By Ankur Sharma, PhD – Singapore, 9 May 2026

TL;DR – Andes virus became the first hantavirus confirmed for human-to-human transmission. A cruise ship returned from Patagonia in April 2026 with a suspected case on board. I built outbreak-agent – a 4-node LangGraph triage pipeline – to show exactly how an agentic AI system should handle this kind of event.

1. The Ship That Came Home Different

MV Hondius is a purpose-built expedition vessel – 114 metres, ice-class, designed for the kind of traveller who considers Antarctica a weekend destination. In April 2026, it returned from a Patagonia itinerary carrying a passenger who had spent excursion days in the Andes foothills near the Argentine–Chile border.

The foothills matter. Andes orthohantavirus (ANDV) circulates in long-tailed pygmy rice rats (Oligoryzomys longicaudatus) – a rodent that has quietly expanded its range northward by approximately 4.7 km per year as Andean glaciers retreat and scrubland reclaims former pasture. The 2026 Southern Hemisphere autumn was the warmest on record for the region. Rodent density indices in Chubut Province were 2.3x the 2019 baseline.

This is the climate-epidemiology link that most outbreak reports bury in a supplementary table. It should be on page one.

The passenger developed fever and myalgia on day 8 after exposure – consistent with ANDV’s notoriously long and variable incubation window of 8 to 45 days (median 18 days). By the time the ship docked, the case had shared a dining table, a shore excursion minibus, and recirculated cabin air with dozens of other passengers.

Why this matters: ANDV is the only hantavirus with confirmed human-to-human transmission. Every other hantavirus – Sin Nombre (SNV), Puumala (PUUV), Seoul – requires direct rodent-excreta contact. ANDV does not. This changes the entire contact-tracing calculus for a cruise ship scenario.

2. What Hantavirus Actually Does (The Biology You Need)

Hantaviruses are negative-sense RNA viruses, family Hantaviridae, with a tripartite genome: S (small), M (medium), and L (large) segments. ANDV causes Hantavirus Cardiopulmonary Syndrome (HCS) – the South American clinical presentation of what North America calls Hantavirus Pulmonary Syndrome (HPS).

The pathophysiology is brutal and fast:

• Days 1–5 (Febrile phase): Fever, myalgia, headache. Indistinguishable from influenza. PCR is positive. Most patients are not yet hospitalised.
• Days 5–8 (Cardiopulmonary phase): Capillary leak syndrome. Bilateral pulmonary oedema. Cardiogenic shock. Mechanical ventilation required in ~40% of ANDV cases.
• Days 8–14 (Convalescent phase): Survivors recover slowly. Case fatality rate for ANDV: 35–50%. For SNV: 36%. For Puumala (HFRS, not HPS): <0.5%.

The 2026 molecular data added a new wrinkle. Genomic sequencing of the index case from MV Hondius showed a 14-amino-acid N-terminus truncation in the nucleocapsid protein and a glycoprotein Gc shift at position G2. The Gc shift is particularly significant: glycoprotein Gc mediates fusion with endosomal membranes during cell entry. Altered receptor binding affinity could explain the aerosol transmission efficiency that makes ANDV unique.

A 2.3 Å cryo-EM map published in Nature Structural & Molecular Biology (March 2026) resolved the ANDV Gn/Gc prefusion complex at near-atomic resolution for the first time. The G2 shift observed in the 2026 cruise-ship strain maps directly onto a solvent-exposed loop on Gc that is not present in SNV or PUUV – a structural basis for the transmission difference that had been hypothesised for a decade.

The public health consequence: A passenger on MV Hondius with confirmed ANDV and a G2-shifted strain is not a rodent-excreta exposure case. It is a potential aerosol transmission event in a closed vessel with recirculated air. The contact list is not three named individuals – it is every passenger and crew member who shared HVAC zones.

3. Why Traditional Surveillance Fails Here

Standard outbreak surveillance assumes a single exposure event, a clear case definition, and linear contact tracing. The MV Hondius scenario violates all three:

A human analyst working through this manually would take 48–72 hours to produce a risk-stratified contact list. In an ANDV outbreak with exponential aerosol potential, 48 hours is the difference between containment and a public health emergency.

This is exactly the problem agentic AI should solve.

4. The outbreak-agent: Architecture and Design Decisions

Most outbreak surveillance tools are databases with search interfaces. You query them, they return data, a human decides what to do next. That loop takes 48–72 hours minimum.

outbreak-agent is different. It is an automated decision pipeline – you give it a case and it gives you a risk-stratified action, an audit trail, and a printed report. No human in the middle.

You feed it the facts: a patient, their age, where they were exposed, which ship, which cabin, who they sat near, how many days since symptoms started, lab values, a genome sequence if available. The agent runs four steps automatically:

• Step 1 — Identify the virus. The genomic_node determines the viral clade, flags mutations of concern, and assesses genome completeness. For MV Hondius: Andes virus, S-clade 2026, with a G2 glycoprotein shift that has direct structural implications for aerosol transmission.

• Step 2 — Map the contacts. The linkage_node resolves the full contact cluster – not just the people the patient named, but inferred contacts based on vessel layout, HVAC zones, and excursion groups. It also determines transmission mode: rodent-contact only, or human-to-human aerosol.

• Step 3 — Score the risk. The risk_node produces a composite score from 0 to 100 and assigns a tier: LOW / MEDIUM / HIGH / CRITICAL. For MV Hondius: 98/100, CRITICAL. Action: immediate isolation, notify MoH within 2 hours, activate IPC team, contact-trace all vessel passengers.

• Step 4 — Audit the output. This is where it becomes an agent rather than a script. The critic_node checks the entire output for internal contradictions before anything reaches a decision-maker. If it finds that a CRITICAL tier was assigned with a low-risk transmission mode, or that a confident clade was declared on a 40% complete genome, it raises a flag and the graph loops back to re-evaluate. Up to 3 times. Only a consistent, contradiction-free output gets approved.

The result: a 3-panel risk dashboard (PNG) and a structured A4 PDF triage report, generated automatically on every run. No API key. No cost. Under 2 seconds.

Why LangGraph and not just Python functions? LangGraph provides the graph engine – state management, node wiring, and the conditional edge that routes the critic’s flag back to the genomic node for re-evaluation. The 4 nodes themselves are rule-based and deterministic; that is what makes 33 tests run free and fast. The architecture is deliberately designed so that replacing any rule-based node with a real LLM call requires changing one function – not rewiring the graph. Deterministic and auditable today, upgradeable without structural changes tomorrow.

The full code is at github.com/ankurgenomics/outbreak-agent.

Graph Topology

START
  │
  ▼
┌─────────────────┐
│  genomic_node   │  Identifies clade & mutations from sequence or location heuristics
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  linkage_node   │  Resolves contact cluster, infers transmission mode
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   risk_node     │  Composite score 0–100 → tier (LOW / MEDIUM / HIGH / CRITICAL)
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  critic_node    │  Audits consistency → approved or loops back (max 3 iterations)
└────────┬────────┘
         │
    approved? ──YES──► END (final_report generated)
         │
        NO (critic_flags raised)
         │
         └────────────► genomic_node (re-evaluate with updated context)

Shared State – OutbreakState

For non-technical readers – what is a shared state?

Think of OutbreakState as the case file that travels with the patient through every step of a hospital admission. When you arrive at A&E, the triage nurse writes down your name, age, symptoms, and how long you have had them. The doctor reads that same file and adds test results. The specialist reads it again and adds a diagnosis. Nobody re-interviews you from scratch at each step – they read the file, add their findings, and pass it on.

OutbreakState works the same way. It is a single document that every node in the pipeline reads from and writes back to:

• The input fields are what the public health officer enters at the start: case ID, patient age, where the exposure happened, which ship, which cabin, who the contacts are, how many days since symptoms started, lab test values, and the genome sequence if available.
• The genomic_node fills in the virus clade (family lineage), any mutation flags, and how complete the genome sequence is.
• The linkage_node fills in the full contact cluster, how the virus is spreading, and how many people are at risk.
• The risk_node fills in the numerical risk score and the recommended action.
• The critic_node adds any consistency flags – or marks the case as approved.

No node ever re-runs the previous step’s work. No information is lost between steps. The public health officer sees the final, fully-populated case file – not a summary of summaries. That completeness is what makes the output auditable.

Every node reads from and writes to a single OutbreakState TypedDict:

class OutbreakState(TypedDict):
    # Input
    case_id:            str
    patient_age:        int
    exposure_location:  str
    vessel:             Optional[str]
    cabin:              Optional[str]
    contacts:           List[str]
    symptom_onset_days: int
    pcr_ct_value:       Optional[float]
    genome_sequence:    Optional[str]

    # genomic_node outputs
    clade:              Optional[str]          # e.g. "ANDV-S-clade-2026"
    mutation_flags:     Optional[List[str]]    # e.g. ["G2-glycoprotein-shift"]
    genome_completeness: Optional[float]

    # linkage_node outputs
    contact_cluster:    Optional[List[str]]
    transmission_mode:  Optional[str]          # "aerosol-human-to-human"
    cluster_size:       Optional[int]

    # risk_node outputs
    risk_score:         Optional[float]        # 0.0 – 100.0
    risk_tier:          Optional[str]          # "CRITICAL"
    recommended_action: Optional[str]

    # critic_node outputs
    critic_flags:       Optional[List[str]]    # empty = approved
    approved:           Optional[bool]
    final_report:       Optional[str]

The Critic Node – Why It Matters Most

For non-technical readers – what does the critic node actually do?

Imagine you are a senior epidemiologist reviewing a junior colleague’s risk assessment. You do not just accept their conclusion – you check whether the pieces are internally consistent. If they wrote “low risk” for a patient with confirmed aerosol transmission of a virus with 35-50% fatality rate, you would send it back. That is exactly what the critic node does – automatically, before any output reaches a decision-maker.

The critic enforces four specific consistency rules:

1. ANDV + aerosol + LOW/MEDIUM tier → flag: likely under-scoring
2. Genome completeness < 70% with confident clade → flag: re-sequence needed
3. Cluster size > 8 with no exposure anchor → flag: linkage may be unreliable
4. UNKNOWN clade + CRITICAL tier → flag: requires expert phylogenetic review

When any flag fires, the graph loops back to genomic_node for re-evaluation with updated context – up to 3 times before a forced exit. This is the self-correction loop that distinguishes an agent from a script.

For the MV Hondius case:

• genomic_node → ANDV-S-clade-2026, G2-glycoprotein-shift, completeness 87%
• linkage_node → aerosol-human-to-human, cluster 5 (3 declared + 2 adjacent cabins)
• risk_node → score 98/100, tier CRITICAL
• critic_node – no flags (ANDV + aerosol + CRITICAL is internally consistent) – Approved

Case: ANDV-2026-001
  Clade          : ANDV-S-clade-2026
  Mutations      : N-end-truncation-14aa, G2-glycoprotein-shift
  Genome quality : 87.0%
  Transmission   : aerosol-human-to-human
  Cluster size   : 5 contacts
  Risk score     : 98.0/100
  Risk tier      : CRITICAL
  Action         : Immediate isolation. Notify MoH within 2 hours.
                   Activate IPC team. Contact trace all vessel passengers.

  Approved by critic (loops: 1)

5. Testing Without an API Key – The Three-Layer Strategy

For non-technical readers – why does this matter?

Most AI tools you read about in the press require an API key to do anything meaningful. An API key means a credit card, a usage bill, and a dependency on a commercial service staying operational. If the service goes down, the tool breaks. If the provider changes pricing, the tool becomes expensive. This is not acceptable for public health tooling.

outbreak-agent is designed so that the core outbreak logic – the part that actually does the triage – runs entirely offline, on your laptop, for free, with no external dependencies. You can audit every decision the agent makes without paying anyone.

The three test layers reflect three different levels of trust:

Layer 1 – Pure Unit Tests (Free, always run)

pytest tests/test_nodes.py -v

Each node is tested in isolation with hardcoded OutbreakState inputs:

def test_andv_clade_detected_from_location(self):
    result = genomic_node(MOCK_CASE_HONDIUS)
    assert result["clade"] == "ANDV-S-clade-2026"

def test_hondius_is_critical(self):
    state = self._full_state(MOCK_CASE_HONDIUS)
    result = risk_node(state)
    assert result["risk_tier"] == "CRITICAL"

Layer 2 – Full Graph Integration (Free, always run)

pytest tests/test_graph.py -v

Runs all 4 nodes end-to-end through the LangGraph state machine against 4 mock cases. No external model API, no cost, deterministic output.

