Machine Learning Techniques for Early Detection and Situational Awareness of Rabies Outbreaks

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-01 (month precision)Source: Jan-25

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Health and Medical

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

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.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Microsoft

Custom-developed code

No

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

Not reported

Federal Data Catalog

www.nature.com ↗

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported