100+ datasets found
  1. COVID-19 Related Shocks Survey in Rural India 2020, Round 1 - India

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    • +1more
    Updated Jan 14, 2021
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    World Bank (2021). COVID-19 Related Shocks Survey in Rural India 2020, Round 1 - India [Dataset]. https://microdata.worldbank.org/index.php/catalog/3769
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    Dataset updated
    Jan 14, 2021
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    Time period covered
    2020
    Area covered
    India
    Description

    Abstract

    An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

    Geographic coverage

    Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

    Analysis unit

    Household

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The samples for these surveys were drawn from surveys and impact evaluations previously conducted by the World Bank, the Ministry of Rural Development, India and IDInsight. A detailed note on the sampling frames is available for download.

    Sampling deviation

    Details will be made available after all rounds of data collection and analysis is complete.

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Research instrument

    The survey questionnaire consists of the following modules: - Module 0: Introduction - Module 1: Migration - Module 2: Labor and Income - Module 3: Consumption - Module 4: Agriculture - Module 5: Access to Relief - Module 6: Health

    Response rate

    ~55%

  2. COVID-19-Related Shocks in Rural India 2020, Rounds 1-3 - India

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    • +1more
    Updated Jan 15, 2021
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    World Bank (2021). COVID-19-Related Shocks in Rural India 2020, Rounds 1-3 - India [Dataset]. https://microdata.worldbank.org/index.php/catalog/3830
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    Dataset updated
    Jan 15, 2021
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    Time period covered
    2020
    Area covered
    India
    Description

    Abstract

    An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

    Geographic coverage

    Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

    Analysis unit

    Household

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

    These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

    A detailed note covering key features of each sample frame is available for download.

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Research instrument

    The survey questionnaires covered the following subjects:

    1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

    2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

    3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

    4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

    5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

    While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

    Response rate

    Round 1: ~55% Round 2: ~46% Round 3: ~55%

  3. R

    WageIndicator Survey of Living and Working in Coronavirus Times

    • datasets.iza.org
    • dataverse.iza.org
    zip
    Updated Feb 21, 2024
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    Research Data Center of IZA (IDSC) (2024). WageIndicator Survey of Living and Working in Coronavirus Times [Dataset]. http://doi.org/10.15185/wif.corona.1
    Explore at:
    zip(1577392), zip(122268054)Available download formats
    Dataset updated
    Feb 21, 2024
    Dataset provided by
    Research Data Center of IZA (IDSC)
    License

    https://www.iza.org/wc/dataverse/IIL-1.0.pdfhttps://www.iza.org/wc/dataverse/IIL-1.0.pdf

    Area covered
    Gambia, Burundi, Kuwait, Haiti, Plurinational State of, Bolivia, Germany, Ukraine, Yemen, Mexico, Ecuador
    Description

    WageIndicator is interviewing people around the world to discover what makes the Coronavirus lockdown easier (or tougher), and what is the COVID-19 effect on our jobs, lives and mood. WageIndicator shows coronavirus-induced changes in living and working conditions in over 110 countries on the basis of answers on the following questions among others in the Corona survey: Is your work affected by the corona crisis? Are precautionary measures taken at the workplace? Do you have to work from home? Has your workload increased/decreased? Have you lost your job/work/assignments? The survey contains questions about the home situation of respondents as well as about the possible manifestation of the corona disease in members of the household. Also the effect of having a pet in the house in corona-crisis times is included.

  4. o

    Coronavirus (Covid-19) Data in the United States

    • openicpsr.org
    Updated Dec 7, 2020
    + more versions
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    New York Times (2020). Coronavirus (Covid-19) Data in the United States [Dataset]. http://doi.org/10.3886/E128303V1
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    Dataset updated
    Dec 7, 2020
    Dataset authored and provided by
    New York Times
    Time period covered
    Jan 21, 2020 - Nov 22, 2020
    Area covered
    United States
    Description

    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. This time series data is being compiled from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak. This deposit contains live data from three geographic levels: U.S., states and counties. ICPSR staff scraped these data on 11/22/2020. For the most current data, please visit https://github.com/nytimes/covid-19-data.

  5. g

    Coronavirus (Covid-19) Data in the United States

    • github.com
    • openicpsr.org
    • +2more
    csv
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://github.com/nytimes/covid-19-data
    Explore at:
    csvAvailable download formats
    Dataset provided by
    New York Times
    License

    https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE

    Description

    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.

  6. o

    Covid19Kerala.info-Data: A collective open dataset of COVID-19 outbreak in...

