Analysis of people previously considered to be clinically extremely vulnerable (CEV) in England during the coronavirus (COVID-19) pandemic, including their behaviours and mental and physical well-being.

While diseases can make anyone sick, some Canadians are more at risk of getting an infection and developing severe complications due to their health, social and economic circumstances. Organizations, staff and volunteers play an important role in helping to prevent these populations from getting or spreading the COVID-19 virus. Start by sharing simple things they can do to help keep themselves and others healthy, guide them to help if they develop any signs and symptoms and learn ways help care for sick clients recovering from COVID-19.

Exploring COVID-19 impact on vulnerable populations using infographics (ArcGIS Blog).This blog shows how to explore vulnerable populations in the United States to help plan for the impact of the coronavirus using infographics._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The British Red Cross COVID-19 Vulnerability Index identifies areas in the UK where people might be more vulnerable to the effects of Covid-19. The Index looks at clinical vulnerability, wider health and wellbeing, and socioeconomic vulnerability.Click here for more details.The data sources for this application are as follows:British Red Cross Vulnerability Index by Local Authority DistrictBritish Red Cross COVID-19 Vulnerability Index by Middle Super Output Area (MSOA) in EnglandBritish Red Cross COVID-19 Vulnerability Index by Middle Super Output Area (MSOA) in WalesBritish Red Cross COVID-19 Vulnerability Index by Intermediate Zone in ScotlandBritish Red Cross COVID-19 Vulnerability Index by Super Output Area in Northern IrelandIndex of Multiple Deprivation 2015 (England)Index of Multiple Deprivation 2016 (Scotland)

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dictionary describing what each numeric digit means within each classification. The “Category” column uses numeric digits (2-6, depending on the factor) defined in the “Classification” column.

Metro vs. Non-Metro – “Metro_Rural” Metro vs. Non-Metro classification type is an aggregation of the 6 National Center for Health Statistics (NCHS) Urban-Rural classifications, where “Metro” counties include Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro areas and “Non-Metro” counties include Micropolitan and Non-Core (Rural) areas. 1 – Metro, including “Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro” areas 2 – Non-Metro, including “Micropolitan, and Non-Core” areas

Urban/rural - “NCHS_Class” Urban/rural classification type is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Levels consist of:

1 Large Central Metro
2 Large Fringe Metro 3 Medium Metro 4 Small Metro 5 Micropolitan 6 Non-Core (Rural)

American Community Survey (ACS) data were used to classify counties based on their age, race/ethnicity, household size, poverty level, and health insurance status distributions. Cut points were generated by using tertiles and categorized as High, Moderate, and Low percentages. The classification “Percent non-Hispanic, Native Hawaiian/Pacific Islander” is only available for “Hawaii” due to low numbers in this category for other available locations. This limitation also applies to other race/ethnicity categories within certain jurisdictions, where 0 counties fall into the certain category. The cut points for each ACS category are further detailed below:

Age 65 - “Age65”

1 Low (0-24.4%) 2 Moderate (>24.4%-28.6%) 3 High (>28.6%)

Non-Hispanic, Asian - “NHAA”

1 Low (<=5.7%) 2 Moderate (>5.7%-17.4%) 3 High (>17.4%)

Non-Hispanic, American Indian/Alaskan Native - “NHIA”

1 Low (<=0.7%) 2 Moderate (>0.7%-30.1%) 3 High (>30.1%)

Non-Hispanic, Black - “NHBA”

1 Low (<=2.5%) 2 Moderate (>2.5%-37%) 3 High (>37%)

Hispanic - “HISP”

1 Low (<=18.3%) 2 Moderate (>18.3%-45.5%) 3 High (>45.5%)

Population in Poverty - “Pov”

1 Low (0-12.3%) 2 Moderate (>12.3%-17.3%) 3 High (>17.3%)

Population Uninsured- “Unins”

