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%

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%

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.

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.

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.

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"]}

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.

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:

• /api/substances

• /api/references

• /api/substance-reference-relations

• /api/clinical-trials

• /api/clinical-trials-substances

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

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:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

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

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

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

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)

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

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Countries in the dataset by geographic coordinates

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• 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

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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:

• Continents: www.kaggle.com/statchaitya/country-to-continent
• Geographic-coordinates: www.kaggle.com/eidanch/counties-geographic-coordinates
• Temperatures: www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
• Share of the population over 65 years old: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS
• GDP/Capita: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

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

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.

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.

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.

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

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.

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