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NYC Office of Technology and Innovation

NYC - DTPR AI Register

AI-Assisted Pathogen Cluster Analysis for Disease Surveillance

What it is for

Healthcare
  • PHYLOViZ supports epidemiological surveillance by visualizing evolutionary relationships between pathogen strains, enabling public health scientists to detect outbreak clusters from laboratory sequencing data.
Research & Development
  • The tool is used for outbreak visualization and surveillance reporting — functioning as a scientific instrument within a public health laboratory environment rather than a consumer-facing service.
Run by
Department of Health and Mental Hygiene (DOHMH)
Where
No fixed location
Kept
Retained Not specified in source material
Shared with
Not stated by the NYC Office of Technology and Innovation.
Your copy
You cannot see the data it holds about you. What you can do

What it is for

HealthcareResearch & Development

PHYLOViZ is a bioinformatics tool used by the NYC Department of Health and Mental Hygiene to visualize the evolutionary relationships between bacterial or pathogen strains using a minimum spanning tree algorithm. It helps public health scientists identify clusters of related isolates that may indicate an outbreak. The system processes laboratory sequencing data — not personal identifying information — and produces graphical relatedness networks used for surveillance reporting.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Allelic profiles or phylogenetic outputs generated from sequencing-based typing analyses of biological samples. No personal identifying information is included in these inputs.

Processing

Clustering & Segmentation
  • Uses the goeBURST algorithm — a refinement of eBURST — to cluster pathogen isolates by genetic similarity. The algorithm generates a complete minimum spanning tree to represent possible evolutionary relationships between strains.

What it does

Deciding (Analytical AI)
Human decides
  • PHYLOViZ applies the goeBURST algorithm to classify and rank allelic profiles into minimum spanning tree clusters. Human epidemiologists and laboratory scientists interpret the resulting graphical networks and decide on any public health response.

Outputs

About a measurement
Anonymized data
  • Graphical relatedness networks identifying clustered isolates, used for outbreak visualization and surveillance reporting. Outputs represent relationships between pathogen strains, not individuals.

Run by

Department of Health and Mental Hygiene (DOHMH)
  • The agency responsible for protecting and promoting the health of all New Yorkers. The Disease Control – Public Health Laboratory division operates PHYLOViZ to support outbreak investigation and surveillance.

NYC AI Use Cases — PHYLOViZ

Built by

Not stated by the NYC Office of Technology and Innovation.

Kept for

Retained Not specified in source material
  • The register does not specify a retention period for the graphical outputs or the allelic profile inputs. The outputs are used for outbreak visualization and surveillance reporting purposes.
  • Duration: Not specified in source material

Shared with

Not available to me
  • Outputs are used internally by public health laboratory staff for surveillance and outbreak investigation. Members of the public do not have individual access to the resulting network graphs or cluster assignments.

Stored

Not stated by the NYC Office of Technology and Innovation.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the NYC Office of Technology and Innovation, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe NYC AI/ADS Use Case Register publicly discloses that PHYLOViZ uses the goeBURST algorithm for minimum spanning tree generation. The tool's methodology and source code are available at https://www.phyloviz.net/. As no personal identifying information is processed, individual-level transparency rights have limited applicability; however, members of the public may learn about the system's general logic through the public register.

Risks and safeguards

No risks or safeguards have been published for this system yet.