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.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

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 Covid-19 curve in the United States is rising again after months of decline, with the number of new cases per day doubling over the past three weeks, driven by the fast-spreading Delta variant, lagging vaccination rates, and Fourth of July gatherings

In the United States of America, from 3 January 2020 to 5:05 pm CEST, 14 July 2021, there have been 33,572,715 confirmed cases of COVID-19 with 602,409 deaths, reported to WHO. As of 9 July 2021, a total of 334,282,915 vaccine doses have been administered.

Content

This Column is a resource to help advance the understanding of the virus all-state in the USA

After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

It is a weekly snapshot in time that:

• Focuses on recent outcomes in the last seven days and changes relative to the month prior
• Provides additional contextual information at the county level for each state, and includes national level information
• Supports rapid visual interpretation of results with color thresholds

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Data is obtained from COVID-19 Tracking project and NYTimes. Sincere thanks to them for making it available to the public.

Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19 - World Health Organization

The number of new cases are increasing day by day around the world. This dataset has information from 50 US states and the District of Columbia at daily level.

LICENSE:

Please refer here

              Apache License 2.0

A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code. For counties dataset, please refer here

Content us_states_covid19_daily.csv

This dataset has number of tests conducted in each state at daily level. Column descriptions are

date - date of observation state - US state 2 digit code positive - number of tests with positive results negative - number of tests with negative results pending - number of test with pending results death - number of deaths total - total number of tests

Acknowledgements Sincere thanks to COVID-19 Tracking project from which the data is obtained.

Sincere thanks to NYTimes for the counties dataset

There is a nice tableau public dashboard on the data. Images for this dataset is obtained from the same. Thank you.

Inspiration Some of the questions that could be answered are

How is the spread over time to various states Change in number of people tested over time

Context

COVID-19 is on a rise worldwide. It was first identified in the city of Wuhan in China in 2019 and has now spread into a global pandemic. California is currently the fourth largest affected state in USA. The state's confirmed cases have been on a rise since early March 2020 due to more testing capabilities. In this dire time, it is extremely important to understand the factors affecting the spread of the virus in California, identify susceptible population and predict the trajectory of the infected and dead cases on a daily basis.

Content

Update: 4 April 2020, 7:27 PM Pacific Time (PT)

This data contains information about confirmed cases (13927) and fatalities (321) due to COVID-19 in 58 California counties along with instructions provided by health agencies in all counties. A breakdown of confirmed cases in the cities of California is also provided. The information has been sourced from Los Angeles Times.

As mentioned by LA Times, "The tallies here are mostly limited to residents of California, which is the standard method used to count patients by the state’s health authorities. Those totals do not include people from other states who are quarantined here, such as the passengers and crew of the Grand Princess cruise ship that docked in Oakland."

Acknowledgements

LA Times - https://www.latimes.com/projects/california-coronavirus-cases-tracking-outbreak/

Inspiration

1. This dataset will be useful in understanding and predicting the trajectory of the infected and dead cases in California in the coming days.
2. It might also be useful for COVID19 Local US-CA Forecasting (Week 1) competition
3. The dataset can also highlight any need to update any health agency instructions to take further precautionary measures and save lives.

Please consider upvoting if the data is found useful in any way. If there are any improvement suggestions, do let me know.

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

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.

NOTE: This dataset has been retired and marked as historical-only.

Only Chicago residents are included based on the home ZIP Code, as provided by the medical provider, or the address, as provided by the Cook County Medical Examiner.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted on the date the test specimen was collected. Deaths are those occurring among cases based on the day of death. Hospitalizations are based on the date of first hospitalization. Only one hospitalization is counted for each case. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Because of the nature of data reporting to CDPH, hospitalizations will be blank for recent dates They will fill in on later updates when the data are received, although, as for cases and deaths, may continue to be updated as further data are received.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases, deaths, and hospitalizations, sources used, how cases, deaths and hospitalizations are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office

Status of COVID-19 cases in Ontario

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue:

• Ontario COVID-19 testing percent positive by age group
• Confirmed positive cases of COVID-19 in Ontario
• Ontario COVID-19 testing metrics by Public Health Unit (PHU)
• Ontario COVID-19 testing percent positive by age group
• COVID-19 cases in hospital and ICU, by Ontario Health (OH) region
• Cumulative deaths (new methodology)
• Deaths Involving COVID-19 by Fatality Type

For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data.

