Study Objective: Facemask use is associated with reduced transmission of SARS-CoV-2. Most surveys assessing perceptions and practices of mask use miss the most vulnerable racial, ethnic, and socio-economic populations. These same populations have suffered disproportionate impacts from the pandemic. The purpose of this study was to assess beliefs, access, and practices of mask wearing across 15 urban emergency department (ED) populations. Methods: This was a secondary analysis of a cross-sectional study of ED patients from December 2020 to March 2021 at 15 geographically diverse, safety net EDs across the US. The primary outcome was frequency of mask use outside the home and around others. Other outcome measures included having enough masks and difficulty obtaining them. Results: Of 2,575 patients approached, 2,301 (89%) agreed to participate; nine had missing data pertaining to the primary outcome, leaving 2,292 included in the final analysis. A total of 79% of respondents reported wearing masks “all of the time” and 96% reported wearing masks over half the time. Subjects with PCPs were more likely to report wearing masks over half the time compared to those without PCPs (97% vs 92%). Individuals experiencing homelessness were less likely to wear a mask over half the time compared to those who were housed (81% vs 96%). Conclusions: Study participants reported high rates of facemask use. Respondents who did not have PCPs and those who were homeless were less likely to report wearing a mask over half the time and more likely to report barriers in obtaining masks. The ED may serve a critical role in education regarding, and provision of, masks for vulnerable populations. Methods Study Design and Setting We conducted this secondary analysis of a previously published study regarding ED patients perceptions’ of COVID-19 vaccination.[13] The parent study was a prospective, cross-sectional survey of ED patients at 15 safety net EDs in 14 US cities. The University of California Institutional Review Board approved this study. Verbal consent was obtained. Data Processing Participant ethnicity (Latinx/non-Latinx) and race were self-reported. We categorized those who self-identified as any race other than Latinx as ‘reported race’, non-Latinx (i.e. Black, non-Latinx and White, non-Latinx). If the patient identified themselves as Latinx, they were placed in that category and not in that of any other race. If an individual identified as more than one non-Latinx race, they were categorized as multiracial. Individuals who reported that they were currently applying for health insurance, were unsure if they were insured, or if their response to the question was missing (18 respondents) were categorized as uninsured in a binary variable, and separate analysis was done based on type of insurance reported. The survey submitted in our supplement (S1) is the version used at the lead site. Each of the remaining sites revised their survey to include wording applicable to their community (i.e., the site in Los Angeles changed Healthy San Francisco to Healthy Los Angeles), and these local community health plans were coded together. We identified individuals who reported English and Spanish as their primary language, and grouped those who reported Arabic, Bengali, Cantonese, Tagalog, or Other as “Other” primary language. With regards to gender, we categorized those who identified as gender queer, nonbinary, trans man and trans woman as “other”. Study Outcomes and Key Variables Our primary outcome was subjects’ response to the question, “Do you wear a mask when you are outside of your home when you are around other people?” with answer choices a) always, b) most of the time (more than 50%), c) sometimes, but less than half of the time (less than 50%), and d) I never wear a mask. Respondents were provided with these percentages to help quantify their responses. We stratified respondents into two groups: those who responded always or most of the time as “wears masks over half the time” and those who responded sometimes or never as “wears masks less than half the time. We sorted each of the 15 sites into four geographic regions within the United States. There were 3 sites located in New Jersey, Massachusetts, and Pennsylvania which we categorized in the Northeast region. We categorized 3 sites in Michigan and Iowa as Midwest, and 3 sites in North Carolina, Louisiana, and Maryland as the South. There were 6 sites located on the West Coast from California and Washington State.

