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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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

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.

The COVID-19 pandemic that spread across the world at the beginning of 2020 was not only a big threat to public health, but also to the entire sports industry. Many professional and amateur leagues and events were canceled and the public was advised to not spend time in large groups or in public areas. During an April 2020 survey in the United States, 13 percent of respondents strongly agreed with the sentiment that, although they were not really sports fans, they were planning on watching some sporting events when live sports start broadcasting again.

A hub with live displays of news, information, and content related to COVID-19 extracted from Facebook, Reddit and Instagram, from around the world (or in specific regions or country) from local news outlets, regional WHO pages, government agencies, local politicians and more. Each Live Display features post streams sorted by COVID-19 or vaccine related keywords and public accounts to each region.

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

There is a need for development of an analytical method for rapid detection of SARS-CoV-2 virus which is causing the COVID-19 pandemic. Currently available traditional tissue/cell culture-based analytical method is too laborious and takes several days to get the results on the presence/absence of viable/infectious virus in a sample. Such a delay in getting the sample analysis results can be a serious obstacle in rapidly determining the presence of infectious virus in environment which, in turn, can impact environmental epidemiological investigations and studies on surface transmission of this virus. In this manuscript, development of a Rapid Viability Reverse Transcriptase Polymerase Chain Reaction (RV-RT-PCR) method that can significantly reduce the time-to-results for sample analysis from several days to less than a day is described. The RV-RT-PCR method integrates cell-culture based enrichment of the virus with virus-specific RT-PCR analysis. The RTPCR analysis is conducted before and after the cell-culture-virus (sample) incubation. An optimum algorithm is established such that the resultant RT-PCR cycle threshold (CT) value difference between before and after cell-culture-virus incubation RT-PCR analyses determines the presence of viable/infectious virus in the sample. The data set included here is from this research work. A manuscript has also been included here along with the Supplemental Tables for additional data. The Data-Metadata file includes all the data and a glossary to explain the scientific terms used. This dataset is associated with the following publication: Shah, S., S. Kane, M. Elsheikh, and T. Alfaro. Development of a Rapid Viability RT-PCR (RV-RT-PCR) Method to Detect Infectious SARS-CoV-2 from Swabs. JOURNAL OF VIROLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 297: 114251, (2021).

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...

At the height of the coronavirus pandemic, on the last day of March 2020, Wikipedia in all languages broke a record for most traffic in a single day. Since the breakout of the Covid-19 pandemic at the start of January, tens if not hundreds of millions of people have come to Wikipedia to read - and in some cases also contribute - knowledge, information and data about the virus to an ever-growing pool of articles. Our study focuses on the scientific backbone behind the content people across the world read: which sources informed Wikipedia’s coronavirus content, and how was the scientific research on this field represented on Wikipedia. Using citation as readout we try to map how COVID-19 related research was used in Wikipedia and analyse what happened to it before and during the pandemic. Understanding how scientific and medical information was integrated into Wikipedia, and what were the different sources that informed the Covid-19 content, is key to understanding the digital knowledge echosphere during the pandemic. To delimitate the corpus of Wikipedia articles containing Digital Object Identifier (DOI), we applied two different strategies. First we scraped every Wikipedia pages form the COVID-19 Wikipedia project (about 3000 pages) and we filtered them to keep only page containing DOI citations. For our second strategy, we made a search with EuroPMC on Covid-19, SARS-CoV2, SARS-nCoV19 (30’000 sci papers, reviews and preprints) and a selection on scientific papers form 2019 onwards that we compared to the Wikipedia extracted citations from the english Wikipedia dump of May 2020 (2’000’000 DOIs). This search led to 231 Wikipedia articles containing at least one citation of the EuroPMC search or part of the wikipedia COVID-19 project pages containing DOIs. Next, from our 231 Wikipedia articles corpus we extracted DOIs, PMIDs, ISBNs, websites and URLs using a set of regular expressions. Subsequently, we computed several statistics for each wikipedia article and we retrive Atmetics, CrossRef and EuroPMC infromations for each DOI. Finally, our method allowed to produce tables of citations annotated and extracted infromations in each wikipadia articles such as books, websites, newspapers.Files used as input and extracted information on Wikipedia's COVID-19 sources are presented in this archive.See the WikiCitationHistoRy Github repository for the R codes, and other bash/python scripts utilities related to this project.

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

Analysis of ‘COVID-19 Coronavirus Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vignesh1694/covid19-coronavirus on 14 February 2022.

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

Context

A SARS-like virus outbreak originating in Wuhan, China, is spreading into neighboring Asian countries, and as far afield as Australia, the US a and Europe.

On 31 December 2019, the Chinese authorities reported a case of pneumonia with an unknown cause in Wuhan, Hubei province, to the World Health Organisation (WHO)’s China Office. As more and more cases emerged, totaling 44 by 3 January, the country’s National Health Commission isolated the virus causing fever and flu-like symptoms and identified it as a novel coronavirus, now known to the WHO as 2019-nCoV.

The following dataset shows the numbers of spreading coronavirus across the globe.

