The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

The World Health Organization reported 766440796 Coronavirus Cases since the epidemic began. In addition, countries reported 6932591 Coronavirus Deaths. This dataset provides - World Coronavirus Cases- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

This is the data for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).Data SourcesWorld Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-casesMinistry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

This is the real-time JSON version of the COVID-19 Coronavirus Dataset Worldwide dataset, furthermore, the collection methodology can be read through: https://www.ecdc.europa.eu/en/covid-19/data-collection

Context

The worldwide situation about the COVID-19 (by 2019-03-23), data provided by the European Centre for Disease Prevention and Control and published on the EU Open Data Portal.

Content

The dataset contains the latest available public data on COVID-19 including a daily situation update, the epidemiological curve and the global geographical distribution (EU/EEA and the UK, worldwide). On 12 February 2020, the novel coronavirus was named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) while the disease associated with it is now referred to as COVID-19. ECDC is closely monitoring this outbreak and providing risk assessments to guide EU Member States and the EU Commission in their response activities.

Acknowledgements

Official link: https://data.europa.eu/euodp/en/data/dataset/covid-19-coronavirus-data

Inspiration

What applications can we develop to understand COVID-19 current and prospective behavior better?

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

A complete list of live websites using the Msit Corona Virus Live Update Widgets technology, compiled through global website indexing conducted by WebTechSurvey.

For the latest analysis and visualizations of the COVID-19 pandemic, check out my constantly updated EDA notebook here 📈.

Context

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of coronavirus that causes coronavirus disease 2019 (COVID-19), the respiratory illness responsible for the COVID-19 pandemic.

Since its first identification in December 2019 in Wuhan, China, this virus has taken the world by storm. Some people prefer to look at the positive side of things and how this pandemic has brought forward several positive changes. However, the collateral damages produced by this pandemic cannot be overlooked. From the Economic impact to Mental Health impacts, this pandemic period will arguably be one of the hardest periods we'll encounter in our lives. That being said, we always have to arm ourselves with hope. With the new advancements in the vaccine studies, let's hope to wake up from this nightmare as soon as possible.

“Hope is being able to see that there is light despite all of the darkness.” – Desmond Tutu

As for the reason for me building this dataset, it's because I couldn't get my hands on an easily digestible and up-to-date dataset of Covid-19, so, I decided to build my own using Python and web scraping techniques. I will also update this dataset as frequently as possible!

Content

This data was scraped from woldometers.info on 2022-05-14 by Joseph Assaker.

225 countries are represented in this data.

All of countries have records dating from 2020-2-15 until 2022-05-14 (820 days per country). That's with the exception of China, which has records dating from 2020-1-22 until 2022-05-14 (844 days per country), and Palau which has records dating from 2021-8-25 until 2022-05-14 (263 days per country)..

Summary Data Columns Description:

• country: designates the Country in which the the row's data was observed.
• continent: designates the Continent of the observed country.
• total_confirmed: designates the total number of confirmed cases in the observed country.
• total_deaths: designates the total number of confirmed deaths in the observed country.
• total_recovered: designates the total number of confirmed recoveries in the observed country.
• active_cases: designates the number of active cases in the observed country.
• serious_or_critical: designates the estimated number of cases in serious or critical conditions in the observed country.
• total_cases_per_1m_population: designates the number of total cases per 1 million population in the observed country.
• total_deaths_per_1m_population: designates the number of total deaths per 1 million population in the observed country.
• total_tests: designates the number of total tests done in the observed country.
• total_tests_per_1m_population: designates the number of total test done per 1 million population in the observed country.
• population: designates the population count in the observed country.

Daily Data Columns Description:

• date: designates the date of observation of the row's data in YYYY-MM-DD format.
• country: designates the Country in which the the row's data was observed.
• cumulative_total_cases: designates the cumulative number of confirmed cases as of the row's date, for the row's country.
• daily_new_cases: designates the daily new number of confirmed cases on the row's date, for the row's country.
• active_cases: designates the number of active cases (i.e., confirmed cases that still didn't recover nor die) on the row's date, for the row's country.
• cumulative_total_deaths: designates the cumulative number of confirmed deaths as of the row's date, for the row's country.
• daily_new_deaths: designates the daily new number of confirmed deaths on the row's date, for the row's country.

Acknowledgements

As previously mentioned, all the data present in this dataset is scraped from worldometers.info.

Inspiration

Going through this data, Kagglers can visualize various trends in their own country, or compare several countries. One can also combine this dataset with other news and key points in time (lockdowns, new UK mutation, Holidays, etc.) in order to study the effects of these events on the progression of Covid-19 in a multitude of countries. Implementing time series analysis on this dataset would also be an amazing idea! Getting a deep learning algorithm to learn from this sea of data and try to predict the future turn of events could be quite interesting!

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.Trends represent the day-to-day rate of new cases with a focus on the most recent 10 to 14 days. Includes Puerto Rico, Guam, Northern Marianas, and U.S. Virgin Islands. Daily new case counts are volatile for many reasons and sometimes the trends reflect that volatility. Thus, we decided to include longer-term summaries here. County Trends as of 9 Mar 20230 (-0) in Emergent1135 (+51) in Spreading1664 (-63) in Epidemic230 (+10) in Controlled110 (+2) in End StageNotes: Many states now only report once per week, and FL only once every two weeks. On 3/7/2022 we adjusted the formula for active cases to reflect the Omicron Variant which is documented to cause lower rates of serious and severe illness. To produce these trends we analyze daily updates from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.For more information about COVID-19 trends, see our country level trends story map and the full methodology.Data Source: Johns Hopkins University CSSE US Cases by County dashboard and USAFacts for Utah County level Data.Feature layer generated from running the Join Features solution that is the basis for daily updates for the U.S. County COVID-19 Tends Story Map.

Context

This dataset is a curated version of 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE.

Content

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Data processing

From the original source of the data, we perform the following operations: * Concatenate the daily reports files (https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports)
* Add daily update date (as Date field)

• Fix country names duplicates. Multiple countries have duplicate names, ex: South Korea, Republic of Korea, Korea, South. data_df.loc[data_df['Country/Region']==' Azerbaijan', 'Country/Region'] = 'Azerbaijan' data_df.loc[data_df['Country/Region']=='Czechia', 'Country/Region'] = 'Czech Republic' data_df.loc[data_df['Country/Region']=="Cote d'Ivoire", 'Country/Region'] = 'Ivory Coast' data_df.loc[data_df['Country/Region']=='Iran (Islamic Republic of)', 'Country/Region'] = 'Iran' data_df.loc[data_df['Country/Region']=='Hong Kong SAR', 'Country/Region'] = 'Hong Kong' data_df.loc[data_df['Country/Region']=='Holy See', 'Country/Region'] = 'Vatican City' data_df.loc[data_df['Country/Region']=='Macao SAR', 'Country/Region'] = 'Macau' data_df.loc[data_df['Country/Region']=='Mainland China', 'Country/Region'] = 'China' data_df.loc[data_df['Country/Region']=='Republic of Ireland', 'Country/Region'] = 'Ireland' data_df.loc[data_df['Country/Region']=='Korea, South', 'Country/Region'] = 'South Korea' data_df.loc[data_df['Country/Region']=='Republic of Ireland', 'Country/Region'] = 'Ireland' data_df.loc[data_df['Country/Region']=='Republic of Korea', 'Country/Region'] = 'South Korea' data_df.loc[data_df['Country/Region']=='Republic of Moldova', 'Country/Region'] = 'Moldova' data_df.loc[data_df['Country/Region']=='Republic of the Congo', 'Country/Region'] = 'Congo (Brazzaville)' data_df.loc[data_df['Country/Region']=='Taiwan*', 'Country/Region'] = 'Taiwan' data_df.loc[data_df['Country/Region']=='The Gambia', 'Country/Region'] = 'Gambia' data_df.loc[data_df['Country/Region']=='Gambia, The', 'Country/Region'] = 'Gambia' data_df.loc[data_df['Country/Region']=='UK', 'Country/Region'] = 'United Kingdom' data_df.loc[data_df['Country/Region']=='Viet Nam', 'Country/Region'] = 'Vietnam'
• Replace missing data in Lat/Long for Province/State and/or Country/Region

