Machine Learning Techniques for Early Detection and Situational Awareness of Rabies Outbreaks
Description
Rabies is enzootic in wildlife and modern-day surveillance techniques are too weak to fully capture the geographic extent of outbreaks nor early outbreak detection. Our ML algorithm uses public health surveillance data to "fill in the gaps" inherent in wildlife disease surveillance programs to accurately and rapidly detect outbreaks and deploy public health resources.
Detailed example
Disease trend for real-time monitoring of rabies, Probabilities of disease occurrence over time and space. Spatiotemporal clustering with tiered risk classification differentiates stable circulation from emerging rabies transmission, improving situational awareness and guiding seasonally targeted surveillance and interventions, underscoring the need for real-time data sharing to strengthen outbreak response.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
This model is currently being using in domestic and international settings for early rabies outbreak detection. This information is shared with relevant public health authorities to initiate preventive actions which often include: public awareness campaigns/social media, deployment of vaccines for animals and people, deployment of testing reagents to bolster surveillance.
Controls / human review
ATO: Yes; PIA: Not published