A note of a virtual conference on AI & CoViD-19 - Section 3
Full Agenda: HAI Stanford
Full video: Youtube
Edit (8/4/2020) I add 4 more sections.
Session III: Tracking the Epidemic - 3:01:45
Taiwan’s Use of Data Analytics to Control COVID-19
- Based on the JAMA paper on 3 Mar 2020.
- How Taiwan prepares for the next crisis starts with the Communicable Disease Control Act.
- How Taiwan designed a center command center with three databases: Insurance stockpile systems, Infectious Disease Stat System, and media surveillance.
- In the first 30 days, how Taiwan recognized the crisis, including boarding people from Wuhan and the command center.
- Linked dataset for big data analytics between insurance and immigration.
- Government stockpile: 44M surgical mask, 1.0M N95.
- Diamond Princess ship story.
- Mobile app to track inventories for masks.
- Infrared temperature map.
- Communication and Politics.
- Challenge for Taiwan.
Tools for Estimating Unreported Infections of COVID-19
- Pathengon Genomics and mathematical models
- Why is undetected infection important? Asymptomatic or subclinical infection, limited testing capacity, restricted criteria for testing.
- Consequence: spreading without notice, impacted large regions, longer time to elimination.
- How to estimate: Mass testing, but expensive and time consuming; modeling; viral genomic data.
- An example of a viral genomic data by time series with Wuhan Data.
- Lead to Recommendation of face mask use by CDC
Methods for Real Time Mapping of COVID-19 Cases Worldwide
- Started in late December. It is a challenge to create a visualization (health map) because of a lack of resources.
- Solved by going to local news and China sources.
- It needs an army of people to get data, not just by machine learning and web scraping. So people created a network of researchers and professionals for every single case around the globe > A risk and intervention.
- An example of Baidu data about actions when you have detailed information.
- An example of crowd source data from Thermia. Data connected to a medical device.
- Symptom checkers with AI chatbot called Buoy. Other side, collect data to increase local surveillance.
- Another flu near you to crowdsource data.
- Another example with CoViD near you. 260 people reported symptoms.
- Mining for social measure: to understand social distancing impact with collaboration with CDC, e.g. mapping data between policy and impact.
Epidemiological Forecasting Tools for COVID-19
- Flu background: Influenza and author story with connection with CDC
- Flu prevalence data: Most target interest of CDC is % influenza like illness started in 1997. Data collected weekly. Why? Based on the severity pyramid.
- How to forecast a season epidemic:
- Choose a type of model: mechanistic vs. statistical;
- For stat models, collects lots of data like Google search queries, Wikipedia page hits, thermometer sales, etc.
- For stat models, long history of training data to learn predictive relationships.
- Either type, should model revisions (backcasting!)
- Either type, prob forecasts are key; as are ENSEMBLEs.
- How to not forecast a pandemic: SEASONAL epidemic
- Digital surveillance sensor > erratic model;
- Without sensors, ILI decreases.
- Information about CDC flu forecast challenge about %ILC and hospitalization.
- CMU effort in data challenge at https://delphi.cmu.edu/crowdcast/
A mobile app intervention to slow COVID-19 using crowdsourced data
- Shared a story of Wuhan quarantine along with a speaker’s acknowledgement about the scale, and how she started to know about the China virus tracking app with a concern about a pervasive data collection which is not accepted in the US.
- GPS is perhaps a solution with anonymous data for contact tracing apps, which allows us to find individuals contacted by an illed person. The challenge is highly restricted information of medical data.
- Talked about a tracking app for the flu by using bluetooth protocols which are localized, so can deconstruct data. It allows us not to collect data of a person to do intervention.
- Bluetooth strength allows us to know about the proximity of the person between two people.
- Some other teams also work on this topic: COVID watch, TraceTogether, PrivateKit: Safe Paths, Colgi: Community Epidemiology in Actions.
AI for COVID-19: An online virtual care approach
- Healthcare access and scalability with 50% world with no access to essential health service, ~30% of the US adults under-insured. ⅓ Americans self-diagnose online, ~15 minutes to capture information, diagnose and recommend treatment. COVID-19 is putting a huge stress on the medical system.
- Problem of telehealth: it only solves some problems, importantly can NOT SCALE.
- A proposal towards AI powered scalable health systems with a human in the loop, mobile first care.
- A demo of how it looks in practice (chat-based application) with client side and doctor side.
- An example of UI for Personalized diagnostic assessment.
- Some peer-reviewed research about diagnosis with an example of question similarity with automated question and answering & feedback loop. But it is hard to verify because of lacking ground truth.
Knowledge technology to accelerate open science in addressing the COVID-19 pandemic
- COVID-19 will change science and the way we do science.
- 10-20 years change drastically with data and hypothesis. There is a shift from output science, but science. Data of COVID-19 becomes available leading to excitement.
- Problem is that there is too much data without a catalog which does not have a catalog.
- The point to make: Open data is about more than disclosure. It must be fair, findable, accessible, interoperable, reusable. Nevertheless most of the data is not fair.
- Example of a failure to use standard terms makes datasets often impossible to search. E.g. name of variables (age, Age, AGE, ‘Age, age (after birth), age (in years), age(y), age[y], age[years])
- Another example of bad meta data from NCBI BioSample 73% of “Boolean” metadata values are not actually true or false; 26% of data can not be parsed into integers, 68% of metadata entries that supposed represent terms from standard vocabularies do not actually do so.
- Why does it matter? To find data from relevant studies, integrate new results, re-explore existing data and verify published samples.
- Solution: CEDAR approach to standardize Metadata with three step process.
- An application of a use of CEDAR with project VODAN to help researchers learn about epidemic.
- Online data will never be fair until we standardize metadata with templates<-controlled terms<-technology that makes it easy for investigators to annotate their datasets in standardized and searchable ways.
What we can learn from Twitter Analysis about COVID-19
- The psychological impact of COVID-19: uncertainty/ anxiety, unemployment and loneliness -> The path of well-being and mental health.
- Unemployment and life satisfaction: job loss highest rate ever since the WWII
- Digital social transition might be a solution for loneliness.
- Introduction to do text analysis on twitter nationwide of the US. Scraping data on 26/2-26/3 in 2015 counties: Urban counties Washing your hands, panic buying, work from home, canceled events, wash hands; Educated counties: Government, healthcare, articles/ news, will be canceled, testing; Older counties: Trump and economic impact; Younger counties: wash hands; Voted for Trump: like the flu; Most negative sentiment: Scared, Economy, Trump;
Tagged #summary, #note, #book.