Pandemic Biosurveillance MCP

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Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "pandemic-biosurveillance-mcp": {
      "url": "https://ryanclinton--pandemic-biosurveillance-mcp.apify.actor/mcp"
    }
  }
}

---

Pandemic biosurveillance intelligence for AI agents — 8 mathematically rigorous epidemic modeling tools backed by live data from 16 public health, ecological, and environmental sources. Connect any MCP-compatible AI client to run stochastic outbreak simulations, infer transmission parameters, estimate effective reproduction numbers, and map zoonotic spillover hotspots in a single tool call.

This MCP server runs on the Apify platform in persistent Standby mode. Each tool call triggers parallel data retrieval from WHO, PubMed, IUCN, GBIF, GDACS, NOAA, FEMA, and 9 more sources, constructs a multi-domain epidemic network, and applies a dedicated mathematical algorithm — Gillespie SSA, particle MCMC, birth-death skyline phylodynamics, augmented synthetic control, mean-field game theory, multinomial logistic regression, MaxEnt species distribution modeling, or Fourier seasonal forcing — returning structured, machine-readable results with uncertainty quantification.

⬇️ What data can you access?

| Data Point | Source | Example |
|---|---|---|
| 📊 Global disease burden and mortality rates | WHO Global Health Observatory | Influenza IFR by region |
| 🧪 Active vaccine and therapeutic trials | ClinicalTrials.gov | Phase 3 mRNA vaccine enrollment |
| 💊 Adverse drug events and safety signals | openFDA FAERS | VAERS signal detection |
| 🇪🇺 European authorized medicines | EMA Medicines Database | Antiviral market authorizations |
| 📄 Biomedical literature and citations | PubMed, OpenAlex, Europe PMC | SARS-CoV-2 transmission papers |
| 🦇 Species distribution and conservation status | IUCN Red List | Rhinolophus bat range maps |
| 🌍 Biodiversity occurrence data | GBIF | Wildlife host occurrence records |
| 🌪️ Natural disaster alerts and impact estimates | GDACS | Flood-disrupted health infrastructure |
| 🌡️ Weather and climate data | NOAA | Temperature-driven seasonality |
| 💨 Air quality and environmental health proxies | OpenAQ | Pollution co-exposure risk |
| 🚨 Disaster declarations and emergency management | FEMA | US outbreak response capacity |
| 🌐 Country demographics and population | REST Countries, World Bank | Population mobility denominators |
| 📍 Geolocation and spatial reference | Nominatim | Hotspot coordinate resolution |

Why use Pandemic Biosurveillance MCP?

Pandemic preparedness work requires integrating epidemiological data, ecological surveillance, clinical trial pipelines, environmental drivers, and geospatial context into a single coherent analysis. Doing this manually across 16 databases takes days and demands specialist knowledge in epidemiological modeling, spatial statistics, and phylogenetics. Even dedicated tools like Nextstrain or the WHO FluMart system each cover only one dimension of the problem.

This MCP server automates the entire intelligence gathering and modeling pipeline. A single tool call fetches and synthesizes data from all 16 sources, builds a multi-domain epidemic network, and runs the appropriate mathematical model — returning actionable results in under three minutes.

- Standby mode — the server stays warm between calls, eliminating cold-start latency on every tool invocation
- Parallel data retrieval — 16 actors run concurrently per tool call, not sequentially, keeping latency low
- API access — trigger analyses from Python, JavaScript, or any HTTP client via the Apify API
- Scheduling — run recurring surveillance passes with Apify's built-in scheduler
- Monitoring — receive Slack or email alerts when model outputs breach defined thresholds via webhooks
- Integrations — connect results to Zapier, Make, or custom pipelines for downstream alerting