1 Low (0-7.1%) 2 Moderate (>7.1%-11.4%) 3 High (>11.4%)

Average Household Size - “HH”

1 Low (1-2.4) 2 Moderate (>2.4-2.6) 3 High (>2.6)

Community Vulnerability Index Value - “CCVI” COVID-19 Community Vulnerability Index (CCVI) scores are from Surgo Ventures, which range from 0 to 1, were generated based on tertiles and categorized as:

1 Low Vulnerability (0.0-0.4) 2 Moderate Vulnerability (0.4-0.6) 3 High Vulnerability (0.6-1.0)

Social Vulnerability Index Value – “SVI" Social Vulnerability Index (SVI) scores (vintage 2020), which also range from 0 to 1, are from CDC/ASTDR’s Geospatial Research, Analysis & Service Program. Cut points for CCVI and SVI scores were generated based on tertiles and categorized as:

1 Low Vulnerability (0-0.333) 2 Moderate Vulnerability (0.334-0.666) 3 High Vulnerability (0.667-1)

IntroductionCOVID-19 vaccine inequities have been widespread across California, the United States, and globally. As COVID-19 vaccine inequities have not been fully understood in the youth population, it is vital to determine possible factors that drive inequities to enable actionable change that promotes vaccine equity among vulnerable minor populations.MethodsThe present study used the social vulnerability index (SVI) and daily vaccination numbers within the age groups of 12–17, 5–11, and under 5 years old across all 58 California counties to model the growth velocity and the anticipated maximum proportion of population vaccinated.ResultsOverall, highly vulnerable counties, when compared to low and moderately vulnerable counties, experienced a lower vaccination rate in the 12–17 and 5–11 year-old age groups. For age groups 5–11 and under 5 years old, highly vulnerable counties are expected to achieve a lower overall total proportion of residents vaccinated. In highly vulnerable counties in terms of socioeconomic status and household composition and disability, the 12–17 and 5–11 year-old age groups experienced lower vaccination rates. Additionally, in the 12–17 age group, high vulnerability counties are expected to achieve a higher proportion of residents vaccinated compared to less vulnerable counterparts.DiscussionThese findings elucidate shortcomings in vaccine uptake in certain pediatric populations across California and may help guide health policies and future allocation of vaccines, with special emphasis placed on vulnerable populations, especially with respect to socioeconomic status and household composition and disability.

In the context of COVID-19, RAND and the Robert Wood Johnson Foundation partnered again to build from the National Survey of Health Attitudes to implement a longitudinal survey to understand how these health views and values have been affected by the experience of the pandemic, with particular focus on populations deemed vulnerable or underserved, including people of color and those from low- to moderate-income backgrounds. The questions in this COVID-19 survey focused specifically on experiences related to the pandemic (e.g., financial, physical, emotional), how respondents viewed the disproportionate impacts of the pandemic, whether and how respondents' views and priorities regarding health actions and investments are changing (including the roles of government and the private sector), and how general values about such issues as freedom and racism may be related to pandemic views and response expectations. This study includes the results for Wave 4 for the general population. Demographic information includes sex, marital status, household size, race and ethnicity, family income, employment status, age, and census region.

The Chicago CCVI identifies communities that have been disproportionately affected by COVID-19 and are vulnerable to barriers to COVID-19 vaccine uptake​. Vulnerability is defined as a combination of sociodemographic factors, epidemiological factors​, occupational factors​, and cumulative COVID-19 burden. The 10 components of the index include COVID-19 specific risk factors and outcomes and social factors known to be associated with social vulnerability in the context of emergency preparedness. The CCVI is derived from ranking values of the components by Chicago Community Area, then synthesizing them into a single composite weighted score. The higher the score, the more vulnerable the geographic area. ZIP Code CCVI is included to enable comparison with other COVID-19 data available on the Chicago Data Portal. Some elements of the CCVI are not available by ZIP Code. To create ZIP Code CCVI, the proportion of the ZIP Code population contributed by each Community Areas was determined. The apportioned populations were then weighted by the Community Area CCVI score and averaged to determine a ZIP Code CCVI score. The COVID-19 Community Vulnerability Index (CCVI) is adapted and modified from a Surgo Ventures collaboration (https://precisionforcovid.org/ccvi) and the CDC Social Vulnerability Index​. ZIP Codes are based on ZIP Code Tabulation Areas (ZCTAs) developed by the U.S. Census Bureau. For full documentation see: https://www.chicago.gov/content/dam/city/sites/covid/reports/012521/Community_Vulnerability_Index_012521.pdf

