We extract disease outbreak news, global health observatory indicators, ICD catalogues, and health topic fact sheets from who.int. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake 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 Global Health Observatory objects from who.int. All fields typed and schema-versioned.
"indicator_id": "WHOSIS_000001", "indicator_name": "Life expectancy at birth (years)", "country_code": "IND", "year": 2021, "numeric_value": 67.2, "lower_bound": 65.8, "upper_bound": 68.6
| # | indicator_id | indicator_name | country_code | country_name | year | value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Disease Outbreak News objects from who.int. All fields typed and schema-versioned.
"don_id": "DON-2023-014", "title": "Cholera - Global situation", "disease": "Cholera", "country": "Global", "publication_date": "2023-02-11", "summary": "Since the last publication on 11 February 2023, the global situation of cholera has further deteriorated."
| # | don_id | title | disease | country | region | publication_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Health Topics objects from who.int. All fields typed and schema-versioned.
"topic_id": "HT-042", "title": "Malaria", "key_facts": "['Malaria is a life-threatening disease spread to humans by some types of mosquitoes.', 'In 2022, there were an estimated 249 million cases of malaria worldwide.']", "symptoms": "Fever, chills, headache", "last_updated": "2023-12-04"
| # | topic_id | title | overview | key_facts | symptoms | treatment |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for ICD-11 Classifications objects from who.int. All fields typed and schema-versioned.
"icd_code": "1D01", "title": "Typhoid fever", "definition": "A disease caused by infection with Salmonella enterica subsp. enterica serovar Typhi.", "browser_url": "https://icd.who.int/browse11/l-m/en#/http%3a%2f%2fid.who.int%2ficd%2fentity%2f254881721", "release_id": "2024-01", "chapter": "01 Certain infectious or parasitic diseases"
| # | icd_code | uri | title | definition | inclusions | exclusions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Country Profiles objects from who.int. All fields typed and schema-versioned.
"country_code": "BRA", "country_name": "Brazil", "region": "Americas", "population": 214300000, "income_group": "Upper-middle income", "life_expectancy": 76.2, "infant_mortality_rate": 12.4
| # | country_code | country_name | region | population | income_group | health_expenditure_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our who.int scraper handles every layer of the platform: statistical dashboards, hierarchical classification systems, unstructured outbreak reports, and regional health profiles - with automated pagination and schema normalisation built in.
Extract GHO indicators, cross-referenced by country and year, directly from the underlying API endpoints.
Parse full text extraction, risk assessments, and WHO advice from unstructured narrative reports.
Extract codes, definitions, inclusions, and exclusions by recursively crawling the official classification ontology.
Structure key facts, symptoms, and prevention guidelines from static HTML pages.
Compile demographics, health expenditure, and mortality statistics per Member State.
Extract metadata, publication dates, and download URLs for technical guidelines and situation reports.
Traverse deep statistical tables and report archives without missing records.
Convert narrative epidemiological reports into structured JSON fields for downstream analysis.
Run monthly statistical updates or daily outbreak monitors depending on data volatility.
Map inconsistent regional reporting formats to a unified, queryable schema.
Brief in. Clean data out.
Provide GHO indicator IDs, ICD chapters, or outbreak regions. We design the extraction schema together.
We configure Scrapy crawlers, API interceptors, and text parsers for who.int.
Schema validation, null-rate checks, and data type enforcement before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Public health data extraction requires navigating legacy portals, dynamic dashboards, and deeply nested classification systems. Here is how we ensure data integrity.
Many WHO statistics are embedded in PowerBI or custom JavaScript dashboards. We use Playwright to execute the JavaScript and intercept the underlying JSON responses.
The ICD-11 classification is a massive ontology. Our crawlers recursively traverse parent-child nodes to build a complete, flattened map of the classification system.
WHO publishes critical data in PDF format. We extract the metadata, publication dates, and download URLs to index the document corpus for your NLP pipelines.
We configure locale-specific headers to extract translated versions of fact sheets and outbreak news, maintaining consistent internal IDs across languages.
We maintain a hash index of last-seen values per indicator. Subsequent runs only push diffs, reducing downstream processing load for massive statistical datasets.
Researchers ingest GHO statistics and DONs to train predictive models for disease spread.
Government agencies benchmark domestic health indicators against regional WHO averages.
R&D teams track disease prevalence and mortality rates to prioritise drug development pipelines.
Insurance firms and risk analysts monitor outbreak news to adjust travel and health risk premiums.
AI teams use ICD-11 definitions and health topic fact sheets to train clinical text classifiers.
Logistics firms correlate outbreak reports with medical supply demand surges.
"The World Health Organization publishes the definitive baseline for global health metrics - but accessing it programmatically across legacy systems requires dedicated infrastructure."
Most teams underestimate the investment required: reliable who.int scraping requires navigating dynamic dashboards, deeply nested classification trees, and unstructured narrative reports. DataFlirt absorbs that complexity so your analysts can focus on the epidemiology - not the infrastructure.
Everything supported by our who.int 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 retry logic. Playwright handles JavaScript rendering for dynamic GHO dashboards.
Bypasses UI rendering to extract raw JSON from background network requests, ensuring clean data extraction from embedded visualisations.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About who.int scraping, legality, and pipeline operations.
Ask us directly →Yes, WHO data is public domain. We extract only publicly available statistics, reports, and classifications.
We intercept the underlying network requests to extract the raw JSON data, rather than scraping the rendered UI.
Yes, we recursively traverse the ICD-11 ontology to extract all parent-child relationships, definitions, and exclusion rules.
We extract the metadata, publication dates, and download URLs for PDF reports, but do not parse the internal PDF text.
Outbreak news can be monitored daily. Statistical indicators are typically updated annually or monthly by WHO, and we schedule pipelines to match this cadence.
Yes, we can extract the full historical back-catalogue of GHO indicators and Disease Outbreak News.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of ICD-11 codes or a continuous feed of global health indicators - we scope, build, and operate the pipeline. Tell us what you need.