Data includes:

• reporting date
• daily tests completed
• total tests completed
• test outcomes
• total case outcomes (resolutions and deaths)
• current tests under investigation
• current hospitalizations
  • current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
  • current patients in Intensive Care Units (ICUs) testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness
  • current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19
• Long-Term Care (LTC) resident and worker COVID-19 case and death totals
• Variants of Concern case totals
• number of new deaths reported (occurred in the last month)
• number of historical deaths reported (occurred more than one month ago)
• change in number of cases from previous day by Public Health Unit (PHU).

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Cumulative Deaths

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change.

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

Related dataset(s)

• Confirmed positive cases of COVID-19 in Ontario

Context

COVID-19 is on a rise worldwide. It was first identified in the city of Wuhan in China in 2019 and has now spread into a global pandemic. Michigan is currently the third largest affected state in USA. The state's confirmed cases have been on a rise since early March 2020. In this dire time, it is extremely important to understand the factors affecting the spread of the virus in Michigan, identify susceptible population and predict the trajectory of the infected and dead cases on a daily basis.

Content

Update: April 4, 2020 2:00 PM Eastern Standard Time (EST)

This data currently contains information about COVID-19 confirmed cases (14225) and deaths (540) in Michigan counties. The dataset also includes percentage of COVID-19 confirmed and dead cases by age, gender, race and ethnicity. The information is published by www.michigan.gov on a daily basis at 2:00 PM EST. The results are included as of 10:00 AM every day.

Acknowledgements

Michigan.gov - Coronavirus

Inspiration

1. This dataset will be useful in understanding and predicting the trajectory of the infected and dead cases in Michigan in the coming days.
2. The dataset can also give insight about the most vulnerable age groups in Michigan.

Please consider upvoting if the data is found useful in any way. If there are any improvement suggestions, do let me know.

COVID cases and deaths for LA County and California State. Updated daily.

Data source: Johns Hopkins University (https://coronavirus.jhu.edu/us-map), Johns Hopkins GitHub (https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv). Code available: https://github.com/CityOfLosAngeles/covid19-indicators.

Analysis of ‘Indonesia-Coronavirus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

COVID-19 has infected many people in Indonesia, and the number of confirmed cases is increasing exponentially. Indonesia has raised its coronavirus alert to the "Darurat Nasional (National Emergency)" until 29 May 2020. The Java island, especially Jakarta, the capital city of Indonesia, is the most affected region by the coronavirus.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Ff46e130bad5d4e74a8835ca057dd05ca%2Facc.png?generation=1584939612835429&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2F93b53d1b6601da74041f41ea4ba227f6%2Fcases.png?generation=1584938551413887&alt=media" alt="">

Following are the list of available online portals announce the information of COVID-19, from the public community and provincial (regional) government website in Indonesia.

1. https://www.covid19.go.id/situasi-virus-corona/, by Indonesian National Board for Disaster Management.
2. https://kawalcovid19.id/, by Kawal Covid-19 Indonesia community.
3. https://corona.jakarta.go.id/, Jakarta tanggap Covid-19 by Pemda DKI Jakarta.
4. https://pikobar.jabarprov.go.id/, Pusat Informasi & Koordinasi COVID-19, by Pemprov Jawa Barat.
5. https://corona.jatengprov.go.id/, Jawa Tengah Tanggap COVID-19, by Pemprov Jawa Tengah.
6. https://corona.sumbarprov.go.id/, Sumbar Tanggap Corona, by Pemprov Sumatera Barat.
7. http://corona.jogjaprov.go.id/, Yogyakarta Tanggap Covid-19, by Pemprov DIY.
8. https://covid19.bandung.go.id/. Pusat Informasi & Koordinasi COVID-19 Kota Bandung.

We make a structured dataset based on the report materials in these portals. Thus, the research community can apply recent AI and statistical techniques to generate new insights in support of the ongoing fight against this infectious disease in Indonesia.

Current State

Dataset 1) Total Confirmed Positive Cases 2) Google Trend Related keywords 3) Patient Epidemiological Data 4) Daily Case Statistics 5) Case per Province 6) Case in Jakarta Capital City 7) Daily New Confirmed Cases in Each Province (Timeline)

Kernel 1) Predicting Coronavirus Positive Cases in Indonesia 2) Visualization & Analysis of Covid-19 in Indonesia 3) Logistic Model for Indonesia COVID-19 4) DataSet Characteristics of Corona patients in several countries, including Indonesia 5) Novel Corona Virus (Covid-19) Indonesia EDA 6) Simple Visualization and Forecasting 7) Characteristics of Corona patients DS

