# CDC WONDER & FluView — Public Health Data API (`nexgendata/cdc-wonder-fluview-public-health-surveillance`) Actor Weekly hospitalizations + deaths for influenza, COVID-19, and RSV by US state and age group, normalized against the 5-year MMWR-week seasonal baseline. Pulled directly from CDC NHSN + NCHS provisional deaths + WONDER. Public health, pharma surveillance, insurance actuarial, hospital o - **URL**: https://apify.com/nexgendata/cdc-wonder-fluview-public-health-surveillance.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $80.00 / 1,000 disease-week records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## CDC WONDER & FluView CDC WONDER and FluView public-health surveillance records — disease counts by week, state and age group. > Public regulatory/health data — not medical advice. ### ⏰ Run this as a monitoring subscription (recommended) Regulatory data is monitoring data. Schedule a weekly sweep (`0 8 * * 1`) and catch new records as they post — an always-on official-records feed. ### 📊 What you get Clean JSON, one record per record (full output has 18 fields): - `disease` — Disease - `week` — Week - `year` — Year - `week_ending_date` — Week ending date - `state` — State - `state_code` — State code - `age_group` — Age group - `case_count` — Case count - `hospitalization_count` — Hospitalization count - `hospitalization_rate_per_100k` — Hospitalization rate per 100k **Pricing:** Pay-Per-Event; see the Pricing tab. ### 🤖 Use with AI agents Point Claude, the OpenAI Agents SDK, an n8n flow or any MCP-aware client at it and pull official records on demand. **Agentic payments (x402):** Supports agentic payment via x402 — agents can call this actor with USDC, no API key required. ### 🔗 Related regulatory actors **FDA & public-health records family:** [OpenFDA Drug](https://apify.com/nexgendata/openfda-drug-search) · [Drug Approvals](https://apify.com/nexgendata/fda-drug-approvals) · [Drug Shortages](https://apify.com/nexgendata/fda-drug-shortages-monitor) · [Purple Book](https://apify.com/nexgendata/fda-purple-book-biologics-biosimilars) · [Warning Letters/483](https://apify.com/nexgendata/fda-warning-letters-483-inspections) · [Pharma Pipeline](https://apify.com/nexgendata/fda-pharma-catalyst-pipeline) · [Adverse Events](https://apify.com/nexgendata/fda-adverse-events-faers-maude) · [Medical Devices](https://apify.com/nexgendata/medical-device-fda-intelligence) **Product-safety recalls:** [NHTSA Recalls](https://apify.com/nexgendata/nhtsa-us-vehicle-recalls) · [NHTSA Defects](https://apify.com/nexgendata/nhtsa-vehicle-recall-defect-tracker) · [NHTSA Safety](https://apify.com/nexgendata/nhtsa-vehicle-safety-intelligence) · [CPSC Recalls](https://apify.com/nexgendata/cpsc-product-recalls) **Agent front door — Healthcare & FDA MCP:** [Healthcare & FDA MCP](https://apify.com/nexgendata/healthcare-fda-intelligence-mcp) --- *Public official records and enforcement data, assembled for research and monitoring.* # Actor input Schema ## `disease` (type: `string`): Which respiratory disease to track. `influenza` = seasonal flu, `covid-19` = SARS-CoV-2, `rsv` = respiratory syncytial virus, `any` = all three in one run. Death counts are available for influenza and COVID-19; RSV deaths are reported as null (CDC does not publish weekly RSV death counts). ## `state_filter` (type: `string`): Comma-separated list of US state codes (e.g. `CA,NY,TX`) or full state names. Use `USA` for the national aggregate. Leave empty to pull every state + DC + PR + USA aggregate. Accepts both formats (`CA` or `California`). ## `age_group` (type: `string`): Age stratum. The NHSN weekly admissions feed publishes one aggregate per jurisdiction/week — this actor apportions to the requested age bucket using the CDC FluView surveillance age distribution (`0-4`, `5-17`, `18-49`, `50-64`, `65+`). Use `all` for the unsplit aggregate. ## `weeks_back` (type: `integer`): Look-back window in MMWR weeks. NHSN HRD updates every Friday. 52 weeks = one season; 104 weeks = two seasons (recommended for seasonal-baseline comparisons); 260 weeks = full 5 years (max). ## `compare_seasonal_baseline` (type: `boolean`): If true, the actor fetches the 5-year NHSN history and emits `vs_5yr_seasonal_avg_pct` per record — the percent deviation from the same MMWR-week's 5-year average. This is the headline analyst-time-saver: instantly see which state/week is `+200%` vs trend. ## `max_records` (type: `integer`): Hard cap on emitted records. A typical full-USA flu season run = ~52 weeks × 52 states × 1 disease ≈ 2,704 raw rows; the actor caps at 2000. Set to 5 for a smoke test, 200-2000 for analyst runs. ## Actor input object example ```json { "disease": "influenza", "state_filter": "", "age_group": "all", "weeks_back": 52, "compare_seasonal_baseline": true, "max_records": 200 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "disease": "influenza", "state_filter": "", "age_group": "all", "weeks_back": 52, "compare_seasonal_baseline": true, "max_records": 200 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/cdc-wonder-fluview-public-health-surveillance").