The response to COVID-19 in Taiwan has been quite innovative and it involved big data analytics, new technology, and provocative testing. As you can see on this paper published in JAMA by Dr. Jason Wang, Taiwan is not unfamiliar with information technology and also big data approach, especially to electronic health record. The other example is original paper that’s published by our Deputy Minister Dr. Chen, containing COVID-19 among 627,000 persons in contact with the Diamond Princess cruise ship passengers
who disembarked in Taiwan: A big data analytics. This paper this quite interesting paper described an event that the 600,000 person that’s that’s actually contacted by the 2000 cruise ship passengers from a ship named diamond princess who spent about 8 hours touring new Taipei City and then they left. But after three days, it is revealed that they are people who got infected by the COVID-19 among these 2,000 passengers and we were able to look at all the communication data with the cellphone positioning technology. And all that… to look at all the people that has contact with 2,000 people. And actually they found 600,000 people. And we track order 600,000 people until for 14 days.
That’s how Taiwan is able to control the COVID-19 pandemic. Some other contributions like the application of Google search trends
for risk communications infectious disease management: A case study of the COVID-19 average in Taiwan. This one is published from our Institute Taipei Medical University, the graduate Institute of biomedical informatics by Dr. Emily Su. They used the Google search trend to look at the COVID-19 outbreak. So it’s a tool that we can use to predict future or recurrence of this type of pandemic. The International Medical Informatics Association, which is also an NGO under WHO in the group of the fellowship on International Academy of Health Science Informatics, actually formulated a recommendation letter for WHO on April 1st, this year 2020. And in the letter, the focus, we recommend WHO to do four things. Ok, the first thing is about predict.
So we need to collect big data and then predict. Using the AI predict power for precision prevention. The second point is telemedicine. Although people…we have been doing telemedicine for many years, but it has not become a main stream of medicine delivery. So we need to use more of it. And the third point is sharing big data. And the fourth point is about transparency. So I’ll go through these points one by one in the next few minutes. So about telemedicine. The… although there have been research on telemedicine but unfortunately, we do not use telemedicine often enough in our medical practice or everyday healthcare delivery. That’s why this pandemic actually presents an opportunity of large scale implementation of telemedicine.
There’s one company called Babylon in in UK. That’s working with NHS to deliver 10 to 15% of all the patient visit through telemedicine. In Taiwan we also formulate we assemble a fifty volunteered dermatologist and we established this TWcanHelp Facebook group. Anyone can join the Facebook group and if you have a dermatology problem during this quarantine from COVID-19, you could take a photo and then put your question in there. There are 50 dermatologists that’s ready to answer your question 24 hours a day. So far we’ve got hundreds of questions from more than 400 members participating, so there are many different ways to deliver telemedicine through the current information and communication infrastructure.
Because we already have very good mobile and internet connection so so I think it’s quite easy after 2020, we will see more tournaments and delivered around the world. Instead of having people traveling, you know, go to the hospital; go to the clinic, exposing to additional unnecessary threat or risk to these people. Okay, so number three is about sharing and in collecting these big data. Although there are individual efforts of sharing COVID-19 dataset around the world but unfortunately, these data set are inconsistent in terms of granularity, in terms of format, in terms of data model and dimension. For example, on Kaggle, you can see data shared from Italy which is based on city, each city.
Data share from Korea is based on the patient. But only have very very few and low granularity data share from each patient. In Japan, they use a different format. You know U.S. uses a different format. So if we’re going to make the big data really useful we have to be very consistent and use a common data model when we share the data. Number four is about transparency. We need to have reliable sources for all this information and these informations should be shared and exchanged to every part of the world. I mean social media is one way to share it. It’s a good place to share it.
But it’s very important that we really have transparency of how the data are collected and how the statistics were done, you know. It’s infamous for some of the countries to provide wrong information or even fake data. And these data would actually poison the big data set that’s gonna be used in the future AI application. So transparency is the key for Humanity to fight the COVID-19 and future pandemics.