CDC Open Data¶

# List all available datasets
uv run python -m cdc_open list

# Query a dataset by key name
uv run python -m cdc_open query leading_death -f table

# Raw Socrata query with filter
uv run python -m cdc_open query leading_death \
  --where "year='2017' AND state='United States'" \
  -f table

# Ask a question using the LLM analyzer
uv run python -m cdc_open analyze \
  "What were the top 5 leading causes of death in 2017?"

uv run python -m cdc_open query schoolvaxview \
  --where "geography_type='National' AND vaccine='MMR' AND survey_type='Census'" \
  --select "year_season,coverage_estimate" \
  --order "year_season" -f table

# Re-fetch the pre-aggregated national monthly CSV
uv run python -m cdc_open.fetch_beam

# Raw query: all source types, by state
uv run python -m cdc_open query beam_report \
  --select "year,month,state,source_type,pathogen,sum(number_of_isolates)" \
  --where "pathogen='Salmonella'" \
  --order "year,month,state" -f csv

# Human isolates only, all pathogens
uv run python -m cdc_open query beam_report \
  --where "source_type='Human'" \
  --select "year,month,pathogen,serotype_species,number_of_isolates" \
  --order "year DESC,month DESC" -f table

# Leading causes of death, United States, all years
uv run python -m cdc_open query leading_death \
  --where "state='United States'" \
  --order "year" -f csv

# Weekly deaths: COVID + flu + pneumonia, last 52 weeks, all states
uv run python -m cdc_open query weekly_deaths \
  --where "state!='United States'" \
  --select "state,end_date,covid_19_deaths,influenza_deaths,total_deaths" \
  --order "end_date DESC" --limit 2600 -f csv

# Drug overdose deaths by state, 2000-2016
uv run python -m cdc_open query drug_overdose_state \
  --where "sex='Both sexes' AND age='All Ages'" \
  --select "year,state,death_rate" \
  --order "year,state" -f csv

# PLACES: county obesity rates in Texas
uv run python -m cdc_open query places_county \
  --where "stateabbr='TX' AND measureid='OBESITY'" \
  --select "locationname,data_value" \
  --order "data_value DESC" -f table

# NWSS wastewater COVID trends, California sites
uv run python -m cdc_open query wastewater_covid \
  --where "state_territory='California'" \
  --order "sample_collect_date DESC" --limit 100 -f table

# VSRR provisional overdose deaths, latest 12 months
uv run python -m cdc_open query drug_overdose_vsrr \
  --where "indicator='Number of Deaths'" \
  --order "year DESC,month DESC" --limit 200 -f csv

# BEAM foodborne pathogens: human Salmonella isolates by state
uv run python -m cdc_open query beam_report \
  --where "source_type='Human' AND pathogen='Salmonella'" \
  --select "year,month,state,serotype_species,number_of_isolates" \
  --order "year DESC,month DESC" -f table

# HAI: MRSA case rates by year
uv run python -m cdc_open query hai_mrsa \
  --order "yearname DESC" --limit 50 -f table

# NNDSS: measles cases by state, current year
uv run python -m cdc_open query nndss_measles \
  --where "year='2025'" \
  --order "week DESC" -f table

# Requires ANTHROPIC_API_KEY in .env
uv run python -m cdc_open analyze \
  "What are the top 10 causes of death in the US for the most recent year?"

uv run python -m cdc_open analyze \
  "Which states had the highest drug overdose death rates in 2015?"

uv run python -m cdc_open analyze \
  "How has COVID-19 as a percentage of weekly deaths changed over time?"

uv run python -m cdc_open analyze \
  "Which counties in Florida have the highest obesity rates?"

# Show the raw data alongside the analysis
uv run python -m cdc_open analyze \
  "What is the SARS-CoV-2 wastewater signal trend in the last 3 months?" \
  --dump-data --dump-format csv

# Any data.cdc.gov dataset by ID
uv run python -m cdc_open query bi63-dtpu \
  --where "year='2017' AND state='United States'" \
  -f table