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "disease": "influenza", "state_filter": "", "age_group": "all", "weeks_back": 52, "compare_seasonal_baseline": True, "max_records": 200, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/cdc-wonder-fluview-public-health-surveillance").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "disease": "influenza", "state_filter": "", "age_group": "all", "weeks_back": 52, "compare_seasonal_baseline": true, "max_records": 200 }' | apify call nexgendata/cdc-wonder-fluview-public-health-surveillance --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/cdc-wonder-fluview-public-health-surveillance", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CDC WONDER & FluView — Public Health Data API", "description": "Weekly hospitalizations + deaths for influenza, COVID-19, and RSV by US state and age group, normalized against the 5-year MMWR-week seasonal baseline. Pulled directly from CDC NHSN + NCHS provisional deaths + WONDER. Public health, pharma surveillance, insurance actuarial, hospital o", "version": "0.0", "x-build-id": "C4JmULtUMIxaXI1D9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~cdc-wonder-fluview-public-health-surveillance/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-cdc-wonder-fluview-public-health-surveillance", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~cdc-wonder-fluview-public-health-surveillance/runs": { "post": { "operationId": "runs-sync-nexgendata-cdc-wonder-fluview-public-health-surveillance", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~cdc-wonder-fluview-public-health-surveillance/run-sync": { "post": { "operationId": "run-sync-nexgendata-cdc-wonder-fluview-public-health-surveillance", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "disease": { "title": "Disease", "enum": [ "influenza", "covid-19", "rsv", "any" ], "type": "string", "description": "Which respiratory disease to track. `influenza` = seasonal flu, `covid-19` = SARS-CoV-2, `rsv` = respiratory syncytial virus, `any` = all three in one run. Death counts are available for influenza and COVID-19; RSV deaths are reported as null (CDC does not publish weekly RSV death counts).", "default": "influenza" }, "state_filter": { "title": "State filter (optional)", "type": "string", "description": "Comma-separated list of US state codes (e.g. `CA,NY,TX`) or full state names. Use `USA` for the national aggregate. Leave empty to pull every state + DC + PR + USA aggregate. Accepts both formats (`CA` or `California`).", "default": "" }, "age_group": { "title": "Age group", "enum": [ "0-4", "5-17", "18-49", "50-64", "65+", "all" ], "type": "string", "description": "Age stratum. The NHSN weekly admissions feed publishes one aggregate per jurisdiction/week — this actor apportions to the requested age bucket using the CDC FluView surveillance age distribution (`0-4`, `5-17`, `18-49`, `50-64`, `65+`). Use `all` for the unsplit aggregate.", "default": "all" }, "weeks_back": { "title": "Weeks back from today", "minimum": 1, "maximum": 260, "type": "integer", "description": "Look-back window in MMWR weeks. NHSN HRD updates every Friday. 52 weeks = one season; 104 weeks = two seasons (recommended for seasonal-baseline comparisons); 260 weeks = full 5 years (max).", "default": 52 }, "compare_seasonal_baseline": { "title": "Compare to 5-year seasonal baseline", "type": "boolean", "description": "If true, the actor fetches the 5-year NHSN history and emits `vs_5yr_seasonal_avg_pct` per record — the percent deviation from the same MMWR-week's 5-year average. This is the headline analyst-time-saver: instantly see which state/week is `+200%` vs trend.", "default": true }, "max_records": { "title": "Max records", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Hard cap on emitted records. A typical full-USA flu season run = ~52 weeks × 52 states × 1 disease ≈ 2,704 raw rows; the actor caps at 2000. Set to 5 for a smoke test, 200-2000 for analyst runs.", "default": 200 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```