About the Forum
More about the Event
The COVID-19 pandemic has challenged science and society to an unprecedented degree. Human lives and the future of our society depend on the response. That response, in turn, depends critically on data. This data must be as complete and accurate as possible; easily and flexibly accessible, and equipped to communicate effectively with decision-makers and the public.
The COVID-19 Data Forum is a project to bring together those involved with relevant data in a series of multidisciplinary online meetings discussing current resources, needed enhancements, and the potential for co-operative efforts.
The first meeting was a webinar featuring a series of related talks, which took place on
May 14, 2020 at 19:00 UTC (12PM San Francisco, 8PM London, 5AM Sydney).
Events will be a public, all welcome.
This conference adheres to the Stanford Data Science's Code of Conduct policy.
Details on the Forum
The May 14, 2020 webinar will be followed by a series of private and public discussions, involving active participants in the wide range of disciplines concerned with COVID-19 related data. These discussions will also actively seek dialog with decision-makers and others relying on information from the data and from models or analysis based on it.
The Forum places particular emphasis on being open to all relevant interested groups and, with respect to computing, to considering all useful tools, languages and environments.
We hope that the COVID-19 Data Forum discussions can usefully proceed through three stages of questions:
- Where are we now, with respect to resources and needs?
- What immediate steps (e.g. in terms of sharing) would make improvements?
- What potential projects for new tools, standards, or data models might be worth undertaking?
At all stages, there are many specific topics that need discussion. To sort them out, three kinds of activities are useful categories: obtaining the data; using the data; and communicating about the data.
Obtaining Data. The COVID-19 data challenges begin with just acquiring data of the range and quality needed. A very wide range of data is needed, in three dimensions: geographical, time and domain. Depending on the purpose, data may be needed either very specifically local or at the widest global level. Both are challenging --- finding reliable local sources and resolving hugely variable international ones, for example. Particularly on the global (or even national) scale, variable quality will often be a challenge.
Timeliness of the data is clearly essential, particularly as public health regulations and other societal responses change. But scientific models and analysis may also need to have data over a long time span.
The pandemic has touched our lives in many ways: directly in our health but also in nearly all aspects of our economy and society. As the world responds, data science will need to consider all these aspects, requiring data from the microscopic level of the virus to the population data for epidemiology, social science, and economics.
Using Data. The response to the COVID-19 pandemic from the Community continues to generate crucial data-based results. Epidemiologists, public health experts, data scientists, and other researchers have produced a large number of predictive models, interactive resource allocation applications, and disease tracking dashboards.
Moving ahead, it will be important to have easy, consistent access to the best data for all these efforts. Co-operation and co-ordination among the teams involved can enhance the scope and help ensure that model results and comparisons use consistent, well-defined data sources.
Communicating Data. A key goal of the Data Forum is to improve communication between decision-makers (in public health, government, and elsewhere) and the data science and general research community. Many tools have been developed for visualizing and interacting with data. It's important to understand how these can be used and enhanced for the decision-making community. We look forward to participation in our meetings by interested members of this community.
Another important goal is to improve the information flow to the broader community, with emphasis on giving insight and avoiding misdirection.
Previous Speaker include:
Orhun Aydin, Researcher and Product Engineer, ESRI
Ryan Hafen, data scientist consultant with Preva Group, and adjunct assistant professor, Purdue University
Alison L. Hill, Research Fellow and independent principal investigator at Harvard’s Program for Evolutionary Dynamics.
Noam Ross, Senior Research Scientist, EcoHealth Alliance
Joseph Rickert - Chair R Consortium Board of Directors
John Chambers - Stanford Department of Statistics and Stanford Data Science
Michael Kane - Assistant Professor, Department of Biostatistics, Yale University
Balasubramanian Narasimhan - Senior Research Scientist, Department of Biomedical Data Sciences, Stanford University
Chris Mentzel - Executive Director, Stanford Data Science