Related Publication 1) Response to Covid-19: Data Analytics and Transparency, Koderea Talks, 18 March 2020, https://www.researchgate.net/publication/340003505_Response_to_Covid-19_Data_Analytics_and_Transparency 2) Covid-19 Data Science, ID Institute Obrolin Data Coronavirus, 24 March 2020, https://www.researchgate.net/publication/340116231_IDInstitute_Covid-19_Data_Science

Other Country Level Datasets

• South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset
• Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy
• Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil
• USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa
• France - https://www.kaggle.com/lperez/coronavirus-france-dataset
• Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia
• Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

Acknowledgements

Thanks sincerely to all the members of the DSCI Team, KawalCovid19.id, Pemda DKI Jakarta, Pemprov Jawa Barat, Pemprov Jawa Tengah, Pemprov Sumatera Barat, and Pemprov DIY.

DSCI Team

1. Ardiansyah (ardisragen)
2. Tri A Sundara (trilabs)
3. Thomhert (thomhert)
4. Epsi Sayidina (epsisayidina)
5. Teuku Hashrul (hahasrul)
6. Naufal Hakim (hakimbazol)

Invitation

We welcome anyone to join us as collaborators! Join WAG Chat: https://s.id/fgPoP For more information please contact ardi@ejnu.net or WA +8210-4297-0504

Working with https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Fd56eaf0a5d770d756a54cec0d09c87ff%2Fkoderea.png?generation=1584539195622597&alt=media" alt="">

--- Original source retains full ownership of the source dataset ---

ObjectiveU.S. drug-related overdose deaths and Emergency Department (ED) visits rose in 2020 and again in 2021. Many academic studies and the news media attributed this rise primarily to increased drug use resulting from the societal disruptions related to the coronavirus (COVID-19) pandemic. A competing explanation is that higher overdose deaths and ED visits may have reflected a continuation of pre-pandemic trends in synthetic-opioid deaths, which began to rise in mid-2019. We assess the evidence on whether increases in overdose deaths and ED visits are likely to be related primarily to the COVID-19 pandemic, increased synthetic-opioid use, or some of both.MethodsWe use national data from the Centers for Disease Control and Prevention (CDC) on rolling 12-month drug-related deaths (2015–2021); CDC data on monthly ED visits (2019-September 2020) for EDs in 42 states; and ED visit data for 181 EDs in 24 states staffed by a national ED physician staffing group (January 2016-June 2022). We study drug overdose deaths per 100,000 persons during the pandemic period, and ED visits for drug overdoses, in both cases compared to predicted levels based on pre-pandemic trends.ResultsMortality. National overdose mortality increased from 21/100,000 in 2019 to 26/100,000 in 2020 and 30/100,000 in 2021. The rise in mortality began in mid-to-late half of 2019, and the 2020 increase is well-predicted by models that extrapolate pre-pandemic trends for rolling 12-month mortality to the pandemic period. Placebo analyses (which assume the pandemic started earlier or later than March 2020) do not provide evidence for a change in trend in or soon after March 2020. State-level analyses of actual mortality, relative to mortality predicted based on pre-pandemic trends, show no consistent pattern. The state-level results support state heterogeneity in overdose mortality trends, and do not support the pandemic being a major driver of overdose mortality.ED visits. ED overdose visits rose during our sample period, reflecting a worsening opioid epidemic, but rose at similar rates during the pre-pandemic and pandemic periods.ConclusionThe reasons for rising overdose mortality in 2020 and 2021 cannot be definitely determined. We lack a control group and thus cannot assess causation. However, the observed increases can be largely explained by a continuation of pre-pandemic trends toward rising synthetic-opioid deaths, principally fentanyl, that began in mid-to-late 2019. We do not find evidence supporting the pandemic as a major driver of rising mortality. Policymakers need to directly address the synthetic opioid epidemic, and not expect a respite as the pandemic recedes.

Context

The first COVID-19 case was reported on 30 January in a student who arrived in Kerala state from Wuhan. Then 2 more cases were reported in the next 2 days in Kerala again. For almost a month, no new cases were reported in India, however, on 8th March, five new cases of coronavirus in Kerala were again reported and since then the cases have been rising affecting 14 states. Here is the Data of of Covid-19 patients in India up till 1st May 2020

Content

The Dataset contains Daily number of reported/confirmed cases. It also contains total number of discharged cases.