data_df = pd.DataFrame()
for file in tqdm(os.listdir(db_source)):
  try:
    crt_date, crt_ext = crt_file = file.split(".")
    if(crt_ext == "csv"):
      crt_date_df = pd.read_csv(os.path.join(db_source, file))
      crt_date_df['date_str'] = crt_date
      crt_date_df['date'] = crt_date_df['date_str'].apply(lambda x: datetime.strptime(x, "%m-%d-%Y"))
      data_df = data_df.append(crt_date_df)
  except:
    pass

province_state = data_df['Province/State'].unique()

for ps in province_state:

  data_df.loc[(data_df['Province/State']==ps) & (data_df['Latitude'].isna()), 'Latitude'] =\
        data_df.loc[(~data_df['Latitude'].isna()) & \
              (data_df['Province/State']==ps), 'Latitude'].median()
  
  data_df.loc[(data_df['Province/State']==ps) & (data_df['Longitude'].isna()), 'Longitude'] =\
      data_df.loc[(~data_df['Longitude'].isna()) & \
            (data_df['Province/State']==ps), 'Longitude'].median() 

country_region = data_df['Country/Region'].unique()

for cr in country_region:

  data_df.loc[(data_df['Country/Region']==cr) & (data_df['Latitude'].isna()), 'Latitude'] =\
        data_df.loc[(~data_df['Latitude'].isna()) & \
              (data_df['Country/Region']==cr), 'Latitude'].median()
  
  data_df.loc[(data_df['Country/Region']==cr) & (data_df['Longitude'].isna()), 'Longitude'] =\
      data_df.loc[(~data_df['Longitude'].isna()) & \
            (data_df['Country/Region']==cr), 'Longitude'].median() 

Acknowledgements

Data source: https://github.com/CSSEGISandData/COVID-19

Inspiration

Represent the geographical data distribution of 2019-nCoV spread. Represent time series with Confirmed, Recovered, Deaths cases. Analyse the mortality. Try to forecast the evolution of cases. Compare the spread of Coronavirus for different countries, with different policies for social isolation, closing schools, stopping international travels.

