Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

content

The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

• sex: 1 for female and 2 for male.
• age: of the patient.
• classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• pneumonia: whether the patient already have air sacs inflammation or not.
• pregnancy: whether the patient is pregnant or not.
• diabetes: whether the patient has diabetes or not.
• copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
• asthma: whether the patient has asthma or not.
• inmsupr: whether the patient is immunosuppressed or not.
• hypertension: whether the patient has hypertension or not.
• cardiovascular: whether the patient has heart or blood vessels related disease.
• renal chronic: whether the patient has chronic renal disease or not.
• other disease: whether the patient has other disease or not.
• obesity: whether the patient is obese or not.
• tobacco: whether the patient is a tobacco user.
• usmr: Indicates whether the patient treated medical units of the first, second or third level.
• medical unit: type of institution of the National Health System that provided the care.
• intubed: whether the patient was connected to the ventilator.
• icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
• date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

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

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

At the beginning of 2023, China's COVID-19 containment policies were relaxed, leading to a rapid spread of the novel coronavirus pneumonia among the populace. Many people quickly became infected and shared their stories of illness and experiences on the Sina Weibo platform. This dataset mined 6,000 comment texts posted under the topic "COVID Recovery Diary" on the Sina Weibo platform between December 7, 2022, and January 27, 2023, after data cleaning and sampling.

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.

NOTE: This layer is deprecated (last updated 3/14/2022). Was formerly a daily update. Summary The cumulative number of COVID-19 positive Maryland residents who have been released from home isolation. Description The MD COVID-19 - Total Number Released from Isolation data layer is a collection of the statewide cumulative total of individuals who tested positive for COVID-19 that have been reported each day by each local health department via the ESSENCE system as having been released from home isolation. As "recovery” can mean different things as people experience COVID-19 disease to varying degrees of severity, MDH reports on individuals released from isolation. “Released from isolation” refers to those who have met criteria and are well enough to be released from home isolation. Some of these individuals may have been hospitalized at some point. 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 COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

United States recorded 16306656 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, United States reported 797346 Coronavirus Deaths. This dataset includes a chart with historical data for the United States Coronavirus Recovered.

Context

According to WHO Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illnesses.

Johns Hopkins University has made an excellent dashboard for tracking the spread of COVID-19. Data is extracted from the Johns Hopkins Github repository associated and made available here.

Content

This dataset has daily level information on the number of confirmed cases, deaths and recovery cases from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number. The data is available from 22 Jan, 2020 and updated regularly. Github repository of this clean dataset is here

Columns Description

Filename is covid-19_cleaned_data.csv(updated) - Province/State- Province/State of the observations - Country/Region-Country of observations - Date- Last update - Confirmed - Cumulative number of confirmed cases till that date - Recovered - Cumulative number of recovered till that date - Deaths- Cumulative number of deaths till that date - Lat and Long - Coordinates

Acknowledgements

• Johns Hopkins University -https://github.com/CSSEGISandData/COVID-19
• World Health Organization(WHO) - https://www.who.int/

Inspiration

Some insights could be 1. Mortality rate over time 2. Exponential growth 3. Changes in the number of affected cases over time 4. The latest number of affected cases

The Community Engagement team at the Greater London Authority (GLA) commissioned this report to identify and examine past and present projects which involve collecting Londoners experiences of COVID-19 through a variety of creative and non-traditional materials. The purpose of the report is to: provide an overview of projects and activities which record Londoners COVID-19 stories and experiences. outline who is responsible for these projects and activities (individuals, museums, community groups, charities, community interest groups, non-profits, other institutions and organisations). analyse the voices of individuals/groups/communities targeted in the projects and activities. highlight obvious gaps in the collected data which can inform future programmes geographically map out projects and other activities which record COVID-19 stories and experiences across Greater London. The data provides insight into trends and patterns in COVID-19 collecting projects and activities that have been carried out in London from March 2020 to March 2021. Reflections and final suggestions on how to navigate these projects and activities for specific next steps in the Community-Led Recovery Programme, targeted missions, suggestions etc. will be discussed later in this report. In particular, this report provides information relevant to the London Community Story (LCS) Programme, one of the two strands of the Community-Led Recovery programme. Alongside this report is a dataset outlining 160 COVID-19 collecting projects that took place in London. The dataset gives project names, boroughs, material types, collecting organisation type and organisation names. We encourage you to use this dataset as a starting point and then do your own additional research on the 160 projects. If you are aware of a project that has not been included, please let us know and we can add it.