AbstractThis study reports a comprehensive empirical investigation of the nature and correlates of anti-mask attitudes during the COVID-19 pandemic. Accumulating evidence underscores the importance of facemasks, as worn by the general public, in limiting the spread of infection. Accordingly, mask wearing has become increasingly mandatory in public places such as stores and on public transit. Although the public has been generally adherent to mask wearing, a small but vocal group of individuals refuse to wear masks. Anti-mask protest rallies have occurred in many places throughout the world, sometimes erupting violently. Few empirical studies have examined the relationship between anti-mask attitudes and mask non-adherence and little is known about how such attitudes relate to one another or other factors (e.g., non-adherence to social distancing, anti-vaccination attitudes). To investigate these issues, the present study surveyed 2,078 adults from the US and Canada. Consistent with other surveys, we found that most (84%) people wore masks because of COVID-19. The 16% who did not wear masks scored higher on most measures of negative attitudes towards masks. Network analyses indicated that negative attitudes about masks formed an intercorrelated network, with the central nodes in the network being (a) beliefs that masks are ineffective in preventing COVID-19, and (b) psychological reactance (PR; i.e., an aversion to being forced to wear masks). These central nodes served as links, connecting the network of anti-masks attitudes to negative attitudes toward SARSCoV2 vaccination, beliefs that the threat of COVID-19 has been exaggerated, disregard for social distancing, and political conservatism. Findings regarding PR are important because, theoretically, PR is likely to strengthen other anti-masks attitudes (e.g., beliefs that masks are ineffective) because people with strong PR react with anger and counter-arguments when their beliefs are challenged, thereby leading to a strengthening of their anti-mask beliefs. Implications for improving mask adherence are discussed. MethodsSPSS file.

During national crises, political elites often rally ’round the flag, promoting a central message to restore unity and calm the public. COVID-19 provided a crisis. But did elites rally? The pandemic occurred at a point of extreme polarization in the United States, which threatens the potential for a rally. In this note, we argue that messaging about masking during COVID-19 offers an opportunity to test the comparative effects of a rally versus polarization. To do so, we use a unique measure: visual public communication by Members of Congress (MOCs). We extract 340,000 images from Congressional Twitter and Facebook accounts and employ supervised machine learning methods to identify when MOCs post images of people wearing masks. We find more evidence of rally effects and polarization. Trump’s actions are especially important: while Trump-loyal Republicans are less likely to post masks, all Republicans increase posting masks after Trump first appears wearing a face mask.

Context

This dataset was created by an AP Statistics class. The purpose was to learn about survey methodology and analysis using R.

The participants are all AP Statistics students from schools within the US.

The purpose of their study was to learn more about the habits and beliefs that young adults have regarding face masks.

Content

Below is a description of each variable. - Timestamp: The time and date that the respondent completed the survey. - Boarding: Whether the respondent was a day student or a boarding student. - Age: The age of the respondent. - Gender: The reported gender of the respondent. - ResidentialElder: Response to the question "Do you live with someone over the age of 65 years old?" - InteractedElder: Response to the question "Have you interacted with anyone over the age of 65 in the last month?" - Restaurant: The number of times the respondent reported eating at a restaurant within the last week. - PreventSpread: Response to the question "Do you believe that masks are effective at preventing the spread of the coronavirus?" - Reason: Response to the question "What is your primary reason for wearing a mask?" - Public: Response to the question "Do you wear a mask in public places when it is not required?"

Replication materials for "Taking the Cloth: Social norms and elite cues increase support for masks among white Evangelical Americans." Abstract: During the COVID-19 pandemic, the CDC and the WHO have recommended face masks as key to reducing viral transmission. Yet, in the United States, as the first wave erupted in the Summer of 2020, one fifth of individuals said they wore masks at most some of the time, and a majority said that people in their community wore masks at most some of the time. What strategies most effectively encourage compliance with this critical COVID-19 prevention measure? Relying on social identity theory, we experimentally assess two possible mechanisms of compliance, elite endorsement and social norms, among a representative sample of White U.S.– born Evangelicals, a group that has shown resistance to prevention measures. We find evidence for both mechanisms, but social norms play a remarkably important role – increasing support for mask-wearing by 6% with spillover effects on other prevention guidelines. Our findings confirm the role that appeals to norms and elite endorsements play in shaping individual behavior, and offer lessons for public health messaging.

Face mask dataset for facial recognition

This dataset contains over 11,100+ video recordings of people wearing latex masks, captured using 5 different devices.It is designed for liveness detection algorithms, specifically aimed at enhancing anti-spoofing capabilities in biometric security systems. By utilizing this dataset, researchers can develop more accurate facial recognition technologies, which is crucial for achieving the iBeta Level 2 certification, a benchmark for robust and… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/latex-mask-attack.

Recent studies suggest that covering the face inhibits the recognition of identity and emotional expressions. However, it might also make the eyes more salient, since they are a reliable index to orient our social and spatial attention. This study investigates (1) whether the pervasive interaction with people with face masks fostered by the COVID-19 pandemic modulates the processing of spatial information essential to shift attention according to other’s eye-gaze direction (i.e., gaze-cueing effect: GCE), and (2) whether this potential modulation interacts with motor responses (i.e., Simon effect). Participants were presented with face cues orienting their gaze to a congruent or incongruent target letter location (gaze-cueing paradigm) while wearing a surgical mask (Mask), a patch (Control), or nothing (No-Mask). The task required to discriminate the identity of the lateralized target letters by pressing one of two lateralized response keys, in a corresponding or a non-corresponding position with respect to the target. Results showed that GCE was not modulated by the presence of the Mask, but it occurred in the No-Mask condition, confirming previous studies. Crucially, the GCE interacted with Simon effect in the Mask and Control conditions, though in different ways. While in the Mask condition the GCE emerged only when target and response positions corresponded (i.e., Simon-corresponding trials), in the Control condition it emerged only when they did not correspond (i.e., Simon-non-corresponding trials). These results indicate that people with face masks induce us to jointly orient our visual attention in the direction of the seen gaze (GCE) in those conditions resembling (or associated with) a general approaching behavior (Simon-corresponding trials). This is likely promoted by the fact that we tend to perceive wearing the mask as a personal safety measure and, thus, someone wearing the face mask is perceived as a trustworthy person. In contrast, people with a patch on their face can be perceived as more threatening, therefore inducing a GCE in those conditions associated with a general avoidance behavior (Simon-non-corresponding trials).

Attacks with 2D Printed Masks of Indian People - Biometric Attack Dataset

The dataset consists of videos of individuals wearing printed 2D masks of different kinds and directly looking at the camera. Videos are filmed in different lightning conditions and in different places (indoors, outdoors). Each video in the dataset has an approximate duration of 3-4 seconds.

  The similar dataset that includes all ethnicities - Printed 2D Masks Attacks Dataset






  Types of… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/attacks-with-2d-printed-masks-of-indian-people.

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Source

This data comes from the New York Times Coronavirus (Covid-19) Data in the United States GitHub repository. They use it to power their interactive page(s) on Covid-19, such as Coronavirus in the U.S.: Latest Map and Case Count.

What's Included?

The primary data published here are the daily cumulative number of cases and deaths reported in each county and state across the U.S. since the beginning of the pandemic. We have also published these additional data sets:

• Prisons: Cases in prisons
• Colleges: Cases on college and university campuses.
• Excess deaths: The elevated overall number of deaths during the pandemic.
• Mask use: A July 2020 survey of how regularly people in each county wore masks.
• Averages and anomalies: A set of pre-computed rolling averages of cases and deaths for ease of analysis or use in making graphics, along with a set of days with anomalous data that have been excluded from the averages.

The cumulative & rolling averages for cases and deaths are continually updated, but the more specific data mentioned above for prisons, etc. is no longer being updated.

This includes data at the national, state, and county levels.

License and Attribution

If you use this data, you must attribute it to “The New York Times” in any publication. If you would like a more expanded description of the data, you could say “Data from The New York Times, based on reports from state and local health agencies.”

