One Health Threat Detection and Risk Assessment Platform (OH-TREADS) / Planner

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Health and Medical

Operational functions

Translation and transcription, Cyber threat detection Derived by keyword rules; see methodology

Problem the AI is intended to solve

Early warning, Decision Making, and Situational Awareness

Expected benefits

Visuals will be created using a broad variety of data pulled from open-source, public, and non-public sources. AI/ML will make it possible to ingest data from a variety of digital formats, and translate it to usuable information into machine-readable data. The volume of data, and lack of clear data collection standards, requires AI to help merge streams with different data ontologies while facilitating data interoperability with mis-paired data sets. Neither activity is, or can be, practically performed by people. AI/ML will be further used in Planner to generate risk and predictive scores in addtion to displaying relevant information and analyses that help analysts understand health security threats and broadly monitor the health security landscape. AI/ML is meant to provide comprehensive situational awareness for health surveillance and public health response, with target capabilities that: enable global situational awareness of current and potential health risks from a One Health perspective; assist in early warning of health threats by location, facility, and species; aid in rapid identification of health threats at population, facility, and greater geographic resolution; and support data-driven decision making to prevent, mitigate, and respond to health threats.

System outputs

The main AI system outputs will be anomaly detection following the translation of information into a structured, machine-readable datasets. AI/ML is then used for risk identification and disease prediction that are overlayed on a map and displayed with other wholistic visuals. The visuals and analytics can then be combined with critical infrastructure locations, resources, or capabilities (feeral, state, local, tribal, and territorial) to aid response and decision making.

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)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

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

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

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