Member States of the World Health Organization have shown their commitment to global public health since their membership and even before. It is not a coincidence that they have also been working collaboratively to protect people from the threat of influenza continuously. Global Influenza Surveillance and Response System (GISRS), a global platform to put countries’ data, preparedness, response and also their monitoring of the influenza virus, has been in operation since 1952. Here is a promotional video of GISRS on its 65th anniversary by the WHO:
Even though with GISRS we could fight the seasonal, pandemic and zoonotic influenza collaboratively the fact that it does not offer a real-time access to the data has shown its deficiency. For example, the latest influenza global epidemiological and virological update has been published in the WHO official website on 23 November 2020, based on the data up to 08 November 2020. The preparations for a real-time surveillance system have been done with the digital age. This week we would like to present to you one of those systems: how to make surveillance of the influenza epidemics via Twitter?
Social media has been on the radar for the public health workers by means of its potential to offer real-time access to millions of short, geographically localized messages. Since those messages also include information about the health status of the social media users, it is not a surprise that we could manage to make a faster and more efficient surveillance with those messages.
One of the obstacles to make such a surveillance system work sufficiently is that the data algorithm that we develop should be able to differentiate between the chatter and the infection that has been mentioned in the messages. An algorithm that has been developed by Broniatowski et al. has managed to develop such an algorithm on Twitter. They have filtered all the relevant tweets from October 2012 to May 2013 and they have seen that their predictions just by using their own algorithm have been consistent with the epidemics report by CDC in the same time interval
They also mention in their paper that their estimates are significant over general seasonal trends, which actually shows us that by using social media they could actually predict the tendency of the epidemics. This is just fascinating. It is no surprise that you can achieve a lot by using algorithms and more generally, technology, even in the healthcare industry. As FluAI we are now just trying to help the people to get accurate information about their upper respiratory tract infections and give them medically accurate suggestions via artificial intelligence developed by us. The world is leading us the way, and we are just trying to catch up. We hope that you have found some inspiration in this blog post.
Broniatowski, D. A., Paul, M. J., & Dredze, M. (2013). National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic. PLoS ONE, 8(12), e83672. doi:10.1371/journal.pone.0083672
PM & CLINICAL TEAM