Acknowledgements

Header Image: https://www.pexels.com/photo/n95-face-mask-3993241/

More Information

See the original New York Times source README which is also included in this dataset.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This map is updated weekly and currently shows data through March 5, 2023, which will be the final update of this map.Note: Nebraska stopped reporting county level-results on 5/25/2021 and re-started on 9/26/21 with a lump-sum representing the previous four months - this impacted the weekly sum of cases fields.It shows COVID-19 Trend for the most recent Monday with a colored dot for each county. The larger the dot, the longer the county has had this trend. Includes Puerto Rico, Guam, Northern Marianas, U.S. Virgin Islands.The intent of this map is to give more context than just the current day of new data because daily data for COVID-19 cases is volatile and can be unreliable on the day it is first reported. Weekly summaries in the counts of new cases smooth out this volatility. Click or tap on a county to see a history of trend changes and a weekly graph of new cases going back to February 8, 2020. This map is updated every Monday* based on data through the previous Sunday. See also this version of the map for another perspective.COVID-19 Trends show how each county is doing and are updated daily. We base the trend assignment on the number of new cases in the past two weeks and the number of active cases per 100,000 people. To learn the details for how trends are assigned, see the full methodology. There are five trends:Emergent - New cases for the first time or in counties that have had zero new cases for 60 or more days.Spreading - Low to moderate rates of new cases each day. Likely controlled by local policies and individuals taking measures such as wearing masks and curtailing unnecessary activities.Epidemic - Accelerating and uncontrolled rates of new cases.Controlled - Very low rates of new cases.End Stage - One or fewer new cases every 5 days in larger populations and fewer in rural areas.*Starting 8/22/2021 we began updating on Mondays instead of Tuesdays as a result of optimizing the scripts that produce the weekly analysis. For more information about COVID-19 trends, see the full methodology. Data Source: Johns Hopkins University CSSE US Cases by County dashboard and USAFacts for Utah County level Data.

As of May 31, some 48 percent of respondents in the United States stated that people should wear protective face masks outside in the aftermath of the pandemic.

According to Cognitive Market Research, the global Mud Mask market size was USD 8615.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 15.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 3446.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 2584.56 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1981.50 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 430.76 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 172.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.9% from 2024 to 2031.
Hydrating is currently the fastest expanding category.

Market Dynamics of Mud Mask Market

Key Drivers for Mud Mask Market

Growing consumer awareness of skincare benefits

Growing consumer awareness of the benefits of skincare is a major driver of growth in the market for mud masks. As more people become educated about the importance of skincare routines and specific ingredients, they increasingly identify the benefits of using mud masks. These products are known for detoxifying, exfoliating, and hydrating the skin, making them appealing to a wide audience. The awareness has been spread through educational content from beauty influencers, social media platforms, and skincare blogs. Today, consumers are more likely to look for mud masks in their skincare routines due to a desire for healthier, clearer skin. This increased awareness creates greater demand while also challenging manufacturers to develop and market diverse products for consumers to pick their perfect mud masks, thus further supporting growth in this market. For instance, In July 2021, L'Oreal launched Pure Clay Masks, emphasising the skincare advantages of natural clay and advertising them through influencer collaborations to increase awareness.

Increasing preference for natural and organic ingredients

The increasing demand for mud masks is primarily driven by a natural and organic ingredient preference. Since consumers are becoming increasingly health-conscious, they are seeking products in the skincare market that have no harmful chemicals or artificial additives. This change to natural formulations fits well into a larger trend of prioritising wellness and sustainability. Mud masks, which are often mineral and botanical-based, attract this kind of consumer: they promise health benefits while being more holistic than regular masks. Brands emphasising organic origins and green policies are sure to gain an army of fans and strengthen their market position. Furthermore, because the awareness of side effects from synthetic ingredients is increasing, consumers are likely to opt for mud masks that identify as natural, thus raising demand in the market.

