The bivariate analysis of the sociodemographic factors with their knowledge and practice towards COVID-19 among residents of Ethiopia (N = 341).

Objective Daily COVID-19 data reported by the World Health Organization (WHO) may provide the basis for political ad hoc decisions including travel restrictions. Data reported by countries, however, is heterogeneous and metrics to evaluate its quality are scarce. In this work, we analyzed COVID-19 case counts provided by WHO and developed tools to evaluate country-specific reporting behaviors. Methods In this retrospective cross-sectional study, COVID-19 data reported daily to WHO from 3rd January 2020 until 14th June 2021 were analyzed. We proposed the concepts of binary reporting rate and relative reporting behavior and performed descriptive analyses for all countries with these metrics. We developed a score to evaluate the consistency of incidence and binary reporting rates. Further, we performed spectral clustering of the binary reporting rate and relative reporting behavior to identify salient patterns in these metrics. Results Our final analysis included 222 countries and regions...., Data collection COVID-19 data was downloaded from WHO. Using a public repository, we have added the countries' full names to the WHO data set using the two-letter abbreviations for each country to merge both data sets. The provided COVID-19 data covers January 2020 until June 2021. We uploaded the final data set used for the analyses of this paper. Data processing We processed data using a Jupyter Notebook with a Python kernel and publically available external libraries. This upload contains the required Jupyter Notebook (reporting_behavior.ipynb) with all analyses and some additional work, a README, and the conda environment yml (env.yml)., Any text editor including Microsoft Excel and their free alternatives can open the uploaded CSV file. Any web browser and some code editors (like the freely available Visual Studio Code) can show the uploaded Jupyter Notebook if the required Python environment is set up correctly.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Sociodemographic characteristics of respondents to assess prevention knowledge and practice among the community towards the COVID-19 pandemic in Ethiopia (N = 341).

Compilation of published Ro estimates.

The BQC19 enrolling institutions.

Context

Data is collected daily from Our World in Data GitHub repository for covid-19, merged and uploaded.

Content

The data contains the following information: * **Country **- this is the country for which the vaccination information is provided;
* Country ISO Code - ISO code for the country;
* **Date **- date for the data entry; for some of the dates we have only the daily vaccinations, for others, only the (cumulative) total;
* Total number of vaccinations - this is the absolute number of total immunizations in the country;
* Total number of people vaccinated - a person, depending on the immunization scheme, will receive one or more (typically 2) vaccines; at a certain moment, the number of vaccination might be larger than the number of people;
* Total number of people fully vaccinated - this is the number of people that received the entire set of immunization according to the immunization scheme (typically 2); at a certain moment in time, there might be a certain number of people that received one vaccine and another number (smaller) of people that received all vaccines in the scheme;
* Daily vaccinations (raw) - for a certain data entry, the number of vaccination for that date/country;
* Daily vaccinations - for a certain data entry, the number of vaccination for that date/country;
* Total vaccinations per hundred - ratio (in percent) between vaccination number and total population up to the date in the country;
* Total number of people vaccinated per hundred - ratio (in percent) between population immunized and total population up to the date in the country;
* Total number of people fully vaccinated per hundred - ratio (in percent) between population fully immunized and total population up to the date in the country;
* Number of vaccinations per day - number of daily vaccination for that day and country;
* Daily vaccinations per million - ratio (in ppm) between vaccination number and total population for the current date in the country;
* Vaccines used in the country - total number of vaccines used in the country (up to date);
* Source name - source of the information (national authority, international organization, local organization etc.);
* Source website - website of the source of information;

Acknowledgements

I would like to specify that I am only making available Our World in Data collected data about vaccinations to Kagglers. My contribution is very small, just daily collection, merge and upload of the updated version, as maintained by Our World in Data in their GitHub repository.

Inspiration

Track COVID-19 vaccination in the World, answer instantly to your questions:
- Which country is using what vaccine?
- In which country the vaccination programme is more advanced?
- Where are vaccinated more people per day? But in terms of percent from entire population ?

BQC19 planned core analyses.

BQC19 biosample pre-analytical quality information.

BQC19 available biosamples.

Association of knowledge with demographic characteristics.

Hypotheses for first reported case of COVID-19 and school closures.

The Inter-Agency Standing Committee (IASC), created by the United Nations (UN) General Assembly in 1991, serves as the global humanitarian coordination forum of the UN s system. The IASC brings 18 agencies together, including the World Health Organization (WHO), for humanitarian preparedness and response policies and action. Early in the COVID-19 pandemic, the IASC recognized the importance of providing intensified support to countries with conflict, humanitarian, or complex emergencies due to their weak health systems and fragile contexts. A Global Humanitarian Response Plan (GHRP) was rapidly developed in March 2020, which reflected the international support needed for 63 target countries deemed to have humanitarian vulnerability. This paper assessed whether WHO provided intensified technical, financial, and commodity inputs to GHRP countries (n = 63) compared to non-GHRP countries (n = 131) in the first year of the COVID-19 pandemic. The analysis showed that WHO supported all 194 countries regardless of humanitarian vulnerability. Health commodities were supplied to most countries globally (86%), and WHO implemented most (67%) of the $1.268 billion spent in 2020 at country level. However, proportionally more GHRP countries received health commodities and nearly four times as much was spent in GHRP countries per capita compared to non-GHRP countries ($232 vs. $60 per 1,000 capita). In countries with WHO country offices (n = 149), proportionally more GHRP countries received WHO support for developing national response plans and monitoring frameworks, training of technical staff, facilitating logistics, publication of situation updates, and participation in research activities prior to the characterization of the pandemic or first in-country COVID-19 case. This affirms WHO's capacity to scale country support according to its humanitarian mandate. Further work is needed to assess the impact of WHO's inputs on health outcomes during the COVID-19 pandemic, which will strengthen WHO's scaled support to countries during future health emergencies.

Characteristics of included studies: Social distancing intervention.

Characteristics of included studies: Face masking, hand hygiene and multicomponent intervention (face masking and hand hygiene).

Demographic characteristics of health professionals.

Descriptive statistics of COVID-19 cases.

Assessment of preparedness of health professionals and the respective hospitals towards the pandemic.

The proportions of correct answers about the signs, diagnostic methods, identification criteria, and prevention measures regarding COVID-19 given by health professionals.

The results of the estimated STARIMA models.