The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

Age and social vulnerability in the context of Coronavirus COVID-19 (ArcGIS Blog).How to map the confluence of COVID-19 risk factors for US counties._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...

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

Given the characteristics of the COVID-19 pandemic and the limited tools for orienting interventions in surveillance, control, and clinical care, the current article aims to identify areas with greater vulnerability to severe cases of the disease in Rio de Janeiro, Brazil, a city characterized by huge social and spatial heterogeneity. In order to identify these areas, the authors prepared an index of vulnerability to severe cases of COVID-19 based on the construction, weighting, and integration of three levels of information: mean number of residents per household and density of persons 60 years or older (both per census tract) and neighborhood tuberculosis incidence rate in the year 2018. The data on residents per household and density of persons 60 years or older were obtained from the 2010 Population Census, and data on tuberculosis incidence were taken from the Brazilian Information System for Notificable Diseases (SINAN). Weighting of the indicators comprising the index used analytic hierarchy process (AHP), and the levels of information were integrated via weighted linear combination with map algebra. Spatialization of the index of vulnerability to severe COVID-19 in the city of Rio de Janeiro reveals the existence of more vulnerable areas in different parts of the city’s territory, reflecting its urban complexity. The areas with greatest vulnerability are located in the North and West Zones of the city and in poor neighborhoods nested within upper-income parts of the South and West Zones. Understanding these conditions of vulnerability can facilitate the development of strategies to monitor the evolution of COVID-19 and orient measures for prevention and health promotion.

This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city's vulnerability to COVID-19

In this analysis, we highlight red counties that have combinations of a high percentage of vulnerable populations and high rates of COVID-19, and that are also adjacent to counties with similarly high values. We calculate and map a Local Indicator of Spatial Association (LISA) for pairs of variables in counties in the contiguous United States.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

covid_risk_index

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

What is Social Vulnerability?Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, among others, may affect that community’s ability to prevent human suffering and financial loss in the event of a disaster. These factors describe a community’s social vulnerability.What is the CDC/ATSDR Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (hereafter, CDC/ATSDR SVI or SVI) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI indicates the relative vulnerability of every U.S. census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, such as unemployment, racial and ethnic minority status, and disability status. Then, SVI further groups the factors into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.Below, text that describes “tract” methods also refers to county methods.How can the SVI help communities be better prepared for hazardous events?SVI provides specific socially and spatially relevant information to help public health officials and local planners better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks, or chemical exposure.SVI can be used to:Assess community need during emergency preparedness planning.Estimate the type and quantity of needed supplies such as food, water, medicine, and bedding.Decide the number of emergency personnel required to assist people.Identify areas in need of emergency shelters.Create a plan to evacuate people, accounting for those who have special needs, such as those without vehicles, the elderly, or people who do not speak English well.Identify communities that will need continued support to recover following an emergency or natural disaster.For more detailed methodology and attribute details, please review this document.

Description: The COVID-19 dataset used for this EDA project encompasses comprehensive data on COVID-19 cases, deaths, and recoveries worldwide. It includes information gathered from authoritative sources such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and national health agencies. The dataset covers global, regional, and national levels, providing a holistic view of the pandemic's impact.

Purpose: This dataset is instrumental in understanding the multifaceted impact of the COVID-19 pandemic through data exploration. It aligns perfectly with the objectives of the EDA project, aiming to unveil insights, patterns, and trends related to COVID-19. Here are the key objectives: 1. Data Collection and Cleaning: • Gather reliable COVID-19 datasets from authoritative sources (such as WHO, CDC, or national health agencies). • Clean and preprocess the data to ensure accuracy and consistency. 2. Descriptive Statistics: • Summarize key statistics: total cases, recoveries, deaths, and testing rates. • Visualize temporal trends using line charts, bar plots, and heat maps. 3. Geospatial Analysis: • Map COVID-19 cases across countries, regions, or cities. • Identify hotspots and variations in infection rates. 4. Demographic Insights: • Explore how age, gender, and pre-existing conditions impact vulnerability. • Investigate disparities in infection rates among different populations. 5. Healthcare System Impact: • Analyze hospitalization rates, ICU occupancy, and healthcare resource allocation. • Assess the strain on medical facilities. 6. Economic and Social Effects: • Investigate the relationship between lockdown measures, economic indicators, and infection rates. • Explore behavioral changes (e.g., mobility patterns, remote work) during the pandemic. 7. Predictive Modeling (Optional): • If data permits, build simple predictive models (e.g., time series forecasting) to estimate future cases.