The novel coronavirus infectious disease (COVID-19) pandemic has negatively impacted not only our physical health but also mental health, including increasing depressive and anxiety symptoms. In particular, socially and physically vulnerable populations, such as people experiencing homelessness (PEH), may be more likely to have their mental health worsened by the pandemic due to having more difficulty meeting basic human needs. Therefore, this study aims to assess the impact of COVID-19 on mental health of the homeless in Japan by evaluating depressive and anxiety symptoms and identifying the associated factors particularly, sociodemographic variables as age, employment status and the fear and perceived risk of COVID-19 infection. A cross-sectional interview survey among 158 PEH in Osaka Prefecture was conducted from April to May 2022. The survey included sociodemographic questions and history and perceived risk of infection with COVID-19. Depressive symptoms were measured using the nine-item Patient Health Questionnaire (PHQ-9) and anxiety symptoms using the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the fear of COVID-19 using the seven-item Fear of New Coronavirus Scale (FCV-19S). In this study, the prevalence of depression (PHQ-9≥10) was 38.6%, anxiety disorder (GAD≥10) was 19.0%, and high fear of COVID-19 (FCV-19S≥19) was 28.5%. Univariate logistic regression analysis revealed that PEH in younger age groups (18–34 years), and with joblessness, higher perceived infection risk, and higher fear of COVID-19 were more likely to suffer from depression and anxiety (p

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

The PHIRI Federated Research Infrastructure (FRI) is supported by a containerized reproducible solution for data analysis to be deployed on-premises by each participant partner.

This solution is based on the identification of the relevant data sources for each cases study (including the demonstration pilot), the development of the common data models and the analytical pipelines, and enables the FAIR reporting of the rapid cycle outputs.

The aggregated dataset is produced by an analysis script integrated within the PHIRI App for PHIRI Use Case A local analyses performed on data from Austria.

Input data conforms to the respective Common Data Model.

The aggregated output dataset is disseminated within WP6 of the PHIRI project to allow for aggregated and comparative analyses across participating countries.

If you wish to contribute to the PHIRI - Use Case A analyses, please contact the WP6 Coordinator through the PHIRI website.

As of January 2023, members of the Swiss population aged 80 years and older have been most vulnerable to the coronavirus (COVID-19) outbreak, with the highest number of deaths recorded in this age group. Older age groups are believed to be especially at risk.

NOTE: As of 4/15/2021, this dataset will no longer be updated and will be replaced by two new datasets: 1) "COVID-19 Vaccinations by Town" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town/x7by-h8k4) and "COVID-19 Vaccinations by Town and Age Group" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town-and-Age-Group/gngw-ukpw). A summary of COVID-19 vaccination coverage in Connecticut by town. Records without an address could not be included in town vaccine coverage estimates. Total population estimates are based on 2019 data. A person who has received one dose of any vaccine is considered to have received at least one dose. A person is considered fully vaccinated if they have received 2 doses of the Pfizer or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. The number with At Least One Dose and the number Fully Vaccinated add up to more than the total number of doses because people who received the Johnson & Johnson vaccine fit into both categories. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. Towns with a "yes" in the "Has SVI tract >0.75" field are those that have at least one census tract that is in the top quartile of vulnerability (e.g., a high-need area). 34 towns in Connecticut have at least one census tract in the top quartile for vulnerability. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.

