called for a more rigorous approach, including the application of spatial-temporal modeling, for surveillance and disease reporting, particularly for groups societally marginalized. To address this gap, we will invite world-renowned researchers with expertise in mathematical and statistical modeling of spatial and temporal data. In this workshop, our goal is to better integrate population and environmental data for infectious diseases using spatial modeling techniques. As objectives of this workshop: our invited speakers will talk about recent developments in spatial and temporal modeling techniques; we will also highlight recent advances in spatial individual-level statistical models (ILMs) and area-level statistical model (ALM) which are related to infectious disease outcomes.  Expected impact: Complexities of (infectious) disease ecology and quality of data used to characterize disease processes pose important challenges to authorities who need to monitor and respond to emerging and existing diseases in humans, animals, and plants. Statistical and mathematical models can have crucial roles in investigating disease-host systems and informing appropriate population management strategies. By addressing the objectives of this workshop, we will provide new mathematical and statistical techniques that solve prevalent problems stated above in the analysis of (infectious) disease data. An immediate outcome of this workshop is helping our organizations across Canada (we will also invite health researchers from Health Canada and Public Health Agency of Canada) to implement novel modeling products that will improve current technologies that are used to inform population health. Our proposed models will better reflect the true infectious disease dynamics and account for data imperfections, and therefore, researchers, end- users at various agencies, and decision-makers will have better tools for drawing the appropriate conclusions of disease ecology and devising effective disease management strategies to ultimately improve the health of Canadians and globe.

KETNOTE SPEAKERS INCLUDE

Sudipto Bannerjee, PhD, is Professor and Chair in the Department of Biostatistics at UCLA in September 2014. His research interests focus on hierarchical modeling and Bayesian inference for spatial-temporal data. He has developed spatial methods for public health data analysis, including spatial survival analysis and multivariate disease mapping for multiple cancers in spatial epidemiology. Professor Banerjee has published over 130 peer-reviewed journal articles, two textbooks, one edited handbook, and several open-source statistical software packages for modelling and analysis of spatial-temporal data. Professor Banerjee has been recognized with several awards and honours including the Abdel El-Shaarawi Award from The International Environmetric Society (TIES), the Mortimer Spiegelman Award from the American Public Health Association (APHA), a Distinguished Achievement Medal from the American Statistical Association's (ASA) Section on Statistics and the Environment, the ASA Outstanding Application Award and the George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS). Professor Banerjee is an elected member of the International Statistical Institute (ISI), a Fellow of the Institute of Mathematical Statistics (IMS) and a Fellow of the American Statistical Association (ASA).

Leonhard Held is Professor of Biostatistics at the University of Zurich (UZH). He is currently Head of the Biostatistics Department at the Epidemiology, Biostatistics and Prevention Institute, Program Director of the Master Program in Biostatistics and Director of the Center for Reproducible Science at UZH.

Prof. Held obtained his Ph.D. in 1997 at the Department of Statistics at the Ludwig-Maximilians-Universität München (LMU Munich) under the supervision of Ludwig Fahrmeir. During his Ph.D. studies he spent a year at the Department of Statistics of the University of Washington in Seattle, USA. He was Lecturer and Senior Lecturer in Medical Statistics at Imperial College London (2000-2001) and Lancaster University (2001-2002), UK. He then spent four years (2003-2006) at LMU Munich as Associate Professor of Biostatistics. Since September 2006 he is faculty member of the Medical Faculty and the Faculty of Science of the University of Zurich.

Prof. Held was Associate Editor for Biostatistics (2001-2007), Applied Statistics (JRSSC) (2002-2005) and Statistical Modelling (2007-2008). He has served as Associate Editor and Editor of Biometrical Journal from 2008 to 2017. He is currently Editor of the Annals of Applied Statistics for Epidemiology and Clinical Science.

His current methodological research interests are in statistical aspects of reproducibility and replicability, infectious disease data analysis and Bayesian inference. Most of his research is motivated by epidemiological and clinical applications. He is particularly well-known for his contributions to Spatial Epidemiology and Bayesian Biostatistics.

Andrew B. Lawson is Professor of Biostatistics in the Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, College of Medicine, MUSC and is an MUSC Distinguished Professor and ASA Fellow. He was previously a Professor of Biostatistics in the Department of Epidemiology & Biostatistics, University of South Carolina, SC. His PhD is from the University of St. Andrews, UK and was in Spatial Statistics. He has over 175 journal papers on the subject of spatial epidemiology, spatial statistics and related areas. In addition to a number of book chapters, he is the author of 10 books in areas related to spatial epidemiology and health surveillance. The most recent of these is Lawson, A.B. et al (eds) (2016) Handbook of Spatial Epidemiology. CRC Press, New York, and in 2018 a 3rd edition of  Bayesian Disease Mapping; hierarchical modeling in spatial epidemiology  CRC Press. As well as associate editorships on a variety of journals, he is an advisor in disease mapping and risk assessment for the World Health Organization (WHO). He is founding editor of the Elsevier journal Spatial and Spatio-temporal Epidemiology.  Dr Lawson has delivered many short courses in different locations over the last 15 years on Bayesian Disease Mapping with OpenBUGS and INLA, and Nimble,  Spatial Epidemiology and disease Clustering and Surveillance.

Harvard Rue is a Professor in statistics at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. His research interests are within computational Bayesian statistics and spatial statistics, and he is the PI for the research group "Bayesian Computational Statistics & Modelling" at KAUST. Most of his research is centred around the R-INLA project. He is currently one of the Editors for the ISI journal Stat, and is a Highly Cited Researcher according to the Highly Cited Researchers 2019 list from the Web of Science Group.

Lance A. Waller is a Professor in the Department of Biostatistics and Bioinfomatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Board on Mathematical Sciences and Analytics and has served on National Academies Committees on applied and theoretical statistics, cancer near nuclear facilities, geographic assessments of exposures to Agent Orange, and standoff explosive technologies.  His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology.  His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley).  Dr. Waller has also lead two separate T32 training grants, one from NIGMS and the other from NIEHS, and served as the Director of the NHLBI Summer Institute for Research Training in Biostatistics (SIBS) site at Emory for the past 9 years.