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. 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
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    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
    Kuwait, Burundi, Yemen, Ecuador, Germany, Bolivia, Plurinational State of, Haiti, Ukraine, Gambia, Mexico
    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.

  3. s

    COVID-19 cases in Pacific Island Countries and Territories

    • pacific-data.sprep.org
    • pacificdata.org
    Updated Jul 18, 2025
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    SPC (2025). COVID-19 cases in Pacific Island Countries and Territories [Dataset]. https://pacific-data.sprep.org/dataset/covid-19-cases-pacific-island-countries-and-territories
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    application/vnd.sdmx.data+csv; labels=name; version=2; charset=utf-8Available download formats
    Dataset updated
    Jul 18, 2025
    Dataset provided by
    Pacific Data Hub
    Authors
    SPC
    License

    Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
    License information was derived automatically

    Area covered
    [165.50902632210722, [135.55075917519616, [145.2148808223921, 15.726687346843647], [210.56388888886144, -19.123740708466755], [154.5786207439524, 0.647280195324868], -7.496644800517004], -10.111897222222069], Micronesia, Kiribati
    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.

  4. o

    Coronavirus (Covid-19) Data in the United States

    • openicpsr.org
    • catalog.midasnetwork.us
    • +2more
    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. 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%

  6. d

    COVID-19 Vaccination Coverage, ZIP Code

    • catalog.data.gov
    • data.cityofchicago.org
    • +1more
    Updated Jul 12, 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
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    Dataset updated
    Jul 12, 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

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

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    • +1more
    Updated Dec 2, 2022
    + more versions
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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
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    Dataset updated
    Dec 2, 2022
    Dataset provided by
    United Nations High Commissioner for Refugeeshttp://www.unhcr.org/
    Durham University
    UN OCHA
    UN Global Pulse
    WHO
    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

  8. 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
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    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)

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

    • microdata.unhcr.org
    Updated Oct 3, 2021
    + more versions
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    UNHCR (2021). COVID-19 Vaccination Survey, July 2021 - China [Dataset]. https://microdata.unhcr.org/index.php/catalog/518
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    Dataset updated
    Oct 3, 2021
    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 implmented.

    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.

  10. 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.

  11. 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

  12. COVID-19 Trends in Each Country

    • data.amerigeoss.org
    esri rest, html
    Updated Jul 29, 2020
    + more versions
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    ESRI (2020). COVID-19 Trends in Each Country [Dataset]. https://data.amerigeoss.org/dataset/covid-19-trends-in-each-country
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    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

  13. Socioeconomic Impact of COVID-19, 2021 - Costa Rica

    • microdata.worldbank.org
    • microdata.unhcr.org
    • +2more
    Updated Dec 15, 2022
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    UNHCR (2022). Socioeconomic Impact of COVID-19, 2021 - Costa Rica [Dataset]. https://microdata.worldbank.org/index.php/catalog/5297
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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
    Costa Rica
    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. Two regions were targeted: the Greater Metropolitan Area and the Northern region. Two rounds of data collection took place for this survey, with the purpose of following up with the respondents.

    Analysis unit

    Household

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    UNHCR’s ProGres database in Costa Rica contains 47,491 registered individuals of which 30,494 are active. Of the 30,494 active individuals registered in the database, 22,487 have a known location as well as a listed primary phone number. Phone penetration rates are high among the PoC population in Costa Rica with 9 out of 10 families having a phone number registered in the ProGres database. This list constitutes just over 22% of the total estimated PoC population living in Costa Rica. As such, this final list serves as the first-choice sampling frame for the phone survey. In addition, two regions of Costa Rica were identified for targeted sampling of PoC following discussion with the UNHCR country team and regional bureau and based on information captured in the ProGres database. These include the Greater Metropolitan Area (GAM, for its acronym in Spanish) inclusive of the capital San Jose and the Northern region. Moreover, it was identified that understanding differences across sub-groups based on country of origin was essential for operational needs. In the GAM the biggest groups are Nicaraguans (67%), Venezuelans (13%) and Cubans (11%). Alternatively, in the North Nicaraguans represent 90% of the PoC population. Based on the above, a sampling strategy was proposed based on four separate strata in order to adequately represent the regions and sub-groups of interest: 1.)GAM – Nicaragua stratum: Nicaraguan PoC in GAM; 2.) GAM – Venezuela stratum: Venezuelan PoC in GAM; 3.) GAM – Cuba stratum: Cuban PoC in GAM; and 4.) North – Nicaragua stratum: Nicaraguan PoC in the North.