Laboratory-confirmed COVID-19 cases, associated deaths, and recoveries among Department of Developmental Services (DDS) individuals and staff by DDS provider. Recovery is based off of test-based and non-test based strategies detailed in guidance presented by the Centers for Disease Control and Prevention and the CT Department of Public Health. All data are preliminary, and data for previous dates will be updated as new reports are received and data errors are corrected. DDS stopped centralized reporting of COVID-19 cases on 4/3/2023.

As the infection with the SARs-CoV-2 virus has evolved into the most significant pandemic of the last centuries, representing a huge burden for the Health Systems worldwide, over 250 million people became infected and more than 5 million died. Since COVID-19 is a relatively recent illness, the course of CV complications is still unclear. It is speculated that most of them recover sooner or later after the acute illness, but exact date is lacking so that further studies are needed to clarify this aspect.

These documents were produced through a collaboration between GLA, PHE London and Association of Directors of Public Health London. The wider impacts slide set pulls together a series of rapid evidence reviews and consultation conversations with key London stakeholders. The evidence reviews and stakeholder consultations were undertaken to explore the wider impacts of the pandemic on Londoners and the considerations for recovery within the context of improving population health outcomes. The information presented in the wider impact slides represents the emerging evidence available at the time of conducting the work (May-August 2020). The resource is not routinely updated and therefore further evidence reviews to identify more recent research and evidence should be considered alongside this resource. It is useful to look at this in conjunction with the ‘People and places in London most vulnerable to COVID-19 and its social and economic consequences’ report commissioned as part of this work programme and produced by the New Policy Institute. Additional work was also undertaken on the housing issues and priorities during COVID. A short report and examples of good practice are provided here. These reports are intended as a resource to support stakeholders in planning during the transition and recovery phase. However, they are also relevant to policy and decision-making as part of the ongoing response. The GLA have also commissioned the University of Manchester to undertake a rapid evidence review on inequalities in relation to COVID-19 and their effects on London.

The severe acute respiratory syndrome coronavirus - 2 (SARS - CoV - 2) was reported to cause the Wuhan outbreak of the corona virus disease 2019(COVID-19). To date, the COVID-19 has infected more than 600 million people gloabally. As a growing number of patients recover from acute infections and are discharged from hospitals, the proportion of patients in the recovery period is gradually increasing. Many of these individuals have been reported to experience multiple symptoms during the convalescence, such as fatigue, dyspnea and pain which are designated as “long-COVID”, “post-COVID syndrome” or “recovery sequelae. We searched for recent articles published in PubMed on COVID-19 convalescence and found that the pathogenesis of COVID-19 convalescence is not yet well recognized. It may be associated with incomplete recovery of immune system, parenchymal organ damage (liver or lung), coagulation abnormalities, “second hit” caused by viral infection, and Phenomenon of Cell Senescence-Associated Secretory Phenotype (SASP). Some drugs and psychological factors of patients also play a non-negligible role in it. We also found that the effect of traditional Chinese medicine (TCM) is effective in the treatment of the COVID-19 recovery phase, which can not only relieve the corresponding symptoms, but also improve the indicators and pulmonary fibrosis. Bufei Huoxue Capsule, as the only drug explicitly mentioned for COVID-19 recovery period, can exert strong rehabilitative effects on physiological activity in patients recovering from COVID-19. In addition, in previous studies, traditional Chinese medicine has been confirmed to have the ability to resist cytokine storms, as well as improve coagulation and myocardial damage, which makes it have potential therapeutic advantages in targeting the hyperimmune response, coagulation abnormalities and myocardial damage existing in the recovery period. In conclusion, the clinical symptoms of patients convalescing from COVID-19 are complex, and its pathogenesis has not been elucidated. traditional Chinese medicine, as a traditional treatment, its specific action and mechanism need to be confirmed by more studies, so that it can play a better role.

Dataset

This dataset was created by malar kodi

Contents

Our study demonstrated that a postdischarge home monitoring program for COVID-19 patients is feasible and well tolerated. The L.I.F.E. T-shirt device was able to collect a full set of cardiorespiratory parameters (i.e. heart rate, a full ECG, respiratory rate, SpO2), both at rest and during brief exercise, which are valuable in patients suffering from respiratory diseases. As the medium-term and long-term consequences of COVID-19 infection are still unknown, implementing strategies of postrecovery monitoring are useful to identify patients at risk of clinical deterioration.7,8 Moreover, it could help to shorten hospital stays, a particularly desirable goal, given the lack of beds typically experienced during a pandemic crisis and, keeping people at home, it could mitigate the in-hospital transmission of COVID-19.7,8 In addition, despite the lack of a specific questionnaire on satisfaction and acceptance, telephone contact was performed on a daily basis to confirm that the study procedures were well tolerated, with most of the patients reporting feeling reassured by being monitored. Our population, as shown by baseline cardiorespiratory parameters, was at low risk of events. The full potential of this kind of home monitoring will not only be probably experienced in the clinical context of more severe COVID-19 patients but also in other clinical scenarios. Finally, given the small sample size, we were able to identify only one patient without any previous disease who presented post-COVID sleep apnea syndrome. Further studies are certainly needed to assess the prevalence and the clinical impact of this complication in post-COVID-19 patients.

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This table reports case classification and status data.

The "test mode" rows show confirmed and probable case counts for all Cambridge residents who have tested positive for COVID-19 or have been clinically diagnosed with the disease to date. The numbers represented in these rows reflect individual people (cases), not tests performed. If someone is clinically diagnosed and later gets an antibody test, for example, they will be removed from the “clinical diagnosis” category and added to the “antibody positive” category. Case classification is based on guidance from the Massachusetts Department of Public Health and is as follows:

Confirmed Case: A person with a positive viral (PCR) test for COVID-19. This test is also known as a molecular test.

Probable Case: A person with a positive antigen test. This test is also known as a rapid test.

A person who is a known contact of a confirmed case and has received a clinical diagnosis based on their symptoms. People in this category have not received a viral or antibody test. Whenever possible, lab results from a viral (PCR) test are used to confirm a clinical diagnosis, and if that is not feasible, antibody testing can be used.

Suspect Case: A person with a positive antibody test. This test is also known as a serology test.

The "case status" rows show current outcomes for all Cambridge residents who are classified as confirmed, probable, or suspect COVID-19 cases. Outcomes include:

Recovered Case: The Cambridge Public Health Department determines if a Cambridge COVID-19 case has recovered based on the Center for Disease Control and Prevention’s criteria for ending home isolation: https://www.cdc.gov/coronavirus/2019-ncov/hcp/disposition-in-home-patients.html. Staff from the Cambridge Public Health Department (CPHD) or the state’s Community Tracing Collaborative (CTC) follow up with all reported COVID-19 cases multiple times throughout their illness. It is through these conversations that CPHD or CTC staff determine when a Cambridge resident infected with COVID-19 has met the CDC criteria for ending isolation, which connotes recovery. While many people with mild COVID-19 illness will meet the CDC criteria for ending isolation (i.e., recovery) in under two weeks, people who survive severe illness might not meet the criteria for six weeks or more.

Active Case: This category reflects Cambridge COVID-19 cases who are currently infected. Note: There may be a delay in the time between a person being released from isolation (recovered) and when their recovery is reported.

Death: This category reflects total deaths among Cambridge COVID 19 cases.

Unknown Outcome: This category reflects Cambridge COVID-19 cases who public health staff have been unable to reach by phone or letter, or who have stopped responding to follow up from public health staff.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

After October 13, 2022, this dataset will no longer be updated as the related CDC COVID Data Tracker site was retired on October 13, 2022.

This dataset contains historical trends in vaccinations and cases by age group, at the US national level. Data is stratified by at least one dose and fully vaccinated. Data also represents all vaccine partners including jurisdictional partner clinics, retail pharmacies, long-term care facilities, dialysis centers, Federal Emergency Management Agency and Health Resources and Services Administration partner sites, and federal entity facilities.

Following the outbreak of COVID-19, multidisciplinary research focusing on the long-term effects of the COVID-19 infection and the complete recovery is still scarce. With regards to long-term consequences, biomarkers of physiological effects as well as the psychological experiences are of significant importance for comprehensively understanding the complete COVID-19 recovery. The present research surveys the IgG antibody titers and the impact of COVID-19 as a traumatic experience in the aftermath of the active infection period, around 2 months after diagnosis, in a subset of COVID-19 patients from the first wave (March-April 2020) of the outbreak in Northern Cyprus. Associations of antibody titers and psychological survey measures with baseline characteristics and disease severity were explored, and correlations among various measures were evaluated. Of the 47 serology tests conducted for presence of IgG antibodies, 39 (83%) were positive. We identified trends demonstrating individuals experiencing severe or critical COVID-19 disease and/or those with comorbidities are more heavily impacted both physiologically and mentally, with higher IgG titers and negative psychological experience compared to those with milder disease and without comorbidities. We also observed that more than half of the COVID-19 cases had negative psychological experiences, being subjected to discrimination and verbal harassment/insult, by family/friends. In summary, as the first study co-evaluating immune response together with mental status in COVID-19, our findings suggest that further multidisciplinary research in larger sample populations as well as community intervention plans are needed to holistically address the physiological and psychological effects of COVID-19 among the cases.

When people still show symptoms of COVID-19 for weeks or months after their initial recovery, it’s called post COVID-19 condition. It’s also known as long COVID. Post COVID-19 condition may occur in some people after infection.