Features

- Gillespie SSA metapopulation simulation — stochastic SEIR-HCD model with 7 compartments (S, E, I, R, H, C, D) across multiple populations; tau-leaping approximation enables simulation across 20+ populations simultaneously
- Radiation mobility network — inter-population transmission follows Simini et al. (2012): T_ij = T_i(m_i·n_j)/((m_i+s_ij)(m_i+n_j+s_ij)), parameterized directly from World Bank population data
- Particle MCMC parameter inference — alive particle filter with Sequential Monte Carlo resampling for unbiased likelihood estimation; infers R0, latent period, IFR, and hospitalization rate with Gelman-Rubin R-hat convergence diagnostics
- Birth-death skyline phylodynamics — estimates Re trajectory using piecewise-constant birth rates (Stadler et al. 2013); cross-validated via Bayesian model averaging with thermodynamic integration weights
- Augmented synthetic control — causal NPI and pharmaceutical intervention effects using Abadie (2021) synthetic counterfactual construction; conformal prediction intervals for valid uncertainty quantification
- Mean-field game vaccination equilibrium — coupled HJB backward PDE and Fokker-Planck forward PDE to find Nash equilibrium vs social optimum; quantifies the free-rider gap and produces age-group prioritization strategies
- Multinomial logistic variant fitness — estimates selection coefficients and fitness advantages from variant frequency trajectories; Maynard-Smith fitness landscape ruggedness score and escape mutation risk
- MaxEnt zoonotic spillover modeling — P(presence|env) = exp(sum_k lambda_k·f_k(x)) / Z; combines with deforestation frontier KDE for human-wildlife interface hotspot identification; overall spillover probability P(spillover) = P(host) × P(contact) × P(adaptation)
- Fourier seasonal forcing with power-law waning — R_e(t) = R0·(1+alpha·cos(2pi·t/365-phi)) combined with Khoury et al. (2021) power-law antibody waning: Ab(t) = Ab0·t^(-kappa); computes optimal booster timing
- 16 parallel data sources — health (4), research (3), ecological (2), environmental (4), spatial (3); all run concurrently via the Apify actor client
- Multi-domain epidemic network graph — nodes typed as disease, trial, paper, country, species, drug, environment, or location; edges typed as transmits, treats, studies, hosts, mobility, or correlates
- Pay-per-tool-call pricing — no subscription, no minimum commitment; $0.030–$0.040 per tool call depending on the algorithm

Use cases for pandemic biosurveillance intelligence

Pandemic preparedness scenario planning

Government health agencies, defense research labs, and biosecurity think tanks need to stress-test preparedness plans against realistic outbreak scenarios before an event occurs. Use simulate_epidemic_metapopulation with a query like "H5N1 avian influenza pandemic" to generate stochastic spread trajectories across connected populations, estimate peak hospitalization timing, and compute herd immunity thresholds. Combine with assess_zoonotic_spillover to identify the geographic interfaces most likely to generate the index case.

Outbreak response and resource allocation

During an active outbreak, public health operations teams need real-time estimates of whether transmission is growing or declining and how effective current interventions are. Use estimate_phylodynamic_re to track the Re trajectory from epidemiological signals and evaluate_intervention_causality to quantify the causal effect of NPIs already in place. The augmented synthetic control counterfactual provides lives-saved estimates with proper uncertainty bounds, directly suitable for policy briefings.

Vaccination strategy and campaign design

Immunization program managers designing rollout strategies need to understand both optimal allocation and the behavioral incentive landscape. Use compute_vaccination_equilibrium to identify the free-rider gap between Nash equilibrium coverage and the social optimum, and to generate age-group prioritization ranked by cost-effectiveness. Use model_seasonal_waning_dynamics to determine optimal booster timing given the local seasonal forcing amplitude and antibody half-life estimates.

Variant surveillance and genomic monitoring

Virology and genomic surveillance teams tracking an evolving pathogen need to forecast which variants will dominate the next transmission wave. Use forecast_variant_fitness with a query like "SARS-CoV-2 variants 2025" to estimate selection coefficients, project 30-day and 90-day frequency trajectories, and identify variants with high immune evasion scores. Fitness landscape ruggedness scores indicate how rapidly the variant space is evolving.

Zoonotic disease research and spillover risk mapping

Researchers at the wildlife-human interface — ecology institutes, biosafety labs, One Health programs — need to identify where the next spillover is most likely to occur. Use assess_zoonotic_spillover to run MaxEnt species distribution modeling for reservoir hosts, combine with deforestation frontier KDE, and produce a ranked list of geographic hotspots with per-region risk scores. The output includes pathogen family, deforestation driver score, and human contact rate for each species.