Restraint Factor for the Mud Mask Market

Skin sensitivity and allergic reactions

High sensitivity and allergenic reactions will hamper mud mask market growth significantly. Some customers have sensitive skin. A particular component of a mud mask might be irritating; therefore, some may experience unsavoury side effects resulting in redness, itchiness, or breakouts. These effects deter potential mud mask users. Consequently, customer hesitancy to make a purchase can arise due to adverse reactions. Negative reviews and word-of-mouth can also affect brand reputation and sales. Companies need to work on these issues by ensuring that the product is safe to use through adequate testing and proper ingredient lists and by providing hypoallergenic products for sensitive skin types, which can help reduce this restraint in the market.

Impact of Covid-19 on the Mud Mask Market

The impact of Covid-19 on the mud mask market is significant, considering that the pandemic brought about shifts in consumer behaviour and priorities. Many consumers stayed indoors during lockdowns and had more time at home; thus, many of them adopted self-care and wel...

Collected COVID-19 datasets from various sources as part of DAAN-888 course, Penn State, Spring 2022. Collaborators: Mohamed Abdelgayed, Heather Beckwith, Mayank Sharma, Suradech Kongkiatpaiboon, and Alex Stroud

**1 - COVID-19 Data in the United States ** Source: The data is collected from multiple public health official sources by NY Times journalists and compiled in one single file. Description: Daily count of new COVID-19 cases and deaths for each state. Data is updated daily and runs from 1/21/2020 to 2/4/2022. URL: https://github.com/nytimes/covid-19-data/blob/master/us-states.csv Data size: 38,814 row and 5 columns.

**2 - Mask-Wearing Survey Data ** Source: The New York Times is releasing estimates of mask usage by county in the United States. Description: This data comes from a large number of interviews conducted online by the global data and survey firm Dynata, at the request of The New York Times. The firm asked a question about mask usage to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. URL: https://github.com/nytimes/covid-19-data/blob/master/mask-use/mask-use-by-county.csv Data size: 3,142 rows and 6 columns

**3a - Vaccine Data – Global ** Source: This data comes from the US Centers for Disease Control and Prevention (CDC), Our World in Data (OWiD) and the World Health Organization (WHO). Description: Time series data of vaccine doses administered and the number of fully and partially vaccinated people by country. This data was last updated on February 3, 2022 URL: https://github.com/govex/COVID-19/blob/master/data_tables/vaccine_data/global_data/time_series_covid19_vaccine_global.csv
Data Size: 162,521 rows and 8 columns

**3b -Vaccine Data – United States ** Source: The data is comprised of individual State's public dashboards and data from the US Centers for Disease Control and Prevention (CDC). Description: Time series data of the total vaccine doses shipped and administered by manufacturer, the dose number (first or second) by state. This data was last updated on February 3, 2022. URL: https://github.com/govex/COVID-19/blob/master/data_tables/vaccine_data/us_data/time_series/vaccine_data_us_timeline.csv
Data Size: 141,503 rows and 13 columns

**4 - Testing Data ** Source: The data is comprised of individual State's public dashboards and data from the U.S. Department of Health & Human Services. Description: Time series data of total tests administered by county and state. This data was last updated on January 25, 2022. URL: https://github.com/govex/COVID-19/blob/master/data_tables/testing_data/county_time_series_covid19_US.csv
Data size: 322,154 rows and 8 columns

**5 – US State and Territorial Public Mask Mandates ** Source: Data from state and territory executive orders, administrative orders, resolutions, and proclamations is gathered from government websites and cataloged and coded by one coder using Microsoft Excel, with quality checking provided by one or more other coders. Description: US State and Territorial Public Mask Mandates from April 10, 2020 through August 15, 2021 by County by Day URL: https://data.cdc.gov/Policy-Surveillance/U-S-State-and-Territorial-Public-Mask-Mandates-Fro/62d6-pm5i Data Size: 1,593,869 rows and 10 columns