Data Sources: The primary sources of the COVID-19 dataset include the Johns Hopkins CSSE COVID-19 Data Repository, Google Health’s COVID-19 Open Data, and the U.S. Economic Development Administration (EDA). These sources provide reliable and up-to-date information on COVID-19 cases, deaths, testing rates, and other relevant variables. Additionally, GitHub repositories and platforms like Medium host supplementary datasets and analyses, enriching the available data resources.

Data Format: The dataset is available in various formats, such as CSV and JSON, facilitating easy access and analysis. Before conducting the EDA, the data underwent preprocessing steps to ensure accuracy and consistency. Data cleaning procedures were performed to address missing values, inconsistencies, and outliers, enhancing the quality and reliability of the dataset.

License: The COVID-19 dataset may be subject to specific usage licenses or restrictions imposed by the original data sources. Proper attribution is essential to acknowledge the contributions of the WHO, CDC, national health agencies, and other entities providing the data. Users should adhere to any licensing terms and usage guidelines associated with the dataset.

Attribution: We acknowledge the invaluable contributions of the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), national health agencies, and other authoritative sources in compiling and disseminating the COVID-19 data used for this EDA project. Their efforts in collecting, curating, and sharing data have been instrumental in advancing our understanding of the pandemic and guiding public health responses globally.

The ongoing multi-wave COVID-19 pandemic has disproportional impacts on people with different demographic and socioeconomic background, and their access to healthcare facilities. Vulnerable neighborhoods with low healthcare access are places most needed for the enhancement of medical resources and services. Measuring vulnerability to COVID-19 and healthcare accessibility at the fine-grained level serves as the foundation for spatially explicit health planning and policy making in response to future public health crisis. Despite of its importance, the evaluation of vulnerability and healthcare accessibility is insufficient in Japan—a nation with high population density and super-aging challenge. Drawing on the latest 2022 census data at the smallest statistical unit, as well as transport network, medical and digital cadastral data, land use maps, and points of interest data, our study reformulates the concept of vulnerability in the context of COVID-19 and constructs the first fine-grained measure of vulnerability and healthcare accessibility in Tokyo Metropolis, Japan—the most popular metropolitan region in the world. We delineate the vulnerable neighborhoods with low healthcare access and further evaluate the disparity in healthcare access and built environment of areas at different levels of vulnerability. Our outcome datasets and findings provide nuanced and timely evidence to government and health authorities to have a holistic and latest understanding of social vulnerability to COVID-19 and healthcare access at a fine-grained level. Our analytical framework can be employed to different geographic contexts, guiding through the place-based health planning and policy making in the post-COVID era and beyond.

This feature layer visualizes the 2018 overall SVI for U.S. counties and tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract15 social factors grouped into four major themesIndex value calculated for each county for the 15 social factors, four major themes, and the overall rankWhat is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2018 documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the fifteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCounties in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2018 Full DocumentationSVI Home PageContact the SVI Coordinator

Coronavirus-19 Cases vs. Deaths (Hourly Update)See Detailed graphs and tables describing the COVID-19 crisis in New Mexico, updated daily (includes some county level data not found elsewhere) - https://sites.google.com/view/new-mexico-covid19-tracking/homeCDC's Description of the Social Vulnerability Index (takes into account 15 different selected indicators):https://svi.cdc.gov/

