This post focuses on sensing technologies indispensable to many #AI applications in fields like agriculture, medicine, and #epidemiology. Interconnected devices within the Internet-of-Things, #IoT, are poised to change practices by delivering more timely, accurate data than previously available.
For example, epidemiologists currently rely on data sources with significant limitations, including (1) clinical case data tightly controlled by privacy laws and healthcare provider policies, (2) field investigations like contact tracing interviews, which are affected by selection, culture, and human memory, and (3) dated reports from agencies like CDC and WHO.
Timely, accurate, and reliable data from sensors, including wearable devices, can circumvent these limitations and enable epidemiologists to gauge the spread, virulence, and other aspects of infectious diseases in near-real-time. #Epidemic models like the #SIR become more accurate and useful when factors like the number of infected persons, average contacts by infected persons, and the probability of contacts leading to infections are more accurately estimated and quickly updated. I will discuss the implications in the context of COVID in my next post.