**6 – Case Counts & Transmission Level ** Source: This open-source dataset contains seven data items that describe community transmission levels across all counties. This dataset provides the same numbers used to show transmission maps on the COVID Data Tracker and contains reported daily transmission levels at the county level. The dataset is updated every day to include the most current day's data. The calculating procedures below are used to adjust the transmission level to low, moderate, considerable, or high.
Description: US State and County case counts and transmission level from 16-Aug-2021 to 03-Feb-2022 URL: https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-County-Level-of-Community-T/8396-v7yb Data Size: 550,702 rows and 7 columns

**7 - World Cases & Vaccination Counts ** Source: This is an open-source dataset collected and maintained by Our World in Data. OWID provides research and data to help against the world’s largest problems.
Description: This dataset includes vaccinations, tests & positivity, hospital & ICU, confirmed cases, confirmed deaths, reproduction rate, policy responses and other variables of interest. URL: https://github.com/owid/covid-19-data/tree/master/public/data Data Size: 67 columns and 157,000 rows

**8 - COVID-19 Data in the European Union ** Source: This is an open-source dataset collected and maintained by ECDC. It is an EU agency aimed at strengthening Europe's defenses against infectious diseases.
Description: This dataset co...

Sample composition compared to US population.

Introduction

Welcome to the Native American Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 3,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Dataset Updates &

Snapshot img { margin: 10px !important; } The anesthesia face masks market size has the potential to grow by USD 9.85 million during 2020-2024, and the market’s growth momentum will accelerate during the forecast period.

This report provides a detailed analysis of the market by product (disposable face masks, and reusable face masks) and geography (Asia, Europe, North America, and ROW). Also, the report analyzes the market’s competitive landscape and offers information on several market vendors, including Ambu AS, Armstrong Medical Ltd., General Electric Co., HSINER Co. Ltd., Intersurgical Ltd., Koninklijke Philips NV, Medtronic Plc, Smiths Group Plc, Teleflex Inc., and Vyaire Medical Inc.

Market Overview

Market Competitive Analysis

The market is fragmented, and the degree of fragmentation will decrease during the forecast period. Vendors are focusing on deploying different strategies, such as new product launches, to sustain in the competitive market environment. Koninklijke Philips NV, Medtronic Plc, Smiths Group Plc, Teleflex Inc., and Vyaire Medical Inc. are some of the major market participants. Although the rising number of surgical procedures will offer immense growth opportunities, the availability of substitutes will challenge the growth of the market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

To help clients improve their market position, this anesthesia face masks market forecast report provides a detailed analysis of the market leaders and offers information on the competencies and capacities of these companies. The report also covers details on the market’s competitive landscape and provides information on the products offered by various companies. Moreover, this anesthesia face masks market analysis report also includes information on the upcoming trends and challenges that will influence market growth. This will help companies create strategies to make the most of future growth opportunities.

This report provides information on the production, sustainability, and prospects of several leading companies, including:

Ambu AS
Armstrong Medical Ltd.
General Electric Co.
HSINER Co. Ltd.
Intersurgical Ltd.
Koninklijke Philips NV
Medtronic Plc
Smiths Group Plc
Teleflex Inc.
Vyaire Medical Inc.

Anesthesia Face Masks Market: Segmentation by Region

North America had the largest anesthesia face masks market share in 2019, and the region will offer several growth opportunities to market vendors during the forecast period. The increasing number of surgeries, the growing number of ambulatory surgery centers (ASCs), and expansion of anesthesia services by clinician-led organizations will significantly influence anesthesia face masks market growth in this region.

38% of the market’s growth will originate from North America during the forecast period. The US is the critical market for anesthesia face masks in North America. Market growth in this region will be slower than the growth of the market in Asia.

Anesthesia Face Masks Market: Segmentation by Product

The low-cost of the products, the increasing prevalence of HAIs among patients undergoing surgical procedures, and the presence of numerous vendors are the main factors for the growing demand for the disposable face masks segment in the global anesthesia face masks market. The availability of environment-friendly anesthesia face masks further adds to the growth of the disposable anesthesia face masks segment.