    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

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

    • kaggle.com
    Updated Mar 1, 2021
    + more versions
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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

  15. R

    G²LM|LIC - COVID-19 Returned Indian Migrant Panel

    • datasets.iza.org
    • dataverse.iza.org
    zip
    Updated Nov 12, 2023
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    Rohini Pande; Jenna Allard; Yusuf Neggers; Jagnani, Maulik; Simone Schaner; Rohini Pande; Jenna Allard; Yusuf Neggers; Jagnani, Maulik; Simone Schaner (2023). G²LM|LIC - COVID-19 Returned Indian Migrant Panel [Dataset]. http://doi.org/10.15185/glmlic.700.1
    Explore at:
    zip(2599183), zip(591433)Available download formats
    Dataset updated
    Nov 12, 2023
    Dataset provided by
    Research Data Center of IZA (IDSC)
    Authors
    Rohini Pande; Jenna Allard; Yusuf Neggers; Jagnani, Maulik; Simone Schaner; Rohini Pande; Jenna Allard; Yusuf Neggers; Jagnani, Maulik; Simone Schaner
    License

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

    Area covered
    India
    Description

    On March 24, 2020, the Indian Government announced a nationwide lockdown to curb the spread of Covid-19, effective with a few hours of notice. For an estimated 40 million migrant workers in the country, this resulted in loss of income, food shortages, and uncertainty about the future. Over 10 million returned to rural homes in one of the largest internal migrations in the country's history. Once returned, they faced stays in government-run quarantine centers, stigma, and uncertain labor prospects. Over the next year, migrants navigated shifting mobility restrictions aimed at mitigating the spread of the pandemic, widespread outbreaks, and patchwork of social protection schemes in order to make ends meet. In order to understand the long-term labor and well-being effects of the pandemic on this population, the research team conducted a panel survey across four rounds with a random sample of 8,265 migrants that had returned to Bihar and Chhattisgarh shortly after the nationwide lockdown in March 2020. The team constructed a post-lockdown sample frame drawing from the approximate population of returned migrants, drawing from government records that attempted to catalogue all entrants in a given time period. These phone surveys included a repeated set of questions on employment and earnings, migration, access to social protections, and coping strategies, as well as single-wave modules on quarantine experiences, health behaviors and beliefs, household composition, migration networks, and discrimination. The questionnaires focused on different aspects of welfare as the pandemic in India has evolved. The following list below details important topics of the surveys: Pre-Lockdown Work Details (Employment, Earnings) Experiences Post-Migration (Harassment, Food Prices, Shortages, Bank Accounts) Awareness and Perceptions of COVID-19 Migration Networks Social Networks Political Participation Impact of COVID-19

  16. 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

  17. COVID19 Refugee Household Monitoring, 2020 - Lebanon

    • microdata.unhcr.org
    Updated Jun 23, 2020
    + more versions
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    UNHCR (2020). COVID19 Refugee Household Monitoring, 2020 - Lebanon [Dataset]. https://microdata.unhcr.org/index.php/catalog/238
    Explore at:
    Dataset updated
    Jun 23, 2020
    Dataset provided by
    United Nations High Commissioner for Refugeeshttp://www.unhcr.org/
    Authors
    UNHCR
    Time period covered
    2020
    Area covered
    Lebanon
    Description

    Abstract

    UNHCR conducts Protection Monitoring with partners to analyze trends in the protection environment and situation of refugees in all regions of Lebanon on an ongoing basis. With the outbreak of COVID-19 in Lebanon and the introduction of movement and other restrictions aimed at preventing and containing the spread of the virus, UNHCR and its Protection Monitoring partners Caritas, Intersos and Sheild developed a specific questionnaire to elicit feedback from refugees on the impact of the COVID-19 response on their protection and well-being. The feedback from refugees is used to inform advocacy and programmatic interventions and modes of implementation with the aim of improving refugees' access to protection and essential services, assistance and information.

    Geographic coverage

    National coverage

    Analysis unit

    Households

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Each week, the caseload is sampled by Registration and sent to partners, who have a set target for calls to conduct each week.