Content

Sno - Serial number Date - Date of the observation Province / State - Province or state of the observation Country - Country of observation Last Update - Recent update (not accurate in terms of time) Confirmed - Number of confirmed cases Deaths - Number of death cases Recovered - Number of recovered cases

Acknowledgements

Thanks to John Hopkins CSSE for the live updates on Coronavirus and data streaming. Source: https://github.com/CSSEGISandData/COVID-19 Dashboard: https://public.tableau.com/profile/vignesh.coumarane#!/vizhome/DashboardToupload/Dashboard12

Inspiration

Inspired by the following work: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

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

Summary This layer has been DEPRECATED (last updated 12/1/2021). Was formerly a weekly update. The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit https://opendata.maryland.gov/dataset/MD-COVID-19-Congregate-Outbreak/ey5n-qn5s to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21. Confirmed COVID-19 deaths among Maryland residents who live and work in congregate living facilities in Maryland for the reporting period. Description The MD COVID-19 - Total Deaths in Congregate Facility Settings data layer is a total of deaths confirmed by a positive COVID-19 test result that have been reported to MDH in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities for the reporting period. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services. To appear on the list, facilities report at least one confirmed case of COVID-19 over the prior 14 days. Facilities are removed from the list when health officials determine 14 days have passed with no new cases and no tests pending. The list provides a point-in-time picture of COVID-19 case activity among these facilities. Numbers reported for each facility listed reflect totals ever reported for deaths. Data are updated once weekly. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The live music industry and other live entertainment events were significantly impacted by the coronavirus (COVID-19) outbreak in 2020. According to a survey of live music industry professionals, the majority believed that live shows will have increased sanitization when they return. Around 84 percent also believed that events would be cashless, incorporating touchless technology.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 28 January 2022.

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

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

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

The Coronavirus (COVID-19) Press Briefings Corpus is a work in progress to collect and present in a machine readable text dataset of the daily briefings from around the world by government authorities. During the peak of the pandemic, most countries around the world informed their citizens of the status of the pandemic (usually involving an update on the number of infection cases, number of deaths) and other policy-oriented decisions about dealing with the health crisis, such as advice about what to do to reduce the spread of the epidemic.Usually daily briefings did not occur on a Sunday.At the moment the dataset includes:

UK/England: Daily Press Briefings by UK Government between 12 March 2020 - 01 June 2020 (70 briefings in total)Scotland: Daily Press Briefings by Scottish Government between 3 March 2020 - 01 June 2020 (76 briefings in total)Wales: Daily Press Briefings by Welsh Government between 23 March 2020 - 01 June 2020 (56 briefings in total)Northern Ireland: Daily Press Briefings by N. Ireland Assembly between 23 March 2020 - 01 June 2020 (56 briefings in total)World Health Organisation: Press Briefings occuring usually every 2 days between 22 January 2020 - 01 June 2020 (63 briefings in total)

More countries will be added in due course, and we will be keeping this updated to cover the latest daily briefings available.The corpus is compiled to allow for further automated political discourse analysis (classification).

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.

This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse. The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.

Important: This dataset is updated regularly and the latest version for download can be found here: https://www.semanticscholar.org/cord19/download. In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of scholarly articles, including full text content, about COVID-19 and the coronavirus family of viruses for use by the global research community. This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease. The corpus will be updated weekly as new research is published in peer-reviewed publications and archival services like bioRxiv, medRxiv, and others. By downloading this dataset you are agreeing to the Dataset license. Specific licensing information for individual articles in the dataset is available in the metadata file. Additional licensing information is available on the PMC website, medRxiv website and bioRxiv website. Dataset content: Commercial use subset Non-commercial use subset PMC custom license subset bioRxiv/medRxiv subset (pre-prints that are not peer reviewed) Metadata file Readme Each paper is represented as a single JSON object (see schema file for details). Description: The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources: PubMed's PMC open access corpus using this query (COVID-19 and coronavirus research) Additional COVID-19 research articles from a corpus maintained by the WHO bioRxiv and medRxiv pre-prints using the same query as PMC (COVID-19 and coronavirus research) We also provide a comprehensive metadata file of coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications (includes articles without open access full text). We recommend using metadata from the comprehensive file when available, instead of parsed metadata in the dataset. Please note the dataset may contain multiple entries for individual PMC IDs in cases when supplementary materials are available. This repository is linked to the WHO database of publications on coronavirus disease and other resources, such as Microsoft Academic Graph, PubMed, and Semantic Scholar. A coalition including the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health came together to provide this service. Citation: When including CORD-19 data in a publication or redistribution, please cite our arXiv pre-print. The Allen Institute for AI and particularly the Semantic Scholar team will continue to provide updates to this dataset as the situation evolves and new research is released.

Exploring the impact of coronavirus on marketing, a study from June 2020 consulting 32 to 40 senior marketers from companies across 17 sectors worldwide, compared the amount of live and planned response campaigns in March, April and June and found that there has been a significant increase in live campaigns in response to COVID-19 over time. In March, only 32 percent of respondents stated that their response campaigns were already live with 18 percent about to go live. In April these figures had already shifted with 68 percent of respondents saying that their campaigns responding to the coronavirus were live already while another eight percent said they were about to go live. In June, the response campaigns that were already live reached a peak with 78 percent of respondents stating that they were up and running, while six percent of respondents claimed to have campaigns about to go live.

Multilingual (EN, ES, FR, PT, IT, DE, KO, RU, ZH, UK, VI) COVID-19-related corpus acquired from the website (https://www.cdc.gov/) of the Centers for Disease Control and Prevention of US government (11th August 2020). It contains 51202 TUs in total.