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.

Real-time coronavirus pandemic statistics with cases, deaths, recoveries, and vaccination data

Historical Coronavirus (Covid-19) Data for the United States

NEW: We are publishing the data behind our excess deaths tracker in order to provide researchers and the public with a better record of the true toll of the pandemic. This data is compiled from official national and municipal data for 24 countries. See the data and documentation in the excess-deaths/ directory.

[ U.S. Data (Raw CSV) | U.S. State-Level Data (Raw CSV) | U.S. County-Level Data (Raw CSV) ]

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.

Live and Historical Data

We are providing two sets of data with cumulative counts of coronavirus cases and deaths: one with our most current numbers for each geography and another with historical data showing the tally for each day for each geography.

The historical data files are at the top level of the directory and contain data up to, but not including the current day. The live data files are in the live/ directory.

A key difference between the historical and live files is that the numbers in the historical files are the final counts at the end of each day, while the live files have figures that may be a partial count released during the day but cannot necessarily be considered the final, end-of-day tally..

The historical and live data are released in three files, one for each of these geographic levels: U.S., states and counties.

Each row of data reports the cumulative number of coronavirus cases and deaths based on our best reporting up to the moment we publish an update. Our counts include both laboratory confirmed and probable cases using criteria that were developed by states and the federal government. Not all geographies are reporting probable cases and yet others are providing confirmed and probable as a single total. Please read here for a full discussion of this issue.

We do our best to revise earlier entries in the data when we receive new information. If a county is not listed for a date, then there were zero reported confirmed cases and deaths.

State and county files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.

Download all the data or clone this repository by clicking the green "Clone or download" button above.

Historical Data

U.S. National-Level Data

The daily number of cases and deaths nationwide, including states, U.S. territories and the District of Columbia, can be found in the us.csv file. (Raw CSV file here.)

date,cases,deaths
2020-01-21,1,0
...

State-Level Data

State-level data can be found in the states.csv file. (Raw CSV file here.)

date,state,fips,cases,deaths
2020-01-21,Washington,53,1,0
...

County-Level Data

County-level data can be found in the counties.csv file. (Raw CSV file here.)

date,county,state,fips,c...

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

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

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

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

As countless lives are being lost to the coronavirus disease 2019 (COVID-19) pandemic, the idea of building a similar platform to memorialize COVID-19 losses was suggested. It is easy to see the news and be overwhelmed by the numbers, while forgetting these numbers represent real lives that were loved by their friends and family.At a time of such great loss, there is also hope, as over a million diagnosed cases have recovered from the virus. So in a similar vein, a platform to share stories of recovery has also been created.Original storymap and details seen here: https://coronavirus-stories-of-loss-and-recovery-giscorps.hub.arcgis.com/#igotvaccinatedCreated by Jeremiah Lindemann, jlindemann_GISCorps

Background: The COVID-19 pandemic has caused morbidity and mortality, as well as, widespread disruption to people’s lives and livelihoods around the world. As a result of the health and economic threats posed by the pandemic to the global community, there are concerns that rates of suicide and suicidal behaviour may rise during and in its aftermath. Our living systematic review focuses on suicide prevention in relation to COVID-19, with this iteration synthesising relevant evidence up to June 7th 2020. Method: Automated daily searches feed into a web-based database with screening and data extraction functionalities. Eligibility criteria include incidence/prevalence of suicidal behaviour, exposure-outcome relationships and effects of interventions in relation to the COVID-19 pandemic. Outcomes of interest are suicide, self-harm or attempted suicide and suicidal thoughts. No restrictions are placed on language or study type, except for single-person case reports. Results: Searches identified 2070 articles. Twenty-nine articles (28 studies) met our inclusion criteria. Fourteen articles were research letters or pre-prints awaiting peer review. All articles reported observational data: twelve cross-sectional; eight case series; five modelling; and three service utilisation studies. No studies reported on changes in rates of suicidal behaviour. Case series were largely drawn from news reporting in low/middle income countries and factors associated with suicide included fear of infection, social isolation and economic concerns. Conclusions: A marked improvement in the quality of design, methods, and reporting in future studies is needed. There is thus far no clear evidence of an increase in suicide, self-harm, suicidal behaviour, or suicidal thoughts associated with the pandemic . However, suicide data are challenging to collect in real time and economic effects are evolving. Our living review will provide a regular synthesis of the most up-to-date research evidence to guide public health and clinical policy to mitigate the impact of COVID-19 on suicide. Protocol registration: PROSPERO CRD42020183326 01/05/2020

Summary of this dataset

This dataset has been designed and obtained for discussing control measures during the COVID-19 pandemic. In this study, 1,260 people living in Tokyo and Kanagawa prefectures in Japan participated in the survey. This survey was used to collect participants’ behaviors and the objects that they touched on the days that they went out at 15 types of locations and vehicles.

This dataset is expected to improve our understanding of actual human behavior and contact with objects that could. Although it is impossible to disinfect all objects and spaces, this dataset is expected to contribute to the prioritization of disinfection during periods of widespread infection.

Datasets

1. Study and Dataset Design

The participants living in Tokyo and Kanagawa prefectures in Japan were asked to respond, in detail, to a survey regarding the locations they stayed at for an extended period between December 3 (Thursday) and December 7 (Monday), 2020, and all the items that they touched during this time. Using the locations where clusters of infections were found during April 2020, 12 locations were selected (e.g., medical facilities, including hospitals; restaurants; stores whose main objective was to sell alcohol, such as bars; companies, including the participants’ own companies and the offices of others; and sports facilities such as gyms) and investigated. Similarly, three means of transport, namely trains, buses, and taxis, were selected as spaces where people often crowd together.

The main survey was conducted with 1,536 subjects during December 3–8. Data from 1,260 subjects who gave valid responses were used for the dataset. To ensure that the respondents could respond while their memories were still fresh, the survey was distributed to each subject on the day of their corresponding behavior. Participants were asked to respond about the locations where they spent most of their time during the corresponding period. They were also asked to detail all the objects they touched (excluding personal objects) during this time. The objects in this study were evaluated using a free-writing description. Typographical errors and differences in expressions were frequently observed (e.g., water closet, toilet, and bathroom). A categorization rule was thus developed to better ascertain the actual status of locations and object contact. The participants’ expressions were modified through visual inspection.

2. Patient and Public Involvement

This survey was conducted after appropriate review by the Ethics Committee of the Graduate School of Engineering, University of Tokyo (examination number: 20-61, approval number: KE20-72).

Reference

Teruaki Hayashi, Daisuke Hase, Hikaru Suenaga, Yukio Ohsawa, "The Actual Conditions of Person-to-Object Contact and a Proposal for Prevention Measures During the COVID-19 Pandemic," medRxiv, 2021. DOI: https://doi.org/10.1101/2021.04.11.21255290

Acknowledgements

This research project was supported by the “Startup Research Program for Post-Corona Society” of the Academic Strategy Office, School of Engineering, the University of Tokyo, and the “COVID-19 AI and Simulation Project” run by Mitsubishi Research Institute commissioned by the Office for Novel Coronavirus Disease Control, Cabinet Secretariat, Government of Japan. The authors would like to thank PLUG-Inc. for survey design and implementation.

In September 2020, a survey found that ** percent of respondents in the United States had been buying household supplies online before the coronavirus pandemic. After COVID-19, there was an expected ** percentage point increase in consumers buying these home items online. The same survey was conducted in February 2021 and it revealed that spending intentions decreased across many categories. Fitness and wellness, groceries, personal care products, and household supplies were among the few segments where post-COVID-19 growth was still expected. The change is real The coronavirus pandemic has upended lives worldwide, from how we work to shop and socialize, in general. In a survey published in March 2021, U.S consumers were asked about the important attributes of shopping online, among which the most chosen answers were faster delivery and in-stock availability. However, the share of consumers that shopped online for the first time is relatively minimal. For instance, only ****percent of German and Japanese respondents had never purchased online before 2020. Shopping online more than ever The e-commerce purchase frequency has also changed. In the U.S., over ** percent of respondents mentioned that their household goods online purchasing cycle had increased compared to one month previously. In terms of traffic and reach, food and groceries, home and garden, and sports and outdoors were the fastest-growing e-commerce categories worldwide.

The Covid-19 induced United Kingdom-wide lockdown in 2020 saw choirs face a unique situation of trying to continue without being able to meet in-person. Live networked simultaneous music-making for large groups of singers is not possible, so other “virtual choir” activities were explored. A cross sectional online survey of 3948 choir members and facilitators from across the United Kingdom was conducted, with qualitative analysis of open text questions, to investigate which virtual choir solutions have been employed, how choir members and facilitators experience these in comparison to an “in-person” choir, and whether the limitations and opportunities of virtual choir solutions shed light on the value of the experience of group singing as a whole. Three virtual choir models were employed: Multi-track, whereby individuals record a solo which is mixed into a choral soundtrack; Live streamed, where individuals take part in sessions streamed live over social media; Live tele-conferencing, for spoken interaction and/or singing using tele-conferencing software. Six themes were identified in the open text responses: Participation Practicalities, encompassing reactions to logistics of virtual models; Choir Continuity, reflecting the responsibility felt to maintain choir activities somehow; Wellbeing, with lockdown highlighting to many the importance of in-person choirs to their sense of wellbeing; Social Aspects, reflecting a sense of community and social identity; Musical Elements, whereby the value of musical experience shifted with the virtual models; Co-creation through Singing, with an overwhelming sense of loss of the embodied experience of singing together in real-time, which is unattainable from existing virtual choir models. The experiences, activities and reflections of choir singers during lockdown present a unique perspective to understand what makes group singing a meaningful experience for many. Co-creation through Singing needs further investigation to understand the impact of its absence on virtual choirs being able re-create the benefits of in-person choirs.