Acknowledgements

https://www.mohfw.gov.in/

Actions of Public Health France Public Health France’s mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 outbreak, Santé publique France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the various scenarios and putting in place actions to prevent and limit the transmission of this virus on national territory. ### The Tracking Information System (SI-DEP) The new screening information system (SI-DEP), which has been in operation since 13 May 2020, is a secure platform where the results of the laboratory tests carried out by all city and hospital laboratories for SARS-COV2 are systematically recorded. The creation of this information system is authorised for a period of 6 months from the end of the state of health emergency by application of Decree No 2020-551 of 12 May 2020 on the information systems referred to in Article 11 of Law No 2020-546 of 11 May 2020 extending the state of health emergency and supplementing its provisions. ### Description of data This dataset provides information at the departmental and regional level: — the daily and weekly incidence rate per age group; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate. This dataset provides information at the national level: — the daily and weekly incidence rate by age group and sex; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate. The incidence rate corresponds to the number of positive tests per 100,000 inhabitants. It shall be calculated as follows: (100000 * number of positive cases)/Population Accuracy: — From 29/08 onwards, laboratory data indicators (SI-DEP) show rates of incidence, positivity and screening adjusted for screenings conducted at airports upon arrival of international flights. — For more information, see the methodological note available in the resources. Limits: — Only the biological tests of persons for whom the residence department could be located are shown on the maps. Persons whose department could not be traced in the SIDEP data are counted only at the whole French level. As a result, the sum of the tests indicated in the departments or regions is less than the number of tests indicated in France. — The time limit for repeating tests may exceed 9 days in some cases. The indicators are adjusted daily according to the receipt of the results. ### Notable changes Since 8 December, after verifying the quality of the reported data, all results of RT-PCR or Antigenic tests have been included in the production of national and territorial epidemiological indicators (incidence rates, positivity rates and screening rates) relevant to the monitoring of the COVID-19 outbreak. On the other hand, the epidemic is prolonging in time and screening capacities have increased, leading to an increasing frequency of people tested several times. Thus, an adjustment of the methods of splitting for patients benefiting from repeated tests and therefore the definition of the persons tested was necessary. Public Health France, in its patient-centred epidemiological approach, has therefore adapted its methods to ensure that these indicators reflect, in particular, the proportion of infected people among the population tested. These developments have no impact on the trends and interpretation of the dynamics of the epidemic, which remain the same. More precise test data (impact and positivity) are also published by Santé publique France (SI-DEP data).

Context

Coronavirus (COVID-19) made its outbreak in Colombia with the first confirmed in the country on march 6th, since then, number of confirmed cases has been increasing and deaths related to the virus are starting to have the first confirmed cases.

Content

This data set contains complete information about confirmed cases, deaths and number of recovered patients according to the daily reports by the colombian health department (Ministerio de Salud)

I'll try to keep this dataset fully updated daily as soon as the official reports are published.

Versions

Version 5: Data encoding problem solved by omitting accents and 'ñ' in the states' names Version 6: Departments (states) file added with name and coordinates Version 9: File with each detailed case including sex, age and type of case

COVID-19 was responsible for many deaths and economic losses around the globe since its first case report. Governments implemented a variety of policies to combat the pandemic in order to protect their citizens and save lives. Early in 2020, the first cases were reported in Arizona State and continued to rise until the discovery of the vaccine in 2021. A variety of strategies and interventions to stop or decelerate the spread of the pandemic has been considered. It is recommended to define which strategy was successful for disease propagation prevention and could be used in further similar situations. This study aimed to evaluate the effect of people’s contact interventions strategies which were implemented in Arizona State and their effect on reducing the daily new COVID-19 cases and deaths. Their effect on daily COVID-19 cases and deaths were evaluated using an interrupted time series analysis during the pandemic’s first peaks to better understand the onward situation. Canceling the order of staying at home (95% CI, 1718.52 to 6218.79; p<0.001) and expiring large gatherings (95% CI, 1984.99 to 7060.26; p<0.001) on June 30 and August 17, 2020, respectively, had a significant effect on the pandemic, leading to the daily cases to grow rapidly. Moreover, canceling the stay at home orders led to an increase in the number of COVID-19 daily deaths by 67.68 cases (95% CI, 27.96 to 107.40; p<0.001) after about 21 days while prohibiting large gatherings significantly decreased 66.76 (95% CI: 20.56 to 112.96; p = 0.004) the number of daily deaths with about 21 days’ lag. The results showed that strategies aimed at reducing people’s contact with one another could successfully help fight the pandemic. Findings from this study provide important evidence to support state-level policies that require observance of social distancing by the general public for future pandemics.