This dataset includes CSV files that contain IDs and sentiment scores of the tweets related to the COVID-19 pandemic. The tweets have been collected by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. This dataset has been wholly re-designed on March 20, 2020, to comply with the content redistribution policy set by Twitter.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets:(a) Tweets Originating from India During COVID-19 Lockdowns(b) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------Below is the quick overview of this dataset.— Dataset name: COV19Tweets Dataset— Number of tweets : 857,809,018 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Geo-tagged Version: Coronavirus (COVID-19) Geo-tagged Tweets Dataset (GeoCOV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags (archive: keywords.tsv) : "corona", "#corona", "coronavirus", "#coronavirus", "covid", "#covid", "covid19", "#covid19", "covid-19", "#covid-19", "sarscov2", "#sarscov2", "sars cov2", "sars cov 2", "covid_19", "#covid_19", "#ncov", "ncov", "#ncov2019", "ncov2019", "2019-ncov", "#2019-ncov", "pandemic", "#pandemic" "#2019ncov", "2019ncov", "quarantine", "#quarantine", "flatten the curve", "flattening the curve", "#flatteningthecurve", "#flattenthecurve", "hand sanitizer", "#handsanitizer", "#lockdown", "lockdown", "social distancing", "#socialdistancing", "work from home", "#workfromhome", "working from home", "#workingfromhome", "ppe", "n95", "#ppe", "#n95", "#covidiots", "covidiots", "herd immunity", "#herdimmunity", "pneumonia", "#pneumonia", "chinese virus", "#chinesevirus", "wuhan virus", "#wuhanvirus", "kung flu", "#kungflu", "wearamask", "#wearamask", "wear a mask", "vaccine", "vaccines", "#vaccine", "#vaccines", "corona vaccine", "corona vaccines", "#coronavaccine", "#coronavaccines", "face shield", "#faceshield", "face shields", "#faceshields", "health worker", "#healthworker", "health workers", "#healthworkers", "#stayhomestaysafe", "#coronaupdate", "#frontlineheroes", "#coronawarriors", "#homeschool", "#homeschooling", "#hometasking", "#masks4all", "#wfh", "wash ur hands", "wash your hands", "#washurhands", "#washyourhands", "#stayathome", "#stayhome", "#selfisolating", "self isolating"Dataset Files (the local time mentioned below is GMT+5:45)corona_tweets_01.csv + corona_tweets_02.csv + corona_tweets_03.csv: 2,475,980 tweets (March 20, 2020 01:37 AM - March 21, 2020 09:25 AM)corona_tweets_04.csv: 1,233,340 tweets (March 21, 2020 09:27 AM - March 22, 2020 07:46 AM)corona_tweets_05.csv: 1,782,157 tweets (March 22, 2020 07:50 AM - March 23, 2020 09:08 AM)corona_tweets_06.csv: 1,771,295 tweets (March 23, 2020 09:11 AM - March 24, 2020 11:35 AM)corona_tweets_07.csv: 1,479,651 tweets (March 24, 2020 11:42 AM - March 25, 2020 11:43 AM)corona_tweets_08.csv: 1,272,592 tweets (March 25, 2020 11:47 AM - March 26, 2020 12:46 PM)corona_tweets_09.csv: 1,091,429 tweets (March 26, 2020 12:51 PM - March 27, 2020 11:53 AM)corona_tweets_10.csv: 1,172,013 tweets (March 27, 2020 11:56 AM - March 28, 2020 01:59 PM)corona_tweets_11.csv: 1,141,210 tweets (March 28, 2020 02:03 PM - March 29, 2020 04:01 PM)corona_tweets_12.csv: 793,417 tweets (March 30, 2020 02:01 PM - March 31, 2020 10:16 AM)corona_tweets_13.csv: 1,029,294 tweets (March 31, 2020 10:20 AM - April 01, 2020 10:59 AM)corona_tweets_14.csv: 920,076 tweets (April 01, 2020 11:02 AM - April 02, 2020 12:19 PM)corona_tweets_15.csv: 826,271 tweets (April 02, 2020 12:21 PM - April 03, 2020 02:38 PM)corona_tweets_16.csv: 612,512 tweets (April 03, 2020 02:40 PM - April 04, 2020 11:54 AM)corona_tweets_17.csv: 685,560 tweets (April 04, 2020 11:56 AM - April 05, 2020 12:54 PM)corona_tweets_18.csv: 717,301 tweets (April 05, 2020 12:56 PM - April 06, 2020 10:57 AM)corona_tweets_19.csv: 722,921 tweets (April 06, 2020 10:58 AM - April 07, 2020 12:28 PM)corona_tweets_20.csv: 554,012 tweets (April 07, 2020 12:29 PM - April 08, 2020 12:34 PM)corona_tweets_21.csv: 589,679 tweets (April 08, 2020 12:37 PM - April 09, 2020 12:18 PM)corona_tweets_22.csv: 517,718 tweets (April 09, 2020 12:20 PM - April 10, 2020 09:20 AM)corona_tweets_23.csv: 601,199 tweets (April 10, 2020 09:22 AM - April 11, 2020 10:22 AM)corona_tweets_24.csv: 497,655 tweets (April 11, 2020 10:24 AM - April 12, 2020 10:53 AM)corona_tweets_25.csv: 477,182 tweets (April 12, 2020 10:57 AM - April 13, 2020 11:43 AM)corona_tweets_26.csv: 288,277 tweets (April 13, 2020 11:46 AM - April 14, 2020 12:49 AM)corona_tweets_27.csv: 515,739 tweets (April 14, 2020 11:09 AM - April 15, 2020 12:38 PM)corona_tweets_28.csv: 427,088 tweets (April 15, 2020 12:40 