In the emerging post-pandemic era (the ‘wavelet’ era), humans must coexist with viruses for the foreseeable future, and personal protective behaviors will largely replace national-level preventive measures. In this new normal, encouraging the public to implement proper personal protective behaviors against the coronavirus disease (COVID-19) is vital to the sustainable development of cities and communities. This knowledge–attitude–practice (KAP) survey conducted in Chengdu (N = 900) narrowed the knowledge gap regarding post-pandemic public practices of protective behavior. Findings show that:(1) approximately 1/3 of the respondents are currently not concerned about COVID-19 at all; (2) respondents with different demographics and individual COVID-19-related factors showed significant differences in practice behaviors indoors and outdoors; (3) vulnerable groups performed better in practice behavior indoors/outdoors; (4) because the public may relax their vigilance outdoors, public places may become a transmission threat in the next outbreak; (5) attitudes are important, but limited incentives for practice; and (6) when knowledge increases beyond a threshold (68.75–75% in this study), protective behaviors decrease. Our results suggest that authorities must continue to educate and motivate the public, extending measures to cover personal protective practices, and have targeted policies for specific demographics to ensure equity in healthcare in the event of another pandemic (COVID-19 and alike crisis). Besides, comparing the results of the current study with similar studies conducted in other parts of the world can provide insights into how different populations respond to and adopt COVID-19 protective behaviors. The epidemiologists can use the data collected by this and other KAP surveys to refine epidemiologic models, which can help predict the spread of the virus and the impact of interventions in different settings.

The participants of this phone interview were identified using mixed methods. Stratified random sampling were adopted for PoCs based in Kakuma, Kalobeyei, Dadaab and Urban areas. While a census were used for all PoCs who were 18+ years amongst the Shona community; this cohort forms 48.6% of the enumerated population of the Shona people. The survey was conducted at two levels; household and individual. For the second wave, 4390 individuals were included belonging to 1735 households.

COVID-19 and COVID-related decisions are having significant impacts on children and adults vulnerable to, and already experiencing, the crime of forced marriage. This mixed-methods project aimed to chart and understand this impact, inform evaluation of the UK's response to COVID-19, and shape on-going policy regarding the UK's pandemic response. This data includes the questions for and responses to a survey of staff at a national helpline for victims of forced marriage. It also includes visualisations of the data made for the published report.

COVID-19 and COVID-related decisions are having significant impacts on children and adults vulnerable to, and already experiencing, the crime of forced marriage. Our mixed-methods project will chart and understand this impact, inform evaluation of the UK's response to COVID-19, and shape on-going policy regarding the UK's pandemic response. We consider the uneven economic and social impact of the pandemic, and the ethical dimensions of unequal impacts of COVID-related decision-making, on this vulnerable group, and seek to impact how civil society and the voluntary sector support vulnerable people.

The government's Forced Marriage Unit (FMU) and the charity Karma Nirvana (KN) (which provides a national forced marriage helpline) have warned about the significant impact of the pandemic on forced marriage in the UK. We designed this project with both organisations, and will work with them to analyse quantitative and qualitative data about the impact of COVID-19 on those at risk of, or experiencing, forced marriage; and to record and analyse the challenges faced in the pandemic, evaluate the efficacy of mitigation strategies, and formulate new policies and practises for protection and response.

Within the first 6 months, we will have co-created an accurate account of the economic and social impact of COVID-19 and COVID-related decision-making on victims of forced marriage, and the ethical implications of unequal impacts. We will then continue to chart the changing risk environment, while co-developing policy reports and recommendations for the UK government (including FMU), NGO practice responses (including KN), and other stakeholders, to improve the on-going response to COVID-19 and build community resilience.

As of June 13, 2023, there have been almost 768 million cases of coronavirus (COVID-19) worldwide. The disease has impacted almost every country and territory in the world, with the United States confirming around 16 percent of all global cases.

