1. How can implementing artificial intelligence technologies in epidemic surveillance systems be enhanced to better detect and respond to disease outbreaks?
I think a great potential is the power of the LLM models nowadays that allow real-time monitoring of online unstructured textual data. Nowadays our epidemic preparedness is dependent on official reporting, but this may be a long time lag from the outbreak patient zero, or the authorities may fail to report the outbreak honestly, especially in authoritarian countries. The epidemic surveillance system provides a potential early warning, providing transparency for authorities to investigate.
2. What potential benefits do you see in utilizing AI for public preparedness, and what challenges might arise in implementing these technologies effectively?
The benefit is that making use of social media data is one of most responsive sources of information. As seen in many previous examples, early warnings of a potential outbreak is often reported by voices within a community. For example, the reports of COVID-19 were first reported by citizens in Chinese community and doctors of Wuhan hospital on Chinese social media. This makes surveillance systems potentially be able to catch epidemics very early. However, the challenge is of course that it is difficult to filter quality and reliable information from social media. People frequently posts rumours and outrageous, untrue things on their accounts. Social media are also filled with bots that artificially paste fake content. It may be difficult to distinguish the true from the fake, leading to a system with frequent false alarms.