Layer 3 – Live Model Test (Manual, ~$0.01)

export OPENAI_API_KEY=sk-...
pytest tests/test_smoke.py -v -s --smoke

Only runs when explicitly invoked with --smoke. Verifies the real API integration without making it a CI blocker.

6. The Geopolitical Layer: Why Surveillance Gaps Are Predictable

The 2026 Patagonia situation did not emerge in a vacuum.

Argentina has maintained active hantavirus surveillance since the 1999 El Bolsón cluster – the first documented ANDV human-to-human transmission event. Their national network is functional but underfunded, with PCR confirmation turnaround averaging 6.2 days at provincial labs (vs. 18 hours at Buenos Aires reference labs).

Chile declared a national alert for ANDV in February 2026 following 14 confirmed cases in Aysén Region – the highest February count since records began. The country has two national reference labs and a reasonably well-integrated reporting system.

The surveillance gap is not in detection – it is in the 8 to 45 day incubation window. A tourist who spends 3 days in the Andes and boards a flight home on day 4 has a 40-day window during which they are infectious-but-asymptomatic and completely off every public health radar.

The cruise-ship scenario is the worst-case version of this gap: a closed environment with international passengers, rotating contact networks, recirculated air, and no mandatory health screening at embarkation.

An agentic triage system cannot fix the surveillance gap. But it can compress the time from symptom onset to risk-stratified action from 48–72 hours to under 2 hours. That is the value proposition.

7. What Comes Next

The outbreak-agent public code is a foundation. The roadmap I am working toward:

Near term (public)

• alert_node – auto-generates WHO-format outbreak notification draft
• report_node – complete. Produces a 3-panel matplotlib risk dashboard (PNG) and a structured A4 PDF triage report via ReportLab. Both generate automatically on every demo.py run. No API key. No cost. See the dashboard image embedded in the README.
• GitHub Actions CI – pytest tests/test_nodes.py tests/test_graph.py on every push

Medium term (private → eventually public)

• Real FASTA integration via the genomics_tools private module
• Phylogenetic placement using IQ-TREE2 with pre-computed ANDV reference panel
• Multi-agent version: separate genomics agent + epidemiology agent + policy agent, coordinated by a supervisor

Longer term

• Integration with GenomicsCopilot – the broader agentic genomics platform I am building
• Real-time rodent density data feeds from GBIF (Global Biodiversity Information Facility) to make the climate-risk scoring dynamic rather than heuristic

Try It Yourself

git clone https://github.com/ankurgenomics/outbreak-agent
cd outbreak-agent
pip install -r requirements.txt

# Run all mock cases (no API key needed)
python demo.py

# Run the MV Hondius case specifically
python demo.py --case hondius

# Run the free test suite
pytest tests/test_nodes.py tests/test_graph.py -v

The MV Hondius mock case should return CRITICAL in under a second. If it does not, open an issue – that is a bug.

References

1. Palma, R.E. et al. (2012). Ecology of rodent-associated Andes orthohantavirus in Chile. Emerging Infectious Diseases, 18(8): 1318–1322.
2. Ferres, M. et al. (2007). Prospective evaluation of household contacts of hantavirus cardiopulmonary syndrome patients in Chile. Journal of Infectious Diseases, 195(11): 1563–1571.
3. Mittler, E. et al. (2026). Near-atomic resolution structure of the Andes virus Gn/Gc prefusion complex. Nature Structural & Molecular Biology, 33: 412–421.
4. WHO. (2026). Hantavirus disease – Argentina and Chile situation report, February 2026.
5. LangGraph documentation. (2026). Stateful graph agents with conditional edges. LangChain, Inc.

Ankur Sharma, PhD is a computational biologist and agentic AI engineer based in Singapore. He builds systems that connect genomic data to real-world decisions – in outbreak surveillance, precision diagnostics, and autonomous research pipelines.

LinkedIn | GitHub | Portfolio | outbreak-agent repo

Copyright 2026 Ankur Sharma, PhD. Licensed under CC BY-NC 4.0. Free to share with attribution. Not for commercial use without permission.

Code in the linked repository is separately licensed under Apache 2.0.