Academic epidemiology and public health research

Epidemiologists and graduate researchers need reproducible, citable methodologies for modeling studies. Each tool in this MCP implements a published mathematical framework with proper convergence diagnostics and uncertainty quantification, making outputs suitable as model inputs or benchmarks. infer_parameters_pmcmc provides posterior distributions over epidemic parameters with 95% credible intervals and effective sample size, ready for inclusion in methods sections.

How to use pandemic biosurveillance MCP tools

1. Connect your MCP client — add the server URL https://pandemic-biosurveillance-mcp.apify.actor/mcp to Claude Desktop, Cursor, Windsurf, Cline, or any MCP-compatible client. No API key is needed for the URL itself; billing is handled through your Apify account.
2. Choose the right tool — select from 8 tools based on your analysis goal: simulation, parameter inference, Re estimation, intervention causality, vaccination strategy, variant forecasting, spillover risk, or seasonal dynamics.
3. Enter a natural-language query — type the disease, pathogen, variant, or geographic context you want to analyze. Examples: "H5N1 avian influenza pandemic", "bat coronavirus spillover Southeast Asia", "SARS-CoV-2 Omicron booster timing".
4. Receive structured results — the server fetches live data from 16 sources in parallel, runs the mathematical model, and returns JSON-structured output with model estimates, uncertainty intervals, and supporting network metadata within 2–4 minutes.

Input parameters

This MCP server has no traditional actor input schema — it operates in Standby mode and receives all inputs via the MCP protocol. Each tool accepts a single parameter:

| Parameter | Type | Required | Description |
|---|---|---|---|
| query | string | Yes | Disease, pathogen, outbreak, or geographic context to analyze. Natural language accepted. |

Tool-level input reference

| Tool | query example |
|---|---|
| simulate_epidemic_metapopulation | "H5N1 avian influenza pandemic" |
| infer_parameters_pmcmc | "mpox outbreak West Africa" |
| estimate_phylodynamic_re | "SARS-CoV-2 JN.1 variant" |
| evaluate_intervention_causality | "COVID-19 lockdown effectiveness" |
| compute_vaccination_equilibrium | "influenza seasonal vaccination" |
| forecast_variant_fitness | "influenza H3N2 variants 2025" |
| assess_zoonotic_spillover | "bat coronavirus spillover Southeast Asia" |
| model_seasonal_waning_dynamics | "RSV seasonal dynamics booster timing" |

Input tips

- Be specific about geography — adding a region narrows the ecological and spatial data returned, improving model accuracy for localized analyses.
- Include the pathogen family — queries like "bat coronavirus" vs "coronavirus" produce different IUCN and GBIF species retrieval, affecting zoonotic spillover results.
- Use variant-level queries for genomic tools — "SARS-CoV-2 JN.1 variant" returns more targeted literature and drug data than a generic query.
- Chain tools deliberately — run simulate_epidemic_metapopulation first to understand population-level dynamics, then use evaluate_intervention_causality to assess the effect of specific measures on the same outbreak context.
- Set a spending limit — configure a maximum spend per run in your Apify account settings to prevent unexpected costs during iterative analysis sessions.

Output example

simulate_epidemic_metapopulation response for "H5N1 avian influenza pandemic":

{
  "networkNodes": 142,
  "networkEdges": 318,
  "trajectories": [
    {
      "time": 0,
      "population": "China",
      "S": 1402000000,
      "E": 0,
      "I": 50,
      "R": 0,
      "H": 0,
      "C": 0,
      "D": 0,
      "Re": 2.1
    },
    {
      "time": 14,
      "population": "China",
      "S": 1401820000,
      "E": 42300,
      "I": 87400,
      "R": 12800,
      "H": 8200,
      "C": 1400,
      "D": 280,
      "Re": 1.87
    },
    {
      "time": 60,
      "population": "India",
      "S": 1380000000,
      "E": 198000,
      "I": 341000,
      "R": 94000,
      "H": 28700,
      "C": 5100,
      "D": 1820,
      "Re": 1.43
    }
  ],
  "peakInfections": 4820000,
  "peakDate": 84,
  "totalInfected": 18400000,
  "totalDeaths": 621000,
  "herdImmunityThreshold": 0.524,
  "mobilityImpact": 0.38,
  "populationsAffected": 22
}

infer_parameters_pmcmc response:

