What data sources does Pandemic Biosurveillance use?
It retrieves data in parallel from 16 sources, including WHO Global Health Observatory, PubMed, IUCN Red List, GBIF, GDACS, NOAA, OpenAQ, FEMA, and more.
@apifyforge
Basic information
Pandemic biosurveillance intelligence for AI agents — 8 mathematically rigorous epidemic modeling tools backed by live data from 16 public health, ecological, and environmental sources.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"pandemic-biosurveillance-mcp": {
"url": "https://ryanclinton--pandemic-biosurveillance-mcp.apify.actor/mcp"
}
}
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Pandemic Biosurveillance is an MCP server providing eight epidemic modeling tools backed by live data from 16 public health, ecological, and environmental sources. It runs on the Apify platform and is designed for AI agents to perform outbreak simulation, parameter inference, variant forecasting, and spillover risk mapping with structured, machine-readable results.
Add the server URL to any MCP-compatible client (e.g., Claude Desktop, Cursor, Windsurf) using the configuration shown in the Quick Start. Each tool accepts a single natural-language query and returns results in 2–4 minutes. No API key is required; billing is handled through your Apify account.
It retrieves data in parallel from 16 sources, including WHO Global Health Observatory, PubMed, IUCN Red List, GBIF, GDACS, NOAA, OpenAQ, FEMA, and more.
Billing is pay-per-tool-call: USD 0.030 to 0.040 per call depending on the algorithm selected, with no subscription or minimum commitment.
No, the server URL does not require an API key; all billing is handled through your Apify account.
The server offers eight tools: simulate_epidemic_metapopulation, infer_parameters_pmcmc, estimate_phylodynamic_re, evaluate_intervention_causality, compute_vaccination_equilibrium, forecast_variant_fitness, assess_zoonotic_spillover, and model_seasonal_waning_dynamics.
Each tool call fetches data from all 16 sources in parallel, runs the mathematical model, and returns results in 2–4 minutes.

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