PM - April 16, 2020 10:03 AM)corona_tweets_29.csv: 433,368 tweets (April 16, 2020 10:04 AM - April 17, 2020 10:38 AM)corona_tweets_30.csv: 392,847 tweets (April 17, 2020 10:40 AM - April 18, 2020 10:17 AM)> With the addition of some more coronavirus specific keywords, the number of tweets captured day has increased significantly, therefore, the CSV files hereafter will be zipped. Lets save some bandwidth.corona_tweets_31.csv: 2,671,818 tweets (April 18, 2020 10:19 AM - April 19, 2020 09:34 AM)corona_tweets_32.csv: 2,393,006 tweets (April 19, 2020 09:43 AM - April 20, 2020 10:45 AM)corona_tweets_33.csv: 2,227,579 tweets (April 20, 2020 10:56 AM - April 21, 2020 10:47 AM)corona_tweets_34.csv: 2,211,689 tweets (April 21, 2020 10:54 AM - April 22, 2020 10:33 AM)corona_tweets_35.csv: 2,265,189 tweets (April 22, 2020 10:45 AM - April 23, 2020 10:49 AM)corona_tweets_36.csv: 2,201,138 tweets (April 23, 2020 11:08 AM - April 24, 2020 10:39 AM)corona_tweets_37.csv: 2,338,713 tweets (April 24, 2020 10:51 AM - April 25, 2020 11:50 AM)corona_tweets_38.csv: 1,981,835 tweets (April 25, 2020 12:20 PM - April 26, 2020 09:13 AM)corona_tweets_39.csv: 2,348,827 tweets (April 26, 2020 09:16 AM - April 27, 2020 10:21 AM)corona_tweets_40.csv: 2,212,216 tweets (April 27, 2020 10:33 AM - April 28, 2020 10:09 AM)corona_tweets_41.csv: 2,118,853 tweets (April 28, 2020 10:20 AM - April 29, 2020 08:48 AM)corona_tweets_42.csv: 2,390,703 tweets (April 29, 2020 09:09 AM - April 30, 2020 10:33 AM)corona_tweets_43.csv: 2,184,439 tweets (April 30, 2020 10:53 AM - May 01, 2020 10:18 AM)corona_tweets_44.csv: 2,223,013 tweets (May 01, 2020 10:23 AM - May 02, 2020 09:54 AM)corona_tweets_45.csv: 2,216,553 tweets (May 02, 2020 10:18 AM - May 03, 2020 09:57 AM)corona_tweets_46.csv: 2,266,373 tweets (May 03, 2020 10:09 AM - May 04, 2020 10:17 AM)corona_tweets_47.csv: 2,227,489 tweets (May 04, 2020 10:32 AM - May 05, 2020 10:17 AM)corona_tweets_48.csv: 2,218,774 tweets (May 05, 2020 10:38 AM - May 06, 2020 10:26 AM)corona_tweets_49.csv: 2,164,251 tweets (May 06, 2020 10:35 AM - May 07, 2020 09:33 AM)corona_tweets_50.csv: 2,203,686 tweets (May 07, 2020 09:55 AM - May 08, 2020 09:35 AM)corona_tweets_51.csv: 2,250,019 tweets (May 08, 2020 09:39 AM - May 09, 2020 09:49 AM)corona_tweets_52.csv: 2,273,705 tweets (May 09, 2020 09:55 AM - May 10, 2020 10:11 AM)corona_tweets_53.csv: 2,208,264 tweets (May 10, 2020 10:23 AM - May 11, 2020 09:57 AM)corona_tweets_54.csv: 2,216,845 tweets (May 11, 2020 10:08 AM - May 12, 2020 09:52 AM)corona_tweets_55.csv: 2,264,472 tweets (May 12, 2020 09:59 AM - May 13, 2020 10:14 AM)corona_tweets_56.csv: 2,339,709 tweets (May 13, 2020 10:24 AM - May 14, 2020 11:21 AM)corona_tweets_57.csv: 2,096,878 tweets (May 14, 2020 11:38 AM - May 15, 2020 09:58 AM)corona_tweets_58.csv: 2,214,205 tweets (May 15, 2020 10:13 AM - May 16, 2020 09:43 AM)> The server and the databases have been optimized; therefore, there is a significant rise in the number of tweets captured per day.corona_tweets_59.csv: 3,389,090 tweets (May 16, 2020 09:58 AM - May 17, 2020 10:34 AM)corona_tweets_60.csv: 3,530,933 tweets (May 17, 2020 10:36 AM - May 18, 2020 10:07 AM)corona_tweets_61.csv: 3,899,631 tweets (May 18, 2020 10:08 AM - May 19, 2020 10:07 AM)corona_tweets_62.csv: 3,767,009 tweets (May 19, 2020 10:08 AM - May 20, 2020 10:06 AM)corona_tweets_63.csv: 3,790,455 tweets (May 20, 2020 10:06 AM - May 21, 2020 10:15 AM)corona_tweets_64.csv: 3,582,020 tweets (May 21, 2020 10:16 AM - May 22, 2020 10:13 AM)corona_tweets_65.csv: 3,461,470 tweets (May 22, 2020 10:14 AM - May 23, 2020 10:08 AM)corona_tweets_66.csv: 3,477,564 tweets (May 23, 2020 10:08 AM - May 24, 2020 10:02 AM)corona_tweets_67.csv: 3,656,446 tweets (May 24, 2020 10:02 AM - May 25, 2020 10:10 AM)corona_tweets_68.csv: 3,474,952 tweets (May 25, 2020 10:11 AM - May 26, 2020 10:22 AM)corona_tweets_69.csv: 3,422,960 tweets (May 26, 2020 10:22 AM - May 27, 2020 10:16 AM)corona_tweets_70.csv: 3,480,999 tweets (May 27, 2020 10:17 AM - May 28, 2020 10:35 AM)corona_tweets_71.csv: 3,446,008 tweets (May 28, 2020 10:36 AM - May 29, 2020 10:07 AM)corona_tweets_72.csv: 3,492,841 tweets (May 29, 2020 10:07 AM - May 30, 2020 10:14 AM)corona_tweets_73.csv: 3,098,817 tweets (May 30, 2020 10:15 AM - May 31, 2020 10:13 AM)corona_tweets_74.csv: 3,234,848 tweets (May 31, 2020 10:13 AM - June 01, 2020 10:14 AM)corona_tweets_75.csv: 3,206,132 tweets (June 01, 2020 10:15 AM - June 02, 2020 10:07 AM)corona_tweets_76.csv: 3,206,417 tweets (June 02, 2020 10:08 AM - June 03, 2020 10:26 AM)corona_tweets_77.csv: 3,256,225 tweets (June 03, 2020