{
  "networkNodes": 138,
  "parameters": [
    {
      "parameter": "R0",
      "mean": 2.34,
      "ci95Lower": 1.91,
      "ci95Upper": 2.88,
      "effectiveSampleSize": 847
    },
    {
      "parameter": "latentPeriod",
      "mean": 3.2,
      "ci95Lower": 2.4,
      "ci95Upper": 4.1,
      "effectiveSampleSize": 912
    },
    {
      "parameter": "IFR",
      "mean": 0.0038,
      "ci95Lower": 0.0021,
      "ci95Upper": 0.0061,
      "effectiveSampleSize": 734
    },
    {
      "parameter": "hospitalizationRate",
      "mean": 0.041,
      "ci95Lower": 0.028,
      "ci95Upper": 0.057,
      "effectiveSampleSize": 801
    }
  ],
  "logLikelihood": -1847.3,
  "dic": 3714.8,
  "particlesUsed": 500,
  "acceptanceRate": 0.24,
  "convergenceDiagnostic": 1.03
}

Output fields

simulate_epidemic_metapopulation

| Field | Type | Description |
|---|---|---|
| networkNodes | number | Total nodes in the epidemic network (diseases, trials, countries, species, etc.) |
| networkEdges | number | Total edges (mobility, transmits, treats, hosts, studies, correlates) |
| trajectories[] | array | Time-series SEIR-HCD state per population per time step |
| trajectories[].time | number | Simulation day |
| trajectories[].population | string | Population name (country or region) |
| trajectories[].S | number | Susceptible count |
| trajectories[].E | number | Exposed count |
| trajectories[].I | number | Infectious count |
| trajectories[].R | number | Recovered count |
| trajectories[].H | number | Hospitalized count |
| trajectories[].C | number | Critical/ICU count |
| trajectories[].D | number | Deaths |
| trajectories[].Re | number | Effective reproduction number at this time step |
| peakInfections | number | Maximum concurrent infections across all populations |
| peakDate | number | Day of peak infections |
| totalInfected | number | Cumulative infections |
| totalDeaths | number | Cumulative deaths |
| herdImmunityThreshold | number | Required immune fraction to halt spread (0–1) |
| mobilityImpact | number | Estimated proportion of spread attributable to inter-population mobility |
| populationsAffected | number | Number of distinct populations with active transmission |

infer_parameters_pmcmc

| Field | Type | Description |
|---|---|---|
| parameters[] | array | Posterior estimates for each epidemic parameter |
| parameters[].parameter | string | Parameter name (R0, latentPeriod, IFR, hospitalizationRate) |
| parameters[].mean | number | Posterior mean |
| parameters[].ci95Lower | number | 2.5th percentile of posterior |
| parameters[].ci95Upper | number | 97.5th percentile of posterior |
| parameters[].effectiveSampleSize | number | ESS from SMC; values above 400 indicate reliable estimates |
| logLikelihood | number | Log marginal likelihood |
| dic | number | Deviance Information Criterion for model comparison |
| particlesUsed | number | Number of SMC particles |
| acceptanceRate | number | Metropolis-Hastings acceptance rate (0.15–0.35 is healthy) |
| convergenceDiagnostic | number | Gelman-Rubin R-hat; values below 1.05 indicate convergence |

estimate_phylodynamic_re

| Field | Type | Description |
|---|---|---|
| intervals[] | array | Piecewise Re estimates across skyline intervals |
| intervals[].startTime | number | Interval start (days before present) |
| intervals[].endTime | number | Interval end |
| intervals[].Re | number | Mean Re estimate for this interval |
| intervals[].ci95Lower | number | Lower credible bound |
| intervals[].ci95Upper | number | Upper credible bound |
| intervals[].growthRate | number | Exponential growth rate for this interval |
| currentRe | number | Most recent Re estimate |
| treeHeight | number | Inferred phylogenetic tree height in days |
| tmrca | number | Time to most recent common ancestor |
| skylinePopSize[] | number[] | Effective population size over skyline intervals |
| modelAvgWeights[] | array | Bayesian model averaging weights for each sub-model |
| doublingTime | number | Doubling time in days given current Re |

