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Daily COVID-19 tests per 1,000 people

See all data and research on:
COVID-19

What you should know about this indicator

  • Testing is a key tool for identifying infections, guiding treatment, isolating positive cases, tracing contacts, and allocating healthcare resources.
  • The number of COVID-19 tests is not measured uniformly across countries: some track 'tests performed', while others count 'people tested'.
  • Most reported data includes PCR and antigen tests; antibody tests are generally excluded since they are less relevant for current infection tracking.
  • At-home self-tests may be counted if reported nationally, but many countries still rely primarily on laboratory tests for confirmation.
  • Varying reporting standards and test definitions can complicate international comparisons and the interpretation of testing statistics.
Daily COVID-19 tests per 1,000 people
New tests for COVID-19 (7-day smoothed). For countries that don't report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window.
Source
Official data collated by Our World in Data (2022) – with major processing by Our World in Data
Last updated
August 9, 2024

Related research and writing

Featured image

Coronavirus (COVID-19) Testing

Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser

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Sources and processing

This data is based on the following sources

Official data collated by Our World in Data – COVID-19, testing

This data is collected by the Our World in Data team from official reports.

On 23 June 2022, we stopped adding new datapoints to our COVID-19 testing dataset. You can read more at https://github.com/owid/covid-19-data/discussions/2667.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our database, and you should always check the license of any such third-party data before use.

Retrieved on
August 9, 2024
Retrieved from
https://github.com/owid/covid-19-data/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Hasell, J., Mathieu, E., Beltekian, D. et al. A cross-country database of COVID-19 testing. Sci Data 7, 345 (2020). https://doi.org/10.1038/s41597-020-00688-8
The data has been obtained from different sources depending on the country:

This data is collected by the Our World in Data team from official reports.

On 23 June 2022, we stopped adding new datapoints to our COVID-19 testing dataset. You can read more at https://github.com/owid/covid-19-data/discussions/2667.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our database, and you should always check the license of any such third-party data before use.

Retrieved on
August 9, 2024
Retrieved from
https://github.com/owid/covid-19-data/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Hasell, J., Mathieu, E., Beltekian, D. et al. A cross-country database of COVID-19 testing. Sci Data 7, 345 (2020). https://doi.org/10.1038/s41597-020-00688-8
The data has been obtained from different sources depending on the country:

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

Comparisons across countries are affected by differences in testing policies and reporting methods.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Daily COVID-19 tests per 1,000 people”, part of the following publication: Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser (2020) - “COVID-19 Pandemic”. Data adapted from Official data collated by Our World in Data. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/daily-tests-per-thousand-people-smoothed-7-day.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Official data collated by Our World in Data (2022) – with major processing by Our World in Data

Full citation

Official data collated by Our World in Data (2022) – with major processing by Our World in Data. “Daily COVID-19 tests per 1,000 people” [dataset]. Official data collated by Our World in Data, “COVID-19, testing” [original data]. Retrieved March 31, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/daily-tests-per-thousand-people-smoothed-7-day.html (archived on March 4, 2026).

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Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/daily-tests-per-thousand-people-smoothed-7-day.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear