Every Monday, many parents grapple with deciding if their child is too sick to attend school. Similarly, public health officials face monumental decisions during epidemics, needing robust data and swift action to prevent disease spread while minimizing societal disruption. Professor K. Selçuk Candan of Arizona State University is spearheading a transformative research initiative called PanAX, which leverages data science and artificial intelligence to model epidemics more effectively. Funded by the National Science Foundation, PanAX synthesizes past epidemic data with machine learning, providing timely insights based on historical patterns and behaviors. This innovative tool avoids the limitations of traditional mechanistic models by applying causal reasoning to understand disease dynamics. With an educational component, it trains students in advanced data science applications. Ultimately, PanAX is a crucial advancement for public health responses, enabling quicker and more accurate forecasting and enhancing our preparedness for future epidemics.
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