We extract epidemiological datasets, clinical guidelines, MMWR publications, and outbreak trackers from cdc.gov. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Outbreak Data objects from cdc.gov. All fields typed and schema-versioned.
"disease_name": "Measles", "jurisdiction": "Illinois", "cases_confirmed": 14, "cases_probable": 2, "deaths_total": 0, "epi_week": 12, "case_rate_per_100k": 0.11, "report_date": "2026-03-24"
| # | disease_name | jurisdiction | cases_confirmed | cases_probable | deaths_total | report_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Travel Advisories objects from cdc.gov. All fields typed and schema-versioned.
"destination_country": "Brazil", "notice_level": "Level 2", "disease_focus": "Dengue", "date_issued": "2026-02-15", "vaccine_recommendations": "Dengue vaccine recommended for eligible populations.", "alert_status_active": true
| # | destination_country | notice_level | disease_focus | date_issued | summary_text | clinical_guidance |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for MMWR Reports objects from cdc.gov. All fields typed and schema-versioned.
"report_id": "mm7314a1", "title": "Tuberculosis Cases in the United States", "publication_date": "2026-04-04", "volume_number": 73, "issue_number": 14, "pdf_download_url": "https://cdc.gov/mmwr/volumes/73/wr/pdfs/mm7314a1.pdf"
| # | report_id | title | publication_date | authors_list | volume_number | issue_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Vaccination Coverage objects from cdc.gov. All fields typed and schema-versioned.
"state_name": "California", "age_group": "19-35 months", "vaccine_type": "MMR", "dose_number": 1, "coverage_pct": 91.4, "survey_year": 2025, "confidence_interval": "89.1-93.7"
| # | state_name | age_group | vaccine_type | dose_number | coverage_pct | sample_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Clinical Guidelines objects from cdc.gov. All fields typed and schema-versioned.
"topic_area": "Infection Control", "target_audience": "Healthcare Providers", "last_reviewed_date": "2026-01-10", "diagnostic_criteria": "Clinical presentation and laboratory confirmation via PCR.", "treatment_protocol": "Standard droplet precautions.", "print_version_url": "https://cdc.gov/infectioncontrol/guidelines/index.html"
| # | topic_area | target_audience | last_reviewed_date | summary_overview | diagnostic_criteria | treatment_protocol |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our CDC scraper navigates complex government data portals, extracts embedded Tableau dashboard metrics, parses PDF reports, and monitors clinical guideline revisions.
Extract weekly notifiable disease reports and case counts across all US jurisdictions directly from tabular data.
Automate complex demographic and mortality queries against the CDC WONDER web interfaces.
Convert unstructured MMWR PDFs and clinical guidance documents into structured JSON with metadata.
Bypass iframe restrictions to scrape underlying tabular data from CDC embedded Tableau and Socrata visualisations.
Track real-time changes to destination-specific health risks and vaccination requirements.
Monitor clinical guidelines for updates, capturing diffs when diagnostic or treatment protocols change.
Crawl archive subdomains to construct longitudinal datasets for epidemiological modelling.
Extract state-level immunisation survey results across adult and paediatric demographics.
Configure continuous pipelines at daily or weekly cadences aligned with CDC publication schedules.
Brief in. Clean data out.
Provide the specific datasets, MMWR volumes, or guideline URLs required. We map the extraction schema.
We configure Scrapy crawlers, Playwright instances for dashboards, and PDF parsing logic.
Schema validation, null-rate checks, and historical data reconciliation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or BigQuery dataset on agreed cadence.
Federal health data is distributed across legacy databases, modern APIs, and unstructured PDFs. Here is how we standardise it.
Many CDC statistics are trapped in embedded Tableau or Socrata iframes. We use Playwright to load these views and intercept the underlying JSON responses before they render.
Historical MMWR reports and guidelines are often published exclusively as PDFs. We use layout-aware parsing to extract tabular data and text blocks into queryable formats.
Government servers have strict rate limits. We implement polite crawling delays and IP rotation to maintain throughput without triggering firewall blocks.
State-level reporting formats change frequently. We map these disparate structures into a single, unified longitudinal schema for downstream analytics.
We hash clinical guideline text blocks to emit diffs only when medical recommendations or diagnostic criteria are officially revised.
Researchers feed historical case counts and mortality rates into predictive disease spread models.
Hospital systems track clinical guideline updates to ensure internal protocols meet federal standards.
Pharmaceutical companies monitor regional outbreak clusters to optimise drug and PPE distribution.
Corporate security teams integrate travel health notices into employee booking platforms.
Academic institutions aggregate WONDER demographic data for longitudinal health outcome studies.
Actuaries use mortality and injury statistics from WISQARS to adjust regional premium models.
"The CDC publishes the most comprehensive epidemiological data in the world, but it is scattered across PDFs, legacy databases, and interactive dashboards."
Extracting actionable intelligence from federal health portals requires navigating fragmented systems, bypassing embedded visualisations, and parsing unstructured documents. DataFlirt centralises this process, converting disparate government publications into clean, normalised warehouse records.
Everything supported by our cdc.gov scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering for interactive dashboards and data portal navigation.
Custom Python pipelines utilise OCR and layout-aware libraries to transform legacy PDF reports into structured, queryable data formats.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting for critical public health datasets.
Data delivered to where your team already works — no new tooling required.
About cdc.gov scraping, legality, and pipeline operations.
Ask us directly →Yes. The CDC is a federal agency, and its publicly published data is generally in the public domain and available for extraction. We strictly target public datasets, guidelines, and reports. We do not attempt to access gated systems or patient-level Protected Health Information (PHI).
We deploy Playwright to load the iframe containing the dashboard. Instead of scraping the visual elements, we intercept the underlying network requests to capture the raw JSON data powering the visualisation, ensuring complete accuracy.
Yes. We use layout-aware parsing libraries to extract text blocks, author metadata, and tabular data from legacy PDF reports, converting them into structured JSON records.
We align extraction cadences with CDC publication schedules. NNDSS reports are typically updated weekly, while travel health notices and certain outbreak trackers can be monitored daily or hourly for critical changes.
Yes. We automate the submission of complex demographic and geographic query parameters to the WONDER interface, extracting the resulting mortality and population data sets.
We maintain a hash index of text blocks for specific guideline pages. When the CDC publishes an update, our change-detection system identifies the modification and emits a diff record highlighting the revised protocol.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical dataset extract or a continuous monitor for clinical guidelines, we build and operate the pipeline. Tell us what you need.