evaluate_intervention_causality

| Field | Type | Description |
|---|---|---|
| effects[] | array | Causal effect estimate per intervention type |
| effects[].intervention | string | Intervention name (e.g., lockdown, mask mandate, vaccination) |
| effects[].causalEffect | number | Estimated reduction in transmission (negative = beneficial) |
| effects[].ci95Lower | number | Conformal prediction lower bound |
| effects[].ci95Upper | number | Conformal prediction upper bound |
| effects[].conformalCoverage | number | Empirical coverage of prediction interval |
| effects[].syntheticControlFit | number | Pre-intervention fit quality (0–1; above 0.85 is good) |
| overallReduction | number | Combined transmission reduction from all interventions |
| bestIntervention | string | Intervention with largest causal effect |
| counterfactualDeaths | number | Projected deaths without any intervention |
| livesSaved | number | Estimated lives saved by current interventions |

compute_vaccination_equilibrium

| Field | Type | Description |
|---|---|---|
| equilibrium.optimalCoverage | number | Socially optimal vaccination coverage (0–1) |
| equilibrium.nashEquilibrium | number | Individual rational Nash equilibrium coverage |
| equilibrium.freeRiderGap | number | Gap between social optimum and Nash equilibrium |
| equilibrium.criticalThreshold | number | Herd immunity threshold |
| equilibrium.costEffectiveness | number | Cost per QALY averted at optimal coverage |
| ageGroupStrategy[] | array | Per-age-group coverage and priority rank |
| supplyConstraints[] | array | Resource availability vs need per supply category |
| welfareGain | number | Population welfare gain from reaching social optimum |

forecast_variant_fitness

| Field | Type | Description |
|---|---|---|
| variants[] | array | Forecast per identified variant |
| variants[].variant | string | Variant identifier |
| variants[].currentFrequency | number | Current proportion of sequences (0–1) |
| variants[].fitnessAdvantage | number | Selection coefficient vs reference strain |
| variants[].projectedFrequency30d | number | Projected frequency in 30 days |
| variants[].projectedFrequency90d | number | Projected frequency in 90 days |
| variants[].immuneEvasion | number | Immune evasion score (0–1) |
| variants[].transmissibility | number | Relative transmissibility vs reference |
| dominantVariant | string | Variant projected to dominate at 90 days |
| sweepTimeline | number | Days until dominant variant exceeds 80% frequency |
| landscapeRuggedness | number | Maynard-Smith fitness landscape ruggedness |
| escapeMutationRisk | number | Probability of immune escape mutation emerging |

assess_zoonotic_spillover

| Field | Type | Description |
|---|---|---|
| risks[] | array | Spillover risk assessment per host species |
| risks[].species | string | Reservoir host species name |
| risks[].pathogenFamily | string | Associated pathogen family |
| risks[].spilloverProbability | number | Combined spillover probability (0–1) |
| risks[].deforestationDriver | number | Deforestation pressure score at range boundary |
| risks[].humanContactRate | number | Estimated human-animal contact frequency |
| risks[].habitatOverlap | number | Overlap between human and host distributions |
| hotspots[] | array | Geographic spillover hotspots with coordinates |
| hotspots[].region | string | Region or administrative area name |
| hotspots[].lat | number | Latitude of hotspot centroid |
| hotspots[].lon | number | Longitude of hotspot centroid |
| hotspots[].riskScore | number | Composite spillover risk score (0–100) |
| overallSpilloverRate | number | Aggregate annual spillover rate estimate |
| highRiskInterfaces | string[] | Named high-risk human-wildlife interface zones |
| maxEntPrediction | number | MaxEnt model predicted occurrence probability |

