Global Hospital Beds Capacity (for Covid-19)

DISCLAIMER

Dataset consists of historical data of pre-pandemic period and doesn’t represent the current reality which may have changed due to the spikes in demand.
This dataset has been generated in collaboration of efforts within CoronaWhy community.

Context

Last updated: April 26th 2020
Updates:
April 14th 2020 - Added missing population data
April 15th 2020 - Added Brazil statewise ICU hospital beds dataset
April 21th 2020 - Added Italy, Spain statewise ICU hospital beds dataset, India statewise TOTAL hospital beds dataset
April 26th 2020 - Added Sweden ICU(2019) and TOTAL(2018) beds datasets

Purpose of the dataset

I am trying to produce a dataset that will provide a foundation for policymakers to understand the realistic capacity of healthcare providers being able to deal with the spikes in demand for intensive care. As a way to help, I’ve prepared a dataset of beds across countries and states. Work in progress dataset that should and will be updated as more data becomes available and public on weekly basis.

Importance

This dataset is intended to be used as a baseline for understanding the typical bed capacity and coverage globally. This information is critical for understanding the impact of a high utilization event, like COVID-19.

Current challenges

Datasets are scattered across the web and are very hard to normalize, I did my best but help would be much appreciated.

Data sources / Acknowledgments

arcgis (USA) - https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Hospitals_1/FeatureServer/0
KHN (USA) - https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/
datahub.io (World) - https://datahub.io/world-bank/sh.med.beds.zs
eurostat - https://data.europa.eu/euodp/en/data/dataset/vswUL3c6yKoyahrvIRyew
OECD - https://data.oecd.org/healtheqt/hospital-beds.htm
WDI (World) - https://data.worldbank.org/indicator/SH.MED.BEDS.ZS
NHP(India) - http://www.cbhidghs.nic.in/showfile.php?lid=1147
data.gov.sg (Singapore) - https://data.gov.sg/dataset/health-facilities?view_id=91b4feed-dcb9-4720-8cb0-ac2f04b7efd0&resource_id=dee5ccce-4dfb-467f-bcb4-dc025b56b977
dati.salute.gov.it (Italy)- http://www.dati.salute.gov.it/dati/dettaglioDataset.jsp?menu=dati&idPag=96
portal.icuregswe.org (Sweden) - https://portal.icuregswe.org/seiva/en/Rapport
publications:
Intensive Care Medicine Journal (Europe) - https://link.springer.com/article/10.1007/s00134-012-2627-8
Critical Care Medicine Journal (Asia) - https://www.researchgate.net/figure/Number-of-critical-care-beds-per-100-000-population_fig1_338520008
Medicina Intensiva (Spain) - https://www.medintensiva.org/en-pdf-S2173572713000878
news:
https://lanuovaferrara.gelocal.it/italia-mondo/cronaca/2020/03/19/news/dietro-la-corsa-a-nuovi-posti-in-terapia-intensiva-gli-errori-del-passato-1.38611596
kaggle:
germany - https://www.kaggle.com/manuelblechschmidt/icu-beds-in-germany
brazil (IBGE) - https://www.kaggle.com/thiagobodruk/brazilianstates
Manual population data search from wiki

Data columns

country,state,county,lat,lng,type,measure,beds,population,year,source,source_url

• country - country of origin, if present
• state - more granular location, if present
• lat - latitude
• lng - longtitude
• type - [TOTAL, ICU, ACUTE(some data could include ICU beds too), PSYCHIATRIC, OTHER(merged ‘SPECIAL’, ‘CHRONIC DISEASE’, ‘CHILDREN’, ‘LONG TERM CARE’, ‘REHABILITATION’, ‘WOMEN’, ‘MILITARY’]
• measure - type of measure (per 1000 inhabitants)
• beds - number of beds per 1000
• population - population of location based on multiple sources and wikipedia
• year - source year for beds and population data
• source - source of data
• source_url - URL of the original source

Files

for each of datasource: hospital_beds_per_source.csv

US only: US arcgis + khn (state/county granularity): hospital_beds_USA.csv

Global (state(region)/county granularity): hospital_beds_global_regional.csv

Global (country granularity): hospital_beds_global_v1.csv

Contributors

Igor Kiulian - extracting/normalizing/formatting/merging data
Artur Kiulian - helped with Kaggle setup
Augaly S. Kiedi - helped with country population data
Kristoffer Jan Zieba - found Swedish data sources

Possible Improvements

Find and megre more detailed (state/county wise) or newer datasource