COVID-19: An unprecedented crisis Health systems around the world were initially overwhelmed by the number of coronavirus cases, and even the richest and most prepared countries struggled. In the most vulnerable countries, millions of people lacked access to critical life-saving supplies, such as test kits, face masks, and respirators. However, several vaccines have been approved for use, and more than 13 billion vaccine doses had already been administered worldwide as of March 2023.

The coronavirus in the United Kingdom Over 202 thousand people have died from COVID-19 in the UK, which is the highest number in Europe. The tireless work of the National Health Service (NHS) has been applauded, but the country’s response to the crisis has drawn criticism. The UK was slow to start widespread testing, and the launch of a COVID-19 contact tracing app was delayed by months. However, the UK’s rapid vaccine rollout has been a success story, and around 53.7 million people had received at least one vaccine dose as of July 13, 2022.

NOTE: As of 2/16/2023, this table is not being updated. For data on COVID-19 updated (bivalent) booster coverage by town please to go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Town/bqd5-4jgh.

This table shows the number and percent of residents of each CT town that have initiated COVID-19 vaccination, are fully vaccinated and who have received additional dose 1.

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.

In the data shown here, a person who has received at least one dose of COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if he/she has completed a primary vaccination series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the people who have received at least one dose.

A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations.

The percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported.

Percentages are calculated using 2019 census data (https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-Town-and-County-Population-for-Connecticut).

Town of residence is verified by geocoding the reported address and then mapping it to a town using municipal boundaries. If an address cannot be geocoded, the reported town is used, if available. People for whom an address is not currently available are shown in this table as “Address pending validation”. Out-of-state residents vaccinated by CT providers are excluded from the table.

Town-level coverage estimates have been capped at 100%. Observed coverage may be greater than 100% for multiple reasons, including census denominator data not including all individuals that currently reside in the town (e.g., part time residents, change in population size since the census), errors in address data or other reporting errors. Also, the percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated when records for an individual cannot be linked because of differences in how names or date of birth are reported.

Caution should be used when interpreting coverage estimates for towns with large college/university populations since coverage may be underestimated. In the census, college/university students who live on or just off campus would be counted in the college/university town. However, if a student was vaccinated while studying remotely in his/her hometown, the student may be counted as a vaccine recipient in that town.

SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. Towns with a "yes" in the "Has SVI tract >0.75" field are those that have at least one census tract that is in the top quartile of vulnerability (e.g., a high-need area). 34 towns in Connecticut have at least one census tract in the top quartile for vulnerability.

Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction. Doses administered by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) are not yet reported to CT WiZ.  Data reported here reflect the vaccination records currently reported to CT WiZ.

Note: This dataset takes the place of the original "COVID-19 Vaccinations by Town" dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town/pdqi-ds7f) , which will not be updated after 4/15/2021. A breakdown of vaccinations by town and by age group is also available here: https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town-and-Age-Group/gngw-ukpw .

As part of continuous data quality improvement efforts, duplicate records were removed from the COVID-19 vaccination data during the weeks of 4/19/2021 and 4/26/2021.

COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: North Sumatera: North Labuhanbatu Regency data was reported at 165,223.000 Person in 22 Mar 2025. This stayed constant from the previous number of 165,223.000 Person for 20 Mar 2025. COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: North Sumatera: North Labuhanbatu Regency data is updated daily, averaging 165,223.000 Person from Nov 2021 (Median) to 22 Mar 2025, with 984 observations. The data reached an all-time high of 292,363.000 Person in 05 Nov 2022 and a record low of 69,792.000 Person in 25 Nov 2021. COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: North Sumatera: North Labuhanbatu Regency data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLB012: Coronavirus Disease 2019 (Covid-19): Vaccination Status: by Regency and Municipality: General Public and Vulnerable.