    HHs are sampled by Registration to satisfy the following criteria: 1. Age and gender related criteria: 1. a20% Cases with individuals between 16 and 25 -50% male/female 1.b 30% Cases with individuals between 26 and 59 -50% male/female 1.c 10% Cases with individuals who are 60 and above -50%male/female

    1. Specific needs criteria 2.a 20% Cases with older person at risk 2.b 10% Cases with one or family members with disability 2.c 10% Cases with one or more family members with critical medical condition

    [note that this method results in sampling from overlapping stratas] When sampling and assigning cases to partners, Registration is also taking into account: 3. the geographical distribution of refugees [although note - sampling is not yet exactly proportional to refugee distribution, but efforts are being made it to make it more so) 4. partners' capacity to complete the desired interview volume 5. the non-response rate that each partner is getting (e.g. this week Intersos BML was given more cases as they have a higher non-response rate than average)

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

  18. COVID-19 Worldwide Daily Data

    • kaggle.com
    Updated Aug 28, 2020
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    Altadata (2020). COVID-19 Worldwide Daily Data [Dataset]. https://www.kaggle.com/altadata/covid19/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 28, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Altadata
    Description

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5505749%2F2b83271d61e47e2523e10dc9c28e545c%2F600x200.jpg?generation=1599042483103679&alt=media" alt="">

    ALTADATA is a curated data marketplace where our subscribers and our data partners can easily exchange ready-to-analyze datasets and create insights with EPO, our visual data analytics platform.

    COVID-19 Worldwide Daily Data

    Daily global COVID-19 data for all countries, provided by Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE). If you want to use the update version of the data, you can use our daily updated data with the help of api key by entering it via Altadata.

    Overview

    In this data product, you may find the latest and historical global daily data on the COVID-19 pandemic for all countries.

    The COVID‑19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in December 2019 in Wuhan, China. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March. As of 12 August 2020, more than 20.2 million cases of COVID‑19 have been reported in more than 188 countries and territories, resulting in more than 741,000 deaths; more than 12.5 million people have recovered.

    The Johns Hopkins Coronavirus Resource Center is a continuously updated source of COVID-19 data and expert guidance. They aggregate and analyze the best data available on COVID-19 - including cases, as well as testing, contact tracing and vaccine efforts - to help the public, policymakers and healthcare professionals worldwide respond to the pandemic.

    Methodology

    • Cases and Death counts include confirmed and probable (where reported)
    • Recovered cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project.
    • Active cases = total cases - total recovered - total deaths
    • Incidence Rate = cases per 100,000 persons
    • Case-Fatality Ratio (%) = Number recorded deaths / Number cases
    • Country Population represents 2019 projections by UN Population Division, integrated to the JHU CSSE's COVID-19 data by ALTADATA

    Data Source

    Related Data Products

    Suggested Blog Posts

    Data Dictionary

    • Reported Date (reported_date) : Covid-19 Report Date
    • Country_Region (country_region) : Country, region or sovereignty name
    • Population (population) : Country populations as per United Nations Population Division
    • Confirmed Case (confirmed) : Confirmed cases include presumptive positive cases and probable cases
    • Active cases (active) : Active cases = total confirmed - total recovered - total deaths
    • Deaths (deaths) : Death cases counts
    • Recovered (recovered) : Recovered cases counts
    • Mortality Rate (mortality_rate) : Number of recorded deaths * 100 / Number of confirmed cases
    • Incident Rate (incident_rate) : Confirmed cases per 100,000 persons
  19. o

    Data and Code for: Gendered Impacts of Covid-19 in Developing Countries

    • openicpsr.org
    delimited
    Updated Apr 29, 2022
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    Titan Alon; Matthias Doepke; Kristina Manysheva; Michele Tertilt (2022). Data and Code for: Gendered Impacts of Covid-19 in Developing Countries [Dataset]. http://doi.org/10.3886/E169142V1
    Explore at:
    delimitedAvailable download formats
    Dataset updated
    Apr 29, 2022
    Dataset provided by
    American Economic Association
    Authors
    Titan Alon; Matthias Doepke; Kristina Manysheva; Michele Tertilt
    License

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

    Description

    In many high-income economies, the recession caused by the Covid-19 pandemic has resulted in unprecedented declines in women’s employment. We examine how the forces that underlie this observation play out in developing countries, with a specific focus on Nigeria, the most populous country in Africa. A force affecting high- and low-income countries alike are increased childcare needs during school closures; in Nigeria, mothers of school-age children experience the largest declines in employment during the pandemic, just as in high-income countries. A key difference is the role of the sectoral distribution of employment: whereas in high-income economies reduced employment in contact-intensive services had a large impact on women, this sector plays a minor role in low-income countries. Another difference is that women’s employment rebounded much more quickly in low-income countries. We conjecture that large income losses without offsetting government transfers drive up labor supply in low-income countries during the recovery.

  20. h

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

    • healthdatagateway.org
    • find.data.gov.scot
    • +1more
    unknown
    Updated May 4, 2022
    + more versions
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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.

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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
Organization logo

COVID-19 Related Shocks Survey in Rural India 2020, Round 1 - India

Explore at:
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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