AI-Assisted Genomic Phylogenetic Analysis for Outbreak Detection
What it is for
Healthcare
- Supports infectious disease surveillance by generating phylogenetic trees from genomic sequences to help rule outbreaks of COVID-19 or other pathogens in or out.
Research & Development
- Produces phylogenetic trees used by epidemiologists to assess transmission clusters and outbreak linkage — functioning as a scientific instrument within public health genomic surveillance programs.
- Run by
- Department of Health and Mental Hygiene (DOHMH)
- Where
- No fixed location
- Kept
- Not stated by the NYC Office of Technology and Innovation.
- Shared with
- Accountable organization
- Your copy
- You cannot see the data it holds about you. What you can do
What it is for
This system uses maximum-likelihood statistical methods to build phylogenetic trees from genomic sequence data, helping public health scientists determine whether cases of COVID-19 or other infectious diseases are part of the same outbreak cluster. It runs on biological samples — not on identified individuals — and produces evolutionary relationship maps used by epidemiologists to guide outbreak investigation. No personal identifying information is collected or disclosed.
What it collects and what happens to it
Data taken in
Operational data
Anonymized data
- Multiple sequence alignments derived from public health genomic datasets — genome sequences from pathogen isolates, not linked to named individuals.
Processing
Classification & Prediction
- Maximum-likelihood phylogenetic inference: the algorithm scores tree topologies to find the tree that best explains the observed sequence variation, assigning branch lengths and clustering isolates by genetic relatedness.
Clustering & Segmentation
- Groups pathogen isolates into phylogenetic clusters based on genetic similarity, enabling epidemiologists to identify likely transmission chains and outbreak boundaries without predefined labels.
What it does
Deciding (Analytical AI)
Human decides
- Applies maximum-likelihood regression to genomic sequence alignments to score and rank evolutionary relationships among isolates. A human epidemiologist reviews the resulting phylogenetic tree to decide whether cases constitute a transmission cluster.
Outputs
About a measurement
Anonymized data
- Phylogenetic trees representing evolutionary relationships among pathogen isolates, with branch lengths reflecting genetic distance. Used to assess transmission clusters and outbreak linkage. Not linked to identified individuals.
A recommendation or prediction
Anonymized data
- The phylogenetic tree is an advisory output: it provides evidence that epidemiologists use to recommend whether cases are epidemiologically linked, but it does not make binding public-health decisions autonomously.
Run by
Department of Health and Mental Hygiene (DOHMH)
- The NYC Department of Health and Mental Hygiene's Disease Control division operates IQTREE in its Public Health Laboratory to support genomic epidemiology and outbreak investigations.
Built by
Not stated by the NYC Office of Technology and Innovation.
Kept for
Not stated by the NYC Office of Technology and Innovation.
Shared with
Not available to me
- No identifying information is collected or disclosed. Outputs are phylogenetic trees of pathogen genomes, not personal data accessible to individuals whose samples may have contributed to the analysis.
Available to the accountable organization
- Phylogenetic tree outputs are available to the NYC Department of Health and Mental Hygiene's Disease Control division and Public Health Laboratory staff for epidemiological investigation.
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.
- AI registerNYC Automated Decision Systems — IQTREE (2025)NYC Department of Health and Mental Hygiene, Disease Control – Public Health Laboratory. Reported 2025. First used 2020-05.
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- AI registerNYC Automated Decision Systems — IQTREE (2025)
- Register entryPublished by the NYC Office of Technology and Innovation. Reference f7c795fa. This disclosure was drafted with AI assistance.Schema: ai@2026-05-06-beta
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 TransparencyIQTREE is open-source software (https://github.com/iqtree/iqtree3). The algorithm — maximum-likelihood phylogenetic inference — is published in peer-reviewed literature and its source code is publicly available for inspection.
- Right to Purpose LimitationAccording to the register, no identifying information is collected or disclosed. The system is used exclusively for infectious disease outbreak investigation by the Public Health Laboratory. Data is not used for purposes beyond genomic epidemiology surveillance.
Risks and safeguards
No risks or safeguards have been published for this system yet.