The project had Four Research Stages

Stage 1 – Global Mapping Exercise Aim: Map and develop typologies of the pandemic’s impact on the food/education/play-leisure nexus, with a focus on young people’s vulnerabilities globally, based on an international, integrative review of research and policy literatures. Stage 2: – National and Regional Mapping (Brazil, South Africa, UK) Aim: Examine key impacts of pandemic-related policy on young people’s access to and adaptations around food, education and play/leisure at the national, regional and local scale. Stage 3: Zooming in on local adaptations of young people in monetary-poor households Aim: In-depth research with professional stakeholders and young people in each case study region, with a focus on incremental and innovative strategies and the impact of those adaptations on everyday survival and recovery. In England, this research took place in Birmingham and the West Midlands. In total, we worked with 87 young people, using qualitative methods such as interviews and visual mapping. The research was co-produced with young people: we worked with a core group of ten young people from Birmingham City Council’s Youth Voice team, who co-designed some of the methods, undertook peer research with some of the young people in our sample, and co-analysed data. Stage 4: Co-design of solutions to foster young people’s recovery and resilience Aim: Co-design solutions with our community of young people and key professionals that will help vulnerable young people to recover and be prepared in the eventuality of future major health and socio-economic crises. In England, this process took place in Birmingham and the West Midlands and involved the same core group discussing the project’s main findings. Through a series of workshops, young people’s recommendations were created and tested with us and a selected group of professional stakeholders.

Stage 1 - Interviews with key organisations working in the food/education/play sector and with children and youth.

The team conducted 32 interviews with key organisations between February and June 2023. The aim was to situate and identify what had been the key impacts of pandemic-related policy towards the food, education, play/leisure nexus of issues facing young people during and after COVID-19, in England. It also sought to examine what policy/programmes/initiatives were developed, and how local places mattered (including home life/household contexts). To do so, we identified representatives from a range of organisations that played a key role in supporting young people and/ or in assessing the impacts of the pandemic on them.

Sampling was done through desk-based research based on a review of national and regional review of the literature and reports and further on snowballing, we identified non-governmental and non-profit organisations that played a key contribution in supporting young people and/or assessing the impact and repercussions of the pandemic on them. Selection of the interviews was made either through their role across the country or because of their contribution at regional and city levels. The number of 30 was considered as commensurate with the methods used in similarly-sized comparative projects of similar scale. This included representatives from the following types of organisations:

• Charities (incl. Foundations and Think-Tanks) working either across England or in specific English regions, and specialized in the following sectors: food education, food policy, food provision (including food banks) and healthy food; education provision, education and digital technology, education policy, education and youth, social mobility and educational disadvantage; play provision, play policy; support to disadvantaged and vulnerable young people. • Not-for profit social enterprises focusing on youth education, youth employment, food and nutrition. • Schools/Colleges. • Private Companies specialized in supporting education organisations and play provision. • Research Institutions with specific expertise in education, food and health and children/young people. • Local and Combined Authorities. • Diocesan and Faith groups. • National networks representing community organisations in the faith and play sector. • Young People Ambassadors.

While looking at England as a whole, we also zoomed on West Midlands/Birmingham. The West Midlands was one of the hardest-hit parts of the UK during COVID-19. The region includes some of the most deprived neighbourhoods and a younger than average population. The intent of the interviews was twofold: 1) to understand each organisation’s response to supporting young people during/after COVID-19, and 2) from the organisation’s views, to identify what adaptations and tactics young people used to deal with the challenges that COVID-19 and associated lockdowns presented. Interview questions focused on the following themes: The role of the organisation and how they engaged with young people, the...

PurposeStaySafe PH is the Philippines’ official contact tracing software for controlling the propagation of COVID-19 and promoting a uniform contact tracing strategy. The StaySafe PH has various features such as a social distancing system, LGU heat map and response system, real-time monitoring, graphs, infographics, and the primary purpose, which is a contact tracing system. This application is mandatory in establishments such as fast-food restaurants, banks, and malls.Objective and methodologyThe purpose of this research was to determine the country’s willingness to utilize StaySafe PH. Specifically, this study utilized 12 latent variables from the integrated Protection Motivation Theory (PMT), Unified Theory of Acceptance and Use of Technology (UTAUT2), and System Usability Scale (SUS). Data from 646 respondents in the Philippines were employed through Structural Equation Modelling (SEM), Deep Learning Neural Network (DLNN), and SUS.ResultsUtilizing the SEM, it is found that understanding the COVID-19 vaccine, understanding the COVID-19 Delta variant, perceived vulnerability, perceived severity, performance expectancy, social influence, hedonic motivation, behavioral intention, actual use, and the system usability scale are major determinants of intent to utilize the application. Understanding of the COVID-19 Delta Variant was found to be the most important factor by DLNN, which is congruent with the results of SEM. The SUS score of the application is "D", which implies that the application has poor usability.ImplicationsIt could be implicated that large concerns stem from the trust issues on privacy, data security, and overall consent in the information needed. This is one area that should be promoted. That is, how the data is stored and kept, utilized, and covered by the system, how the assurance could be provided among consumers, and how the government would manage the information obtained. Building the trust is crucial on the development and deployment of these types of technology. The results in this study can also suggest that individuals in the Philippines expected and were certain that vaccination would help them not contract the virus and thus not be vulnerable, leading to a positive actual use of the application.NoveltyThe current study considered encompassing health-related behaviors using the PMT, integrating with the technology acceptance model, UTAUT2; as well as usability perspective using the SUS. This study was the first one to evaluate and assess a contact tracing application in the Philippines, as well as integrate the frameworks to provide a holistic measurement.

Data was analyzed for each area in the city limits, assessed against the key questions below, and assigned a risk score (5:Highest Risk à 0: No Risk).Do Black, Hispanic and Native American populations together make up more than 70% of the community?Does the area have 15% or more of its families at or below 100% of the federal poverty level?Do less than 50% of the area’s households own the home they live in?Is the area rated “High” on the CDC’s Social Vulnerability Index, Socioeconomic Level?Are more than 12% of the area’s residents 65 or older?This map is used in the second tab of this dashboard - https://dallasgis.maps.arcgis.com/home/item.html?id=1f95208936ba485e8b40f26a1e641860This map also feeds this dashboard's second tab: https://dallasgis.maps.arcgis.com/home/item.html?id=0a564464fa1c40ed807f468ad870007d

Contains the following information:COVID cases, case prevalence over different time spans, current COVID hotspots, and number of tests for the ABQ metro area at zip code level. Social vulnerability factors for the ABQ metro area at zip code level. COVID deaths at the small area level. The location of testing sites (updated regularly as new sites and information are found)The spread of COVID, testing, deaths, and PPE supply information by nursing homes (updated regularly)The locations of summer meal sites. This dashboard runs in this app: https://nmcdc.maps.arcgis.com/apps/MapSeries/index.html?appid=1ff0aa71c0ae427cbb5753d08ae19eabThis dashboard runs the following maps:Social Vulnerability Index, Albuquerque Metro Area, Census Tracts & Zip Codes, 2018 - https://nmcdc.maps.arcgis.com/home/item.html?id=850e8f2e7c394fb99041b94f813cb5faCOVID-19 Testing Locations - New Mexico - https://nmcdc.maps.arcgis.com/home/item.html?id=aace827af8fa4d2d9037ce5c7fb0e880COVID Deaths, NM Small Areas - CABQ - https://nmcdc.maps.arcgis.com/home/item.html?id=a56dab27204b4573a7f8d1663bc95844COVID-19 TESTING & CASES by TIME PERIODS, ZIP CODES - v1 - https://nmcdc.maps.arcgis.com/home/item.html?id=14e05ddda38d40cb9746750072d00c80Summer Meal Sites - CABQ - https://nmcdc.maps.arcgis.com/home/item.html?id=5fb8f3e689df4f03ab8be107d04fcd30Nursing Homes, COVID-19 Cases and Deaths, New Mexico and USA - https://nmcdc.maps.arcgis.com/home/item.html?id=8e74a05a32324aa3bcc07e2b1545d446

Coronavirus-19 Cases (Hourly Update) vs. Median Household Income (ACS)See Detailed graphs and tables describing the COVID-19 crisis in New Mexico, updated daily (includes some county level data not found elsewhere) - https://sites.google.com/view/new-mexico-covid19-tracking/homeCDC's Description of the Social Vulnerability Index (takes into account 15 different selected indicators):https://svi.cdc.gov/