Market growth in this segment will be faster than the growth of the market in the reusable face masks segment. This report provides an accurate prediction of the contribution of all the segments to the growth of the anesthesia face masks market size.

Anesthesia Face Masks Market: Key Drivers and Trends

The growing geriatric population, increasing penetration of health insurance in emerging economies, and the increasing prevalence of chronic diseases have contributed to a considerable increase in the number of surgical procedures worldwide. Therefore, the rise in the number of surgeries implies a corresponding increase in the use of different medical supplies, such as syringes, aprons, and anesthesia face masks, which aid in the management of patients during surgical procedures. Additionally, the increasing prevalence of chronic diseases due to factors such as the high consumption of tobacco and alcohol, poor nutrition, and lack of physical activity require surgical intervention as part of the treatment, which, in turn, will contribute to the growth of the market in focus. For instance, the number of people with chronic conditions, such as cancer and cardiac disease, is increasing. For example, a person with ovarian cancer may require a hysterectomy, which is a surgical procedure to remove the uterus. Thus, the increasing number of surgeries will boost the de

This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 2000+ image of people wearing mask which are captured and crowdsourced from over 1000+ urban and rural locations, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Dataset Features

• Dataset size : 2000+ images
• Captured by : Over 1000+ crowdsource contributors
• Resolution : HD and above (1920x1080 and above)
• Location : Captured with 100+ locations across India
• Diversity : Various lighting conditions like day, night, varied distances, view points etc.
• Device used : Captured using mobile phones in 2020-2022
• Usage : Mask Detection, Face Cover Detection, Face Detection, Object Detection, Computer Vision, etc.

Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Beard Hydrogel Mask Sheet Market Outlook

According to our latest research, the Global Beard Hydrogel Mask Sheet market size was valued at $273 million in 2024 and is projected to reach $706 million by 2033, expanding at a robust CAGR of 11.2% during the forecast period of 2024–2033. The primary growth driver for this market is the increasing emphasis on male grooming and personal care worldwide, with consumers seeking advanced skincare solutions that cater specifically to beard maintenance and facial skin health. The market is also benefiting from the rising popularity of innovative, easy-to-use skincare products among men, who are now more willing than ever to invest in premium grooming experiences. This shift is further propelled by the influence of social media, celebrity endorsements, and a broader cultural acceptance of male self-care routines, all of which are fueling demand for beard hydrogel mask sheets across diverse demographics.

Regional Outlook

North America currently holds the largest share of the Beard Hydrogel Mask Sheet market, accounting for approximately 37% of the global revenue in 2024. This dominance can be attributed to the region’s mature grooming product landscape, high disposable incomes, and an established culture of personal care among men. The United States leads with a strong presence of both international and domestic brands, supported by robust distribution networks across specialty stores, supermarkets, and online channels. Additionally, aggressive marketing campaigns and celebrity endorsements have normalized male grooming, further boosting product uptake. Regulatory policies in North America also favor product innovation and safety, ensuring consumer trust and market stability. The region’s technological advancements and high consumer awareness continue to set benchmarks for the global market.

Asia Pacific is emerging as the fastest-growing region in the Beard Hydrogel Mask Sheet market, with a projected CAGR of 14.5% during 2024–2033. The surge in demand is driven by rapidly evolving consumer lifestyles, increasing urbanization, and a growing middle-class population with higher disposable incomes. Countries such as China, Japan, and South Korea are witnessing a cultural shift towards male grooming, with younger consumers particularly receptive to new skincare trends. The proliferation of e-commerce platforms and social media influencers has accelerated product awareness and adoption. Moreover, regional brands are leveraging traditional herbal ingredients and local preferences to create innovative offerings tailored to Asian skin types, further fueling market expansion. Government support for local manufacturing and export incentives also contribute to the region’s dynamic growth trajectory.