TL;DR

Our free COVID-19 Stats and New API lets you send a web-based query to Smartable AI and get back details about global and regional coronavirus data, including latest numbers, historic values, and geo-breakdowns. It is the same API that powers our popular COVID-19 stats pages. Developers can take the returned information and display it in their own tools, apps and visualizations. Different from other coronavirus data sources that produce breaking changes from time to time, the data from our API are more stable, **detailed **and close to real-time, as we leverage AI to gather information from many credible sources. With a few clicks in our API try-it experience, developers can get it running quickly and unleash their creativity.

Click here to get started

Our Mission

“We’re not just fighting an epidemic; we’re fighting an infodemic” – WHO Director-General Tedros Adhanom Ghebreyesus

In Smartable AI, our mission is to use AI to help you be smart in this infodemic world. Information is exploded, and mis-information has impacted the decisions of governments, businesses, and citizens around the world, as well as individuals’ lives. In 2018, The World Economic Forum identified it as one of the top 10 global risks. In a recent study, the economic impact has been estimated to be upwards of 80-100 Billion Dollars. Everything we do is focused on fighting misinformation, curating quality content, putting information in order and leveraging technology to bring clean, organized information through our APIs. Everyone wins when they can make sense of the world around them.

Our Free API and Daily Data Dump

The coronavirus stats and news API offers the latest and historic COVID-19 stats and news information per country or state. The stats are refreshed every hour using credible data sources, including the country/state’s official government websites, data available on wikipedia pages, latest news reports, Johns Hopkins University CSSE 2019-nCoV Dashboard, WHO Situation Reports, CDC Situation Updates, and DXY.cn.

The API takes the location ISO code as input (e.g. US, US-MA), and returns the latest numbers (confirmed, deaths, recovered), the delta from yesterday, the full history in that location, and geo-breakdown when applicable. We offer detailed API documentation, a try-it experience, and code examples in many different programming languages.

https://smartable.azureedge.net/media/2020/03/coronavirus-api-documentation.webp" alt="API Documentation">

We upload a daily dump of the data in the csv format here.

We want it to be a collaborative effort. If you have any additional requirements for the API or observe anything wrong with the data, we welcome you to report issues in our GitHub account. The team will jump in right away. All our team members are ex-Microsoft employees, so you can trust the quality of support, I guess 🙂

Our Example Apps powered by the API

We have developed two example apps by using the API.

• COVID-19 Live Stats+News
• COVID-19 Android app

Coronavirus resources: US state and local health deparments (Live Science web page)._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...