    • explore.openaire.eu
    • data.niaid.nih.gov
    • +1more
    Updated May 9, 2020
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    Jijo Ulahannan; Nikhil Narayanan; Sooraj P. Sooraj P Suresh; Nishad Thalhath; Sreekanth Chaliyeduth; Prem Prabhakaran; Jeevan Uthaman; Akhil Balakrishnan; Sharadh Manian; Manoj Karingamadathil; Shabeesh Balan; Musfir Mohammed; Neetha Nanoth Vellichirammal; E. E Rajeevan; Sindhu Joseph; Sreehari Pillai; Unnikrishnan Sureshkumar; Kumar Sujith (2020). Covid19Kerala.info-Data: A collective open dataset of COVID-19 outbreak in the south Indian state of Kerala [Dataset]. http://doi.org/10.5281/zenodo.3841917
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    Dataset updated
    May 9, 2020
    Authors
    Jijo Ulahannan; Nikhil Narayanan; Sooraj P. Sooraj P Suresh; Nishad Thalhath; Sreekanth Chaliyeduth; Prem Prabhakaran; Jeevan Uthaman; Akhil Balakrishnan; Sharadh Manian; Manoj Karingamadathil; Shabeesh Balan; Musfir Mohammed; Neetha Nanoth Vellichirammal; E. E Rajeevan; Sindhu Joseph; Sreehari Pillai; Unnikrishnan Sureshkumar; Kumar Sujith
    Area covered
    South India, Kerala
    Description

    Covid19Kerala.info-Data is a consolidated multi-source open dataset of metadata from the COVID-19 outbreak in the Indian state of Kerala. It is created and maintained by volunteers of ‘Collective for Open Data Distribution-Keralam’ (CODD-K), a nonprofit consortium of individuals formed for the distribution and longevity of open-datasets. Covid19Kerala.info-Data covers a set of correlated temporal and spatial metadata of SARS-CoV-2 infections and prevention measures in Kerala. Static releases of this dataset snapshots are manually produced from a live database maintained as a set of publicly accessible Google sheets. This dataset is made available under the Open Data Commons Attribution License v1.0 (ODC-BY 1.0). Schema and data package Datapackage with schema definition is accessible at https://codd-k.github.io/covid19kerala.info-data/datapackage.json. Provided datapackage and schema are based on Frictionless data Data Package specification. Temporal and Spatial Coverage This dataset covers COVID-19 outbreak and related data from the state of Kerala, India, from January 31, 2020 till the date of the publication of this snapshot. The dataset shall be maintained throughout the entirety of the COVID-19 outbreak. The spatial coverage of the data lies within the geographical boundaries of the Kerala state which includes its 14 administrative subdivisions. The state is further divided into Local Self Governing (LSG) Bodies. Reference to this spatial information is included on appropriate data facets. Available spatial information on regions outside Kerala was mentioned, but it is limited as a reference to the possible origins of the infection clusters or movement of the individuals. Longevity and Provenance The dataset snapshot releases are published and maintained in a designated GitHub repository maintained by CODD-K team. Periodic snapshots from the live database will be released at regular intervals. The GitHub commit logs for the repository will be maintained as a record of provenance, and archived repository will be maintained at the end of the project lifecycle for the longevity of the dataset. Data Stewardship CODD-K expects all administrators, managers, and users of its datasets to manage, access, and utilize them in a manner that is consistent with the consortium’s need for security and confidentiality and relevant legal frameworks within all geographies, especially Kerala and India. As a responsible steward to maintain and make this dataset accessible— CODD-K absolves from all liabilities of the damages, if any caused by inaccuracies in the dataset. License This dataset is made available by the CODD-K consortium under ODC-BY 1.0 license. The Open Data Commons Attribution License (ODC-By) v1.0 ensures that users of this dataset are free to copy, distribute and use the dataset to produce works and even to modify, transform and build upon the database, as long as they attribute the public use of the database or works produced from the same, as mentioned in the citation below. Disclaimer Covid19Kerala.info-Data is provided under the ODC-BY 1.0 license as-is. Though every attempt is taken to ensure that the data is error-free and up to date, the CODD-K consortium do not bear any responsibilities for inaccuracies in the dataset or any losses—monetary or otherwise—that users of this dataset may incur. {"references": ["A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India Jijo Pulickiyil Ulahannan, NIkhil Narayanan, Nishad Thalhath, Prem Prabhakaran, Sreekanth Chaliyeduth, Sooraj P Suresh, Musfir Mohammed, Rajeevan E, Sindhu Joseph, Akhil Balakrishnan, Jeevan Uthaman, Manoj Karingamadathil, Sunil Thonikkuzhiyil Thomas, Unnikrishnan Sureshkumar, Shabeesh Balan, Neetha Nanoth Vellichirammal medRxiv 2020.05.13.20092510; doi: https://doi.org/10.1101/2020.05.13.20092510"]}

  7. P

    COVID-19 cases in Pacific Island Countries and Territories

    • pacificdata.org
    • pacific-data.sprep.org
    csv
    Updated Jun 3, 2024
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    SPC (2024). COVID-19 cases in Pacific Island Countries and Territories [Dataset]. https://pacificdata.org/data/dataset/covid-19-cases-in-pacific-island-countries-and-territories-df-covid
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 3, 2024
    Dataset provided by
    SPC
    Time period covered
    Jan 1, 2020 - May 31, 2024
    Description

    Statistics from SPC's Public Health Division (PHD) on the number of cases of COVID-19 and the number of deaths attributed to COVID-19 in Pacific Island Countries and Territories.

    Find more Pacific data on PDH.stat.

  8. Up-to-date mapping of COVID-19 treatment and vaccine development...

    • zenodo.org
    • data.niaid.nih.gov
    bin, csv, png
    Updated Jul 19, 2024
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    Tomáš Wagner; Ivana Mišová; Ivana Mišová; Ján Frankovský; Ján Frankovský; Tomáš Wagner (2024). Up-to-date mapping of COVID-19 treatment and vaccine development (covid19-help.org data dump) [Dataset]. http://doi.org/10.5281/zenodo.4601446
    Explore at:
    csv, png, binAvailable download formats
    Dataset updated
    Jul 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tomáš Wagner; Ivana Mišová; Ivana Mišová; Ján Frankovský; Ján Frankovský; Tomáš Wagner
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The free database mapping COVID-19 treatment and vaccine development based on the global scientific research is available at https://covid19-help.org/.

    Files provided here are curated partial data exports in the form of .csv files or full data export as .sql script generated with pg_dump from our PostgreSQL 12 database. You can also find .png file with our ER diagram of tables in .sql file in this repository.

    Structure of CSV files

    *On our site, compounds are named as substances

    compounds.csv

    1. Id - Unique identifier in our database (unsigned integer)

    2. Name - Name of the Substance/Compound (string)

    3. Marketed name - The marketed name of the Substance/Compound (string)

    4. Synonyms - Known synonyms (string)

    5. Description - Description (HTML code)

    6. Dietary sources - Dietary sources where the Substance/Compound can be found (string)

    7. Dietary sources URL - Dietary sources URL (string)

    8. Formula - Compound formula (HTML code)

    9. Structure image URL - Url to our website with the structure image (string)

    10. Status - Status of approval (string)

    11. Therapeutic approach - Approach in which Substance/Compound works (string)

    12. Drug status - Availability of Substance/Compound (string)

    13. Additional data - Additional data in stringified JSON format with data as prescribing information and note (string)

    14. General information - General information about Substance/Compound (HTML code)

    references.csv

    1. Id - Unique identifier in our database (unsigned integer)

    2. Impact factor - Impact factor of the scientific article (string)

    3. Source title - Title of the scientific article (string)

    4. Source URL - URL link of the scientific article (string)

    5. Tested on species - What testing model was used for the study (string)

    6. Published at - Date of publication of the scientific article (Date in ISO 8601 format)

    clinical-trials.csv

    1. Id - Unique identifier in our database (unsigned integer)

    2. Title - Title of the clinical trial study (string)

    3. Acronym title - Acronym of title of the clinical trial study (string)

    4. Source id - Unique identifier in the source database

    5. Source id optional - Optional identifier in other databases (string)

    6. Interventions - Description of interventions (string)

    7. Study type - Type of the conducted study (string)

    8. Study results - Has results? (string)

    9. Phase - Current phase of the clinical trial (string)

    10. Url - URL to clinical trial study page on clinicaltrials.gov (string)

    11. Status - Status in which study currently is (string)

    12. Start date - Date at which study was started (Date in ISO 8601 format)

    13. Completion date - Date at which study was completed (Date in ISO 8601 format)

    14. Additional data - Additional data in the form of stringified JSON with data as locations of study, study design, enrollment, age, outcome measures (string)

    compound-reference-relations.csv

    1. Reference id - Id of a reference in our DB (unsigned integer)

    2. Compound id - Id of a substance in our DB (unsigned integer)

    3. Note - Id of a substance in our DB (unsigned integer)

    4. Is supporting - Is evidence supporting or contradictory (Boolean, true if supporting)

    compound-clinical-trial.csv

    1. Clinical trial id - Id of a clinical trial in our DB (unsigned integer)

    2. Compound id - Id of a Substance/Compound in our DB (unsigned integer)

    tags.csv

    1. Id - Unique identifier in our database (unsigned integer)

    2. Name - Name of the tag (string)

    tags-entities.csv

    1. Tag id - Id of a tag in our DB (unsigned integer)

    2. Reference id - Id of a reference in our DB (unsigned integer)

    API Specification

    Our project also has an Open API that gives you access to our data in a format suitable for processing, particularly in JSON format.

    https://covid19-help.org/api-specification

    Services are split into five endpoints:

    • Substances - /api/substances

    • References - /api/references

    • Substance-reference relations - /api/substance-reference-relations

    • Clinical trials - /api/clinical-trials

    • Clinical trials-substances relations - /api/clinical-trials-substances

    Method of providing data

    • All dates are text strings formatted in compliance with ISO 8601 as YYYY-MM-DD

    • If the syntax request is incorrect (missing or incorrectly formatted parameters) an HTTP 400 Bad Request response will be returned. The body of the response may include an explanation.

    • Data updated_at (used for querying changed-from) refers only to a particular entity and not its logical relations. Example: If a new substance reference relation is added, but the substance detail has not changed, this is reflected in the substance reference relation endpoint where a new entity with id and current dates in created_at and updated_at fields will be added, but in substances or references endpoint nothing has changed.

    The recommended way of sequential download

    • During the first download, it is possible to obtain all data by entering an old enough date in the parameter value changed-from, for example: changed-from=2020-01-01 It is important to write down the date on which the receiving the data was initiated let’s say 2020-10-20

    • For repeated data downloads, it is sufficient to receive only the records in which something has changed. It can therefore be requested with the parameter changed-from=2020-10-20 (example from the previous bullet). Again, it is important to write down the date when the updates were downloaded (eg. 2020-10-20). This date will be used in the next update (refresh) of the data.

    Services for entities

    List of endpoint URLs:

    Format of the request

    All endpoints have these parameters in common:

    • changed-from - a parameter to return only the entities that have been modified on a given date or later.

    • continue-after-id - a parameter to return only the entities that have a larger ID than specified in the parameter.

    • limit - a parameter to return only the number of records specified (up to 1000). The preset number is 100.

    Request example:

    /api/references?changed-from=2020-01-01&continue-after-id=1&limit=100

    Format of the response

    The response format is the same for all endpoints.

    • number_of_remaining_ids - the number of remaining entities that meet the specified criteria but are not displayed on the page. An integer of virtually unlimited size.

    • entities - an array of entity details in JSON format.

    Response example:

    {

    "number_of_remaining_ids" : 100,

    "entities" : [

    {

    "id": 3,

    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32147628",

    "title": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",

    "impact_factor": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",

    "tested_on_species": "in silico",

    "publication_date": "2020-22-02",

    "created_at": "2020-30-03",

    "updated_at": "2020-31-03",

    "deleted_at": null

    },

    {

    "id": 4,

    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32157862",

    "title": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",

    "impact_factor": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",

    "tested_on_species": "Patient",

    "publication_date": "2020-06-03",

    "created_at": "2020-30-03",

    "updated_at": "2020-30-03",

    "deleted_at": null

    },

    ]

    }

    Endpoint details

    Substances

    URL: /api/substances

    Substances

  9. COVID-19 Case Surveillance Public Use Data

    • data.cdc.gov
    • paperswithcode.com
    • +5more
    application/rdfxml +5
    Updated Jul 9, 2024
    + more versions
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    CDC Data, Analytics and Visualization Task Force (2024). COVID-19 Case Surveillance Public Use Data [Dataset]. https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data/vbim-akqf
    Explore at:
    application/rdfxml, tsv, csv, json, xml, application/rssxmlAvailable download formats
    Dataset updated
    Jul 9, 2024
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    CDC Data, Analytics and Visualization Task Force
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

    Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

    This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

    CDC has three COVID-19 case surveillance datasets:

    The following apply to all three datasets:

    Overview

    The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

    For more information: NNDSS Supports the COVID-19 Response | CDC.

    The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

    COVID-19 Case Reports

    COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

    All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

    Data are Considered Provisional

    • The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
    • Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
    • Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

    Data Limitations

    To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

    Data Quality Assurance Procedures

    CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

    • Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
    • Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
    • Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

    Data Suppression

    To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

    For questions, please contact Ask SRRG (eocevent394@cdc.gov).

    Additional COVID-19 Data

    COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

  10. d

    COVID-19 Vaccination Coverage, ZIP Code

    • catalog.data.gov
    • data.cityofchicago.org
    • +1more
    Updated Jul 19, 2025
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    data.cityofchicago.org (2025). COVID-19 Vaccination Coverage, ZIP Code [Dataset]. https://catalog.data.gov/dataset/covid-19-vaccination-coverage-zip-code
    Explore at:
    Dataset updated
    Jul 19, 2025
    Dataset provided by
    data.cityofchicago.org
    Description

    NOTE: This dataset replaces a previous one. Please see below. Chicago residents who are up to date with COVID-19 vaccines by ZIP Code, based on the reported home address and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). “Up to date” refers to individuals who meet the CDC’s updated COVID-19 vaccination criteria based on their age and prior vaccination history. For surveillance purposes, up to date is defined based on the following criteria: People ages 5 years and older: · Are up to date when they receive 1+ doses of a COVID-19 vaccine during the current season. Children ages 6 months to 4 years: · Children who have received at least two prior COVID-19 vaccine doses are up to date when they receive one additional dose of COVID-19 vaccine during the current season, regardless of vaccine product. · Children who have received only one prior COVID-19 vaccine dose are up to date when they receive one additional dose of the current season's Moderna COVID-19 vaccine or two additional doses of the current season's Pfizer-BioNTech COVID-19 vaccine. · Children who have never received a COVID-19 vaccination are up to date when they receive either two doses of the current season's Moderna vaccine or three doses of the current season's Pfizer-BioNTech vaccine. This dataset takes the place of a previous dataset, which covers doses administered from December 15, 2020 through September 13, 2023 and is marked as historical: - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccinations-by-ZIP-Code/553k-3xzc. Data Notes: Weekly cumulative totals of people up to date are shown for each combination ZIP Code and age group. Note there are rows where age group is "All ages" so care should be taken when summing rows. Coverage percentages are calculated based on the cumulative number of people in each ZIP Code and age group who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. For ZIP Codes mostly outside Chicago, coverage percentages are not calculated reliable Chicago-only population counts are not available. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller ZIP Codes with smaller populations. Additionally, the medical provider may report a work address or incorrect home address for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage by geography. All coverage percentages are capped at 99%. Weekly cumulative counts and coverage percentages are reported from the week ending Saturday, September 16, 2023 onward through the Saturday prior to the dataset being updated. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. The Chicago Department of Public Health uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Individuals may receive vaccinations that are not recorded in the Illinois immunization registry, I-CARE, such as those administered in another state, causing underestimation of the number individuals who are up to date. Inconsistencies in records of separate doses administered to the same person, such as slight variations in dates of birth, can result in duplicate records for a person and underestimate the number of people who are up to date. For all datasets related to COVID-19, please

  11. COVID-19 Exposure and Protective Measures, 2020 - Bangladesh

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    • +1more
    Updated Dec 2, 2022
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    UN Global Pulse (2022). COVID-19 Exposure and Protective Measures, 2020 - Bangladesh [Dataset]. https://microdata.worldbank.org/index.php/catalog/5192
    Explore at:
    Dataset updated
    Dec 2, 2022
    Dataset provided by
    United Nations High Commissioner for Refugeeshttp://www.unhcr.org/
    Durham University
    WHO
    UN OCHA
    UN Global Pulse
    Time period covered
    2020
    Area covered
    Bangladesh
    Description

    Abstract

    This dataset was collected as a complement to UN Global Pulse, UNHCR, Durham University, WHO and OCHA's study on simulation models to help with COVID-19 planning in world’s largest refugee settlement. The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. This survey collected data on individual's contact, interactions and time spent in public zones of refugees' camps in Cox's Bazar, in order to fill spreading matrices to inform this simulation of spread.

    Geographic coverage

    Cox's Bazar

    Analysis unit

    Individuals

    Universe

    All participants of Community Based Protection Groups

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample frame was obtained from lists of Community-Based Protection regular working groups. Each camp group was stratified by gender, age and disabilities, and members of each camp were randomly selected from the working groups of 20 camps in Cox's Bazar.

    Mode of data collection

    Telephone interview

  12. d

    COVID-19 Vaccine Delivery - Historical

    • catalog.data.gov
    • data.cityofchicago.org
    • +1more
    Updated Dec 2, 2023
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    data.cityofchicago.org (2023). COVID-19 Vaccine Delivery - Historical [Dataset]. https://catalog.data.gov/dataset/covid-19-vaccine-delivery
    Explore at:
    Dataset updated
    Dec 2, 2023
    Dataset provided by
    data.cityofchicago.org
    Description

    Note: This dataset is historical only. -- COVID-19 vaccine delivered to providers in the City of Chicago. Daily counts are shown for the total number of doses delivered as well as cumulative totals as of that date. Data are updated Monday to Friday. As of the launch of this dataset, weekend deliveries are unusual but will be added to the appropriate date (i.e., Saturday or Sunday) the following Monday if they occur. All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For information about the number of vaccine doses administered by Chicago providers, see https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Vaccinations-Administered-in-Chicag/4564-ixr2. For information about the number of vaccine doses administered to Chicago residents and number of residents considered fully vaccinated regardless of if they were vaccinated in Chicago, see https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Vaccinations-Chicago-Residents/2vhs-cf6b. For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19. Data Sources: Vaccine Tracking System (VTrcks)

  13. COVID19-Dataset-with-100-World-Countries

    • kaggle.com
    Updated Mar 1, 2021
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    Sami Belkacem (2021). COVID19-Dataset-with-100-World-Countries [Dataset]. https://www.kaggle.com/sambelkacem/covid19-algeria-and-world-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 1, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sami Belkacem
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    World
    Description

    COVID19-Algeria-and-World-Dataset

    A coronavirus dataset with 104 countries constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the COVID-19. The assumptions for the different factors are as follows:

    • Geography: some continents/areas may be more affected by the disease
    • Climate: cold temperatures may promote the spread of the virus
    • Healthcare: lack of hospital beds/doctors may lead to more human losses
    • Economy: weak economies (GDP) have fewer means to fight the disease
    • Demography: older populations may be at higher risk of the disease

    The last column represents the number of daily tests performed and the total number of cases and deaths reported each day.

    Data description

    https://raw.githubusercontent.com/SamBelkacem/COVID19-Algeria-and-World-Dataset/master/Images/Data%20description.png">

    Countries in the dataset by geographic coordinates

    https://raw.githubusercontent.com/SamBelkacem/COVID19-Algeria-and-World-Dataset/master/Images/Countries%20by%20geographic%20coordinates.png">

    • Europe: 33 countries
    • Asia: 28 countries
    • Africa: 21 countries
    • North America: 11 countries
    • South America: 8 countries
    • Oceania: 3 countries

    Statistical description of the data

    https://raw.githubusercontent.com/SamBelkacem/COVID19-Algeria-and-World-Dataset/master/Images/Statistical%20description%20of%20the%20data.png">

    Data distribution

    https://raw.githubusercontent.com/SamBelkacem/COVID19-Algeria-and-World-Dataset/master/Images/Data%20distribution.png">

    Download

    The dataset is available in an encoded CSV form on GitHub.

    Python code

    The Python Jupyter Notebook to read and visualize the data is available on nbviewer.

    Data update

    The dataset is updated every month with the latest numbers of COVID-19 cases, deaths, and tests. The last update was on March 01, 2021.

    Data construction

    The dataset is constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the coronavirus. Note that we selected only the main factors for which we found data and that other factors can be used. All data were retrieved from the reliable Our World in Data website, except for data on:

    Citation

    If you want to use the dataset please cite the following arXiv paper, more details about the data construction are provided in it.

    @article{belkacem_covid-19_2020,
      title = {COVID-19 data analysis and forecasting: Algeria and the world},
      shorttitle = {COVID-19 data analysis and forecasting},
      journal = {arXiv preprint arXiv:2007.09755},
      author = {Belkacem, Sami},
      year = {2020}
    }
    

    Contact

    If you have any question or suggestion, please contact me at this email address: s.belkacem@usthb.dz

  14. COVID-19 Vaccination Survey, July 2021 - China

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Dec 2, 2022
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    UNHCR (2022). COVID-19 Vaccination Survey, July 2021 - China [Dataset]. https://microdata.worldbank.org/index.php/catalog/5190
    Explore at:
    Dataset updated
    Dec 2, 2022
    Dataset provided by
    United Nations High Commissioner for Refugeeshttp://www.unhcr.org/
    Authors
    UNHCR
    Time period covered
    2021
    Area covered
    China
    Description

    Abstract

    The COVID-19 Vaccination Survey in China was conducted in July 2021 to understand refugees' accessibility and willingness to receive a COVID-19 vaccination in China. UNHCR stresses that no one can be left behind in the global effort against COVID-19 and is monitoring the inclusion of refugees and asylum seekers in vaccination plans around the world. At the time, Chinese government policy did not provide free vaccines for foreigners without social security. The survey results however show that this policy was implemented with some flexibility, because among the few that were vaccinated already, more than half received a free COVID-19 vaccine. Some refugees reported difficulties or lack of information about vaccine registration or identity documents to book an appointment. Results further show that even though most are willing to get vaccinated, anti-vaccine sentiments are driven by fear of side effects.

    Geographic coverage

    The survey covers 24 provinces with most respondents residing in the province of Guangdong.

    Analysis unit

    Households

    Universe

    The survey was distributed to all 1017 refugees and asylum seekers.

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    No sampling was implemented.

    Mode of data collection

    Self-administered questionnaire: Web-based

    Response rate

    Out of 1017 distributed surveys, UNHCR received 455 answers (45%). Of those, 30 respondents did not provide consent to participate in the survey.

  15. Covid-19 Hazard & Exposure

    • kaggle.com
    Updated Sep 27, 2020
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    Marília Prata (2020). Covid-19 Hazard & Exposure [Dataset]. https://www.kaggle.com/mpwolke/cusersmarildownloadshazardcsv/activity
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 27, 2020
    Dataset provided by
    Kaggle
    Authors
    Marília Prata
    Description

    Context

    The INFORM Epidemic Risk Index is highly relevant, easily adapted and was developed through an extensive process prior to COVID-19. Therefore it has been used as the starting point for a COVID-19 specific risk index, with the structure and relevant indicators retained as far as possible.

    Content

    The INFORM Epidemic Risk Index consists of Hazard & Exposure, Vulnerability and Lack of Coping Capacity dimensions. The Person to Person component of Hazard & Exposure is the most relevant to COVID-19 and is used alone.

    Acknowledgements

    https://data.humdata.org/dataset/inform-covid-19-risk-index-version-0-1-2

    Photo by Troy Bridges on Unsplash

    Inspiration

    Covid-19 Pandemic.

  16. h

    Risk and vulnerabilities variables related to COVID-19 in Brazil - PAMEpi...

    • healthdatagateway.org
    • find.data.gov.scot
    • +1more
    unknown
    Updated May 4, 2022
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    (2022). Risk and vulnerabilities variables related to COVID-19 in Brazil - PAMEpi data [Dataset]. http://doi.org/10.5281/zenodo.6385254
    Explore at:
    unknownAvailable download formats
    Dataset updated
    May 4, 2022
    License

    https://pamepi.rondonia.fiocruz.br/en/covid_en.html;,;https://opendatasus.saude.gov.br/dataset/srag-2021-e-2022;,;https://opendatasus.saude.gov.br/dataset/srag-2020https://pamepi.rondonia.fiocruz.br/en/covid_en.html;,;https://opendatasus.saude.gov.br/dataset/srag-2021-e-2022;,;https://opendatasus.saude.gov.br/dataset/srag-2020

    Description

    The data includes demographic, clinical, and socioeconomic variables of hospitalised SRAS-CoV-2 infections in Brazil from February 2020 to November 2021 and was primarily prepared for use in the analysis performed in our titled manuscript "Profile of COVID-19 in Brazil: Risk factors and socioeconomic vulnerability associated with disease outcome", currently available as a preprint. The raw data can be freely downloaded directly at the OpenData SUS website (Link https://opendatasus.saude.gov.br/dataset/srag-2020 and https://opendatasus.saude.gov.br/dataset/srag-2021-e-2022) or through a Python code available at our GitHub directory https://github.com/PAMepi/PAMepi_scripts_datalake.git.

    The data process to obtain the specific data described here is available at https://github.com/PAMepi/PAMEpi-Reproducibility-of-published-results.git.

    This work can be cited as: 1. Platform For Analytical Models in Epidemiology. (2022). PAMEpi-Reproducibility-of-published-results (v1.0). Zenodo. https://doi.org/10.5281/zenodo.6385254. or 2. Pereira, Felipe AC, Arthur R. de Azevedo, Guilherme L. de Oliveira, Renzo Flores-Ortiz, Luis Iván O. Valencia, Moreno Rodrigues, Pablo IP Ramos, Nívea B. da Silva, and Juliane Fonseca Oliveira. "Profile of COVID-19 in Brazil: Risk Factors and Socioeconomic Vulnerability Associated with Disease Outcome." Available at SSRN 4081979.

  17. k

    Saudi Arabia Coronavirus disease (COVID-19) situation – Demographics

    • datasource.kapsarc.org
    • data.kapsarc.org
    Updated Mar 13, 2024
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    (2024). Saudi Arabia Coronavirus disease (COVID-19) situation – Demographics [Dataset]. https://datasource.kapsarc.org/explore/dataset/saudi-arabia-coronavirus-disease-covid-19-situation-demographics/
    Explore at:
    Dataset updated
    Mar 13, 2024
    Area covered
    Saudi Arabia
    Description

    COVID-19 situation in Saudi Arabia collected from MOH daily reports https://twitter.com/SaudiMOH Explore the latest data on the COVID-19 situation and demographics in Saudi Arabia. This dataset provides valuable insights into the impact of the pandemic within the country. Follow data.kapsarc.org for timely data to advance energy economics research.

    COVID-19 Saudi Arabia

  18. w

    COVID-19 High Frequency Phone Survey 2020 - Chad

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated May 25, 2022
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    Institut National de la Statistique, des Etudes Economiques et Démographiques (INSEED) (2022). COVID-19 High Frequency Phone Survey 2020 - Chad [Dataset]. https://microdata.worldbank.org/index.php/catalog/3792
    Explore at:
    Dataset updated
    May 25, 2022
    Dataset authored and provided by
    Institut National de la Statistique, des Etudes Economiques et Démographiques (INSEED)
    Time period covered
    2020 - 2021
    Area covered
    Chad
    Description

    Abstract

    In Chad, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) in Chad.

    This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 30 minutes questionnaires covered many modules, including knowledge, behavior, access to services, food security, employment, safety nets, shocks, coping, etc. Data collection is planned for four months (four rounds) and the questionnaire is designed with core modules and rotating modules.

    The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

    Geographic coverage

    National coverage, including Ndjamena (Capital city), other urban and rural

    Analysis unit

    • Households
    • Individuals

    Universe

    The survey covered only households of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (ECOSIT 4) which excluded populations in prisons, hospitals, military barracks, and school dormitories.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The Chad COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) which was conducted in 2018-2019. ECOSIT 4 is a survey with a sample size of 7,493 household’s representative at national, regional and by urban/rural. During the survey, each household was asked to provide a phone number of at least one member or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. The sampling of the high frequency survey aimed at having representative estimates by national and area of residence: Ndjamena (capital city), other urban and rural area. The minimum sample size was 2,000 for which 1,748 households (87.5%) were successfully interviewed at the national level. To account for non-response and attrition and given that this survey was the first experience of INSEED, 2,833households were initially selected, among them 1,832 households have been reached. The 1,748 households represent the final sample and will be contacted for the next three rounds of the survey.

    Sampling deviation

    None

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    The questionnaire is in French and has been administrated in French and local languages. The length of an interview varies between 20 and 30 minutes. The questionnaires consisted of the following sections: 1- Household Roster 2- Knowledge of COVID-19 3- Behavior and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12. Gender based Violence (for the fourth wave) 13. Vaccine (for the fourth wave)

    Cleaning operations

    At the end of data collection, the raw dataset was cleaned by the INSEED with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

    Response rate

    The minimum sample expected is 2,000 households covering Ndjamena, other urban and rural areas. Overall, the survey has been completed for 1,748 households that is about 87.5 % of the expected minimal sample size at the national level. This provide reliable estimates at national and area of residence level.

  19. COVID-19 Trends in Each Country

    • data.amerigeoss.org
    esri rest, html
    Updated Jul 29, 2020
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    ESRI (2020). COVID-19 Trends in Each Country [Dataset]. https://data.amerigeoss.org/dataset/covid-19-trends-in-each-country
    Explore at:
    html, esri restAvailable download formats
    Dataset updated
    Jul 29, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Description

    COVID-19 Trends Methodology
    Our 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.


    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 sections
    Revisions 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 6/1/2020
    Discussion 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 Summary
    COVID-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:
    1. 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.
    2. 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.
    3. 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.
    <br

  20. Socioeconomic Impact of COVID-19, 2021 - Mexico

    • microdata.worldbank.org
    • microdata.unhcr.org
    • +1more
    Updated Dec 15, 2022
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    UNHCR (2022). Socioeconomic Impact of COVID-19, 2021 - Mexico [Dataset]. https://microdata.worldbank.org/index.php/catalog/5307
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    Dataset updated
    Dec 15, 2022
    Dataset provided by
    United Nations High Commissioner for Refugeeshttp://www.unhcr.org/
    Authors
    UNHCR
    Time period covered
    2021
    Area covered
    Mexico
    Description

    Abstract

    The COVID-19 pandemic is first and foremost a health shock, but the secondary economic shock is equally formidable. Access to timely, policy-relevant information on the awareness of, responses to and impacts of the health situation and related restrictions are critical to effectively design, target and evaluate programme and policy interventions. This research project investigates the main socioeconomic impacts of the pandemic on UNHCR people of concern (PoC) – and nationals where possible – in terms of access to information, services and livelihoods opportunities. Three geographic regions were taken into consideration: Southern Mexico, Mexico City and the Northern and Central Industrial Corridor. Two rounds of data collection took place for this survey, with the purpose of following up with the respondents.

    Geographic coverage

    Southern Mexico, Mexico City, Northern and Central Mexico

    Analysis unit

    Household

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The ProGres database served as the sampling frame due to the unavailability of other reliable sources. Likewise, the sample was stratified by location and population groups based on country of origin helping to account for the different economic realities from one part of the country to another, as well as differences between nationalities. Following discussion with the UNHCR country team and regional bureau, three geographic regions were presented for consideration : a) Southern Mexico; b) Mexico City; and c) the Northern and Central Industrial Corridor. Additionally, partners expressed interest in the Venezuelan community as a separate group, primarily residing in Mexico City, Monterrey and Cancun. The population of the four groups represents 67% of the active registered refugees in Mexico. Out of the 35,140 refugee households in the four regions, 26,688 families have at least one phone number representing an overall high rate of phone penetration. Across regions of interest, Hondurans make up the single largest group of PoC in Southern Mexico (38%), and the Northern and Central Industrial Corridor (43%), whereas Venezuelans make up over half of the PoC population in Mexico City (52%). Based on the above, a sampling strategy based on four separate strata was proposed in order to adequately represent the regions and sub-groups of interest: 1. Southern Mexico – Honduran and El Salvadoran PoC population 2. Mexico City – Honduran, El Salvadoran and Cuban PoC population 3. Northern and Central Industrial Corridor – Hondurans and El Salvadoran PoC population 4. Venezuelan Population – Mexico City, Monterey (Nuevo Leon) and Cancun (Quintana Roo) A comparable sub-sample of the national population in the same locations PoC were sampled was also generated using random digit dialing (RDD). This was made possible through the inclusion of location-based area codes in the list of phone numbers, however selected participants were also asked about their current location as a first filter to proceed with the phone survey to ensure a comparable national sub-sample.

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Research instrument

    Questionnaire contained the following sections: consent, knowledge, behaviour, access, employment, income, food security, concerns, resilience, networks, demographics

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World Bank (2021). COVID-19 Related Shocks Survey in Rural India 2020, Round 1 - India [Dataset]. https://microdata.worldbank.org/index.php/catalog/3769
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COVID-19 Related Shocks Survey in Rural India 2020, Round 1 - India

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3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jan 14, 2021
Dataset authored and provided by
World Bankhttp://worldbank.org/
Time period covered
2020
Area covered
India
Description

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for these surveys were drawn from surveys and impact evaluations previously conducted by the World Bank, the Ministry of Rural Development, India and IDInsight. A detailed note on the sampling frames is available for download.

Sampling deviation

Details will be made available after all rounds of data collection and analysis is complete.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following modules: - Module 0: Introduction - Module 1: Migration - Module 2: Labor and Income - Module 3: Consumption - Module 4: Agriculture - Module 5: Access to Relief - Module 6: Health

Response rate

~55%

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