Enhancing influenza A risk assessment rubrics: leveraging predictive correlates and machine learning from in vivo experiments.

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2023-05 (month precision)Source: May-23

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

The Pathogenesis Laboratory Team (Immunology and Pathogenesis Branch, Influenza Division, NCIRD) routinely performs influenza A virus (IAV) risk assessment studies in the ferret animal model, to assess IAV pathogenicity and transmissibility in a relevant small mammalian model. However, these studies are typically performed in isolation, with minimal efforts to comprehensively examine how each biological parameter obtained from the work correlates to disease severity and virus transmissibility and how these parameters can be used as a whole to improve risk assessment efforts. We have generated large sets of data collected from 25+ years of performing these in vivo studies, to identify predictive correlates associated with pathogenicity and transmissibility outcomes, and utilized machine learning approaches to better predict the potential public health risk posed by emerging influenza A viruses.

Expected benefits

CDC’s Influenza Risk Assessment Tool (IRAT) rubric is utilized to assess the pandemic potential of novel or emerging IAV that pose a threat to human health. A better understanding of which key quantifiable metrics of virus behavior in this species are most frequently correlated with virulence or transmissibility would greatly aid CDC leadership who score viruses in this rubric to ensure contributing data from the ferret model is rigorously and accurately contextualized within these risk assessments. As the project relies solely on previously collected in vivo data, it represents a valuable opportunity to support the 3 R’s of animal research (reduction, refinement, and replacement), gathering additional information from 25+ years of research in the ferret model already conducted at CDC, thus highlighting the agency’s commitment to responsible and ethical animal research. Numerous peer-reviewed publications have already resulted from this work, including development of predictive models of lethal disease and virus transmissibility, and assessment of which parameters and sample types collected during routine laboratory experimentation offer highest predictive value in these models. These first-in-field analyses also provide an analytic framework and template for subsequent studies with other data collected at CDC.

System outputs

The machine learning work we perform identifies which variables are more predictive for the associated pathogenesis or transmission outcome, which better informs us of the biology of the influenza-ferret model system for how to interpret the clinical and virological data we collect and better inform pandemic risk assessments.

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

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

No

Demographic features

k) None of the above

Training and evaluation data

Not reported

Federal Data Catalog

data.cdc.gov ↗

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