Researchers from National Center for Emerging and Zoonotic Infectious Diseases Detail New Studies and Findings in the Area of Rabies (Machine learning to improve the understanding of rabies epidemiology in low surveillance settings).

Researchers from the National Center for Emerging and Zoonotic Infectious Diseases have conducted studies on rabies epidemiology in low surveillance settings. They compared machine learning techniques to predict rabies cases in animals investigated as part of an Integrated Bite Case Management program. The study found that Extreme Gradient Boosting performed better than Logistic Regression in predicting rabies cases, leading to a 3.2-fold increase in epidemiologically useful data compared to routine surveillance strategies. This research demonstrates the application of machine learning to strengthen zoonotic disease surveillance in resource-limited settings. [Extracted from the article]