In emerging economies across Latin America, the Middle East, and Africa, the Beard Hydrogel Mask Sheet market is gradually gaining traction, albeit at a slower pace compared to developed regions. Adoption challenges persist due to lower consumer awareness, limited access to premium grooming products, and cultural perceptions regarding male skincare. However, localized demand is growing as urbanization increases and international brands expand their footprint through targeted marketing and partnerships with local retailers. Policy reforms aimed at improving import regulations and supporting small- and medium-sized enterprises are beginning to ease market entry barriers. Although these regions currently contribute a smaller share to the global market, their long-term potential remains significant as consumer education and disposable incomes rise.

Report Scope

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Venturi Mask Market Size 2024-2028

The venturi mask market size is forecast to increase by USD 2.74 billion at a CAGR of 15.63% between 2023 and 2028.

The market is experiencing significant growth due to the increasing prevalence of chronic respiratory disorders, such as asthma and COPD. This trend is driven by the rising number of people suffering from these conditions and the subsequent demand for effective respiratory treatment solutions. Additionally, there is a growing focus on improved product designs for medical devices to enhance patient comfort and compliance, while healthcare services continue to prioritize innovation and patient-centered care. However, unfavorable reimbursement scenarios pose a challenge to market growth. Despite this, advancements in technology and the development of innovative mask designs offer opportunities for market expansion. The market is expected to grow steadily due to the high prevalence of chronic respiratory disorders and the availability of advanced healthcare facilities. Companies in this market are investing in research and development to create masks with superior features, such as improved fit, ease of use, and enhanced filtration capabilities. 

What will be the Size of the Venturi Mask Market During the Forecast Period?

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The market, a segment of the global air pollution mitigation industry, is experiencing significant growth due to the rising prevalence of air pollution-related health issues, including preventable deaths from COPD and cardiovascular diseases. This trend is particularly prominent In the sports and geriatric populations, as physical activity and aging can exacerbate respiratory conditions. Cigarette smoke is another major contributor to air pollution and associated health risks, further driving demand for Venturi masks.
The homecare segment of the market is expected to dominate, driven by increasing healthcare costs and the growing elderly population. Economic, social, and political factors, including GDP growth rate, demographic shifts, and regulations, are influencing market dynamics. Quantitative data indicates a steady increase in market size, while qualitative data from subject-related experts advises on pricing trends and product lifecycle stages. Positive changes in regulations and public awareness of air pollution's health impacts are also contributing to market growth.

How is this Venturi Mask Industry segmented and which is the largest segment?

The venturi mask industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  COPD
  Asthma
  Others


End-user

  Hospitals and clinics
  Homecare
  ASCs


Product Type

  Adult Venturi Masks
  Pediatric Venturi Masks
  Disposable Adjustable Venturi Masks


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Asia

    China
    India
    Japan


  South America

    Brazil


  Middle East & Africa

    UAE


  Rest of World (ROW)

By Application Insights

The COPD segment is estimated to witness significant growth during the forecast period.

Chronic obstructive pulmonary disease (COPD) is a significant global health concern, being the fourth leading cause of death worldwide. With an estimated 12% of the global population affected, the prevalence and related healthcare costs are projected to increase in both developed and developing countries. The primary drivers of COPD include smoking and air pollution. Long-term oxygen therapy is the primary treatment for COPD patients with chronic respiratory failure, aiming to enhance their prognosis.

This market is influenced by various economic, political, and social scenarios. Macroeconomic analysis, value chain analysis, pricing analysis, and product development are crucial factors shaping the market's competitive position. External factors, such as regulations and positive/negative changes, also impact the market's growth trajectory. Understanding these elements is essential for businesses aiming to capitalize on opportunities and mitigate risks In the market.

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The COPD segment was valued at USD 747.90 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 41% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in North America held a significant market share in 2023, with the US being the p