Session III: Tracking the Epidemic
Taiwan’s Use of Data Analytics to Control COVID-19
Transparency is critical in a democracy if digital technology is deployed to ensure protection of public health and civil liberties. Jason Wang will look at how technology has the potential to curb the spread of COVID-19, including examples where digital technology has successfully been used for disease surveillance. Taiwan provided a great example on the use of technology in case detection, contact tracing, isolation of cases, and quarantine of exposed individuals.
Tools for Estimating Unreported Infections of COVID-19
Viruses continue to mutate as they infect people, even in asymptomatic individuals. By leveraging the number of mutations between viruses captured in reported individuals, Lucy Li’s research uses mathematical models to predict the most likely number of missing infections. In this presentation, Li will talk about her research to identify the number of unreported infections using the viral genomes and WHO time-series data. Quantifying the total number of infections, not just the reported ones, is important for accurately characterizing the fatality rate given an infection, identifying changes in capacity for testing and providing an indicator for how long it will take to contain the epidemics. Data from China suggests that widespread testing for the virus even amongst asymptomatic individuals is necessary to effectively quantify infections and contain the epidemic. Furthermore, this large number of unreported infections indicates that even if the number of cases were going down, the total number of infections could remain high for a long period of time, so public health interventions would need to be maintained to prevent the number of infections from bouncing back.
Methods for Real Time Mapping of COVID-19 Cases Worldwide
In his presentation, John Brownstein presented how Harvard Medical School has been tracking COVID-19 since late December, and how the research includes a health mining tool called HealthMap, which is freely available. He also addressed the various ways to gather valuable data from the public both to help track the virus but also to understand social distancing impact.
Epidemiological Forecasting Tools for COVID-19
Reporting from one of the CDC’s Centers of Excellence for Flu Forecasting, Ryan Tibshirani will speak about nowcasting and forecasting the Covid-19 pandemic at the level of individual US counties. These nowcasts/forecasts will be aggregated up to the state level and submitted as part of Covid-ILI forecasting efforts at the county and state level, assisting state and local public health officials, who can figure such forecasts into their decisions. This research adapts two existing systems for flu forecasting based on statistical machine learning and wisdom-of-crowds, respectively.
A Mobile App Intervention to Slow COVID-19 Using Crowdsourced Data
With a sufficient diagnosis rate and contact tracing accuracy, COVID-19 can be contained.
Non-pharmaceutical pandemic interventions fundamentally make a trade-off between two important social goods: loss of life from the pandemic and economic impact, which influences health and well-being outcomes indirectly. In general, non-pharmaceutical approaches to infectious disease control have the following components: filtering (picking a subset of the population) and intervention (modifying the behaviour of these people). Without good filtering, broad quarantines and social distancing are needed, incurring a huge cost in the form of negative impact on people’s lives. Tina White will speak about her research on performing automated contact tracing at scale using anonymized Bluetooth proximity sensing.
AI for COVID-19: An Online Virtual Care Approach
With half of the world’s population lacking access to healthcare services, and 30% of the adult population in the US having inadequate health insurance coverage to get even basic access to services, it should have been clear that a pandemic like COVID-19 would strain the global healthcare system way over its maximum capacity. In this context, many are trying to embrace and encourage the use of telehealth as a way to provide safe and convenient access to care. However, telehealth in itself can not scale to cover all our needs unless we improve scalability and efficiency through AI and automation. In this talk, Xavier Amatriain will describe how his work on combining latest AI advances with medical experts and online access has the potential to change the landscape in healthcare access and provide 24/7 quality healthcare. He will also describe how those approaches have been used to address the urgent and immediate needs of the current pandemic.
Knowledge Technology to Accelerate Open Science in Addressing the COVID-19 Pandemic
The response to COVID-19 involves a global community of investigators who want rapid access to emerging results and the opportunity to examine those results in the most efficient and reliable way possible. Science advances most effectively and rapidly when investigators have the opportunity to verify one another’s data and to explore those data in search of new discoveries. The urgency of the crisis makes “open science” an important goal so that researchers can share, access, and re-analyze one another’s data as soon as the data become available. In this presentation, Mark Musen will talk about the development of a Web-based application, known as the CEDAR Workbench, which makes it easy for scientists to create the metadata needed to describe their experiments—including information about the subject of the experiment, the experimental conditions, and the interventions that were made. The system also uses AI to learn patterns in the metadata, helping investigators to streamline their entry of new metadata. There is unprecedented pressure to speed up research on COVID-19, and tools that support open science can enhance the efforts of the entire community that is working so furiously to understand this virus.
What We Can Learn From Twitter Analysis About COVID-19
Stanford HAI junior fellow Johannes Eichstaedt is a psychologist who uses social media to understand the psychological states of large populations. He examined Twitter posts to learn how the virus and social distancing are affecting our anxiety and life satisfaction and how factors such our age, education, and hometown size can impact our emotions.
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