model_seasonal_waning_dynamics

| Field | Type | Description |
|---|---|---|
| dynamics[] | array | Monthly seasonal dynamics across a 12-month cycle |
| dynamics[].month | number | Month (1–12) |
| dynamics[].Re | number | Seasonally-adjusted effective reproduction number |
| dynamics[].immunityLevel | number | Population immunity fraction at this month |
| dynamics[].waningRate | number | Monthly immunity waning rate |
| dynamics[].boosterNeed | number | Proportion of population needing boosting |
| seasonalAmplitude | number | Fourier forcing amplitude (alpha) |
| peakMonth | number | Month of maximum transmission |
| troughMonth | number | Month of minimum transmission |
| antibodyHalfLife | number | Estimated antibody half-life in days |
| waningPowerLawExponent | number | Khoury et al. power-law exponent kappa |
| optimalBoosterTiming | number | Recommended month for booster campaign |

How much does it cost to run pandemic biosurveillance analyses?

This MCP server uses pay-per-event pricing — you pay per tool call. Compute costs are included. Prices range from $0.030 to $0.040 per call depending on the algorithm.

| Tool | Price per call | 10 calls | 50 calls |
|---|---|---|---|
| simulate_epidemic_metapopulation | $0.040 | $0.40 | $2.00 |
| infer_parameters_pmcmc | $0.035 | $0.35 | $1.75 |
| estimate_phylodynamic_re | $0.035 | $0.35 | $1.75 |
| evaluate_intervention_causality | $0.030 | $0.30 | $1.50 |
| compute_vaccination_equilibrium | $0.035 | $0.35 | $1.75 |
| forecast_variant_fitness | $0.030 | $0.30 | $1.50 |
| assess_zoonotic_spillover | $0.030 | $0.30 | $1.50 |
| model_seasonal_waning_dynamics | $0.030 | $0.30 | $1.50 |

Running the full suite of 8 tools for a single outbreak analysis costs $0.265. A weekly surveillance run across 4 pathogens using 3 tools each costs approximately $1.56/week.

Apify's free tier includes $5 of monthly platform credits, which covers approximately 150 tool calls before any payment is required. You can set a maximum spending limit per run in your Apify account settings to cap costs.

Compare this to commercial epidemiological intelligence platforms charging $500–2,000/month for comparable data access, with no programmatic API and no mathematical modeling layer.

How to connect this MCP server

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "pandemic-biosurveillance": {
      "url": "https://pandemic-biosurveillance-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Cursor / Windsurf / Cline

Use the same URL in your client's MCP server settings: https://pandemic-biosurveillance-mcp.apify.actor/mcp

Python (direct HTTP)

import httpx
import json
response = httpx.post(

    "https://pandemic-biosurveillance-mcp.apify.actor/mcp",

    headers={

        "Content-Type": "application/json",

        "Authorization": "Bearer YOUR_APIFY_TOKEN",

    },

    json={

        "jsonrpc": "2.0",

        "method": "tools/call",

        "params": {

            "name": "simulate_epidemic_metapopulation",

            "arguments": {"query": "H5N1 avian influenza pandemic Southeast Asia"}

        },

        "id": 1

    },

    timeout=300,

)
result = response.json()

data = json.loads(result["result"]["content"][0]["text"])

print(f"Peak infections: {data['peakInfections']:,}")

print(f"Herd immunity threshold: {data['herdImmunityThreshold']:.1%}")

print(f"Populations affected: {data['populationsAffected']}")

JavaScript

const response = await fetch("https://pandemic-biosurveillance-mcp.apify.actor/mcp", {
    method: "POST",
    headers: {
        "Content-Type": "application/json",
        "Authorization": Bearer ${process.env.APIFY_TOKEN},
    },
    body: JSON.stringify({
        jsonrpc: "2.0",
        method: "tools/call",
        params: {
            name: "forecast_variant_fitness",
            arguments: { query: "influenza H3N2 variants 2025" }
        },
        id: 1
    }),
});
const result = await response.json();

const data = JSON.parse(result.result.content[0].text);

console.log(Dominant variant: ${data.dominantVariant});

console.log(Sweep timeline: ${data.sweepTimeline} days);

console.log(Escape mutation risk: ${(data.escapeMutationRisk * 100).toFixed(1)}%);

cURL

```bash