Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies.

We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts.

Crowdfunding Campaigns and COVID-19 Misinformation.

Objectives. To understand whether and how crowdfunding campaigns are a source of COVID-19-related misinformation.Methods. We searched the GoFundMe crowdfunding platform using 172 terms associated with medical misinformation about COVID-19 prophylaxes and treatments. We screened resulting campaigns for those making statements about the ability of these searched-for or related terms to prevent or treat COVID-19.Results. There were 208 campaigns worldwide that requested $21 475 568, raised $324 305 from 4367 donors, and were shared 24 158 times. The most discussed interventions were dietary supplements and purported immune system boosters (n = 231), followed by other forms of complementary and alternative medicine (n = 24), and unproven medical interventions (n = 15). Most (82.2%) of the campaigns made definitive efficacy claims.Conclusions. Campaigners focused their efforts on dietary supplements and immune system boosters. Campaigns for purported COVID-19 treatments are particularly concerning, but purported prophylaxes could also distract from known effective preventative approaches. GoFundMe should join other online and social media platforms to actively restrict campaigns that spread misinformation about COVID-19 or seek to better inform campaigners about evidence-based prophylaxes and treatments.

Meet the Medicines-A Crowdsourced Approach to Collecting and Communicating Information about Essential Medicines Online.

The World Health Organization (WHO) maintains a list of medicines and medical devices, essential medicines, that should be available to everyone, to form a functioning healthcare system. Yet, many of these medicines remain out of reach for people around the world. One significant barrier to improving the accessibility of essential medicines is a paucity of information about both the extent and causes of this problem. E$$ENTIAL MEDICINE$ (E$$) is a citizen science project designed to investigate this deficit of information by recruiting members of the public to find, validate, compile and share information on essential medicines through an open, online database. Herein, we report an approach to crowdsourcing both the collection of information on the accessibility of essential medicines and the subsequent communication of these findings to diverse audiences. The Meet the Medicines initiative encourages members of the public to share information from the E$$ database, in a short video format appropriate for social media. This communication details the design and implementation of our crowdsourced approach and strategies for recruiting and supporting participants. We discuss data on participant engagement, consider the benefits and challenges of this approach and suggest ways to promote crowdsourcing practices for social and scientific good.

Hyperbolic modeling and assessment of hypothetical health behaviors during a viral outbreak using crowdsourced samples.

The COVID-19 pandemic provided an opportunity to investigate factors related to public response to public health measures, which could help better prepare implementation of similar measures for inevitable future pandemics. To understand individual and environmental factors that influence likelihood in engaging in personal and public health measures, three crowdsourced convenience samples from Amazon Mechanical Turk (MTurk) completed likelihood-discounting tasks of engaging in health behaviors given a variety of hypothetical viral outbreak scenarios. Experiment 1 assessed likelihood of mask wearing for a novel virus. Experiment 2 assessed vaccination likelihood based on efficacy and cost. Experiment 3 assessed likelihood of seeking health care based on number of symptoms and cost of treatment. Volume-based measures and three-dimensional modeling were used to analyze hypothetical decision making. Hypothetical public and personal health participation increased as viral fatality increased and generally followed a hyperbolic function. Public health participation was moderated by political orientation and trust in science, whereas treatment-seeking was only moderated by income. Analytic methods used in this cross-sectional study predicted population-level outcomes that occurred later in the pandemic and can be extended to various health behaviors.

Engaging Youth and Young Adults in the COVID-19 Pandemic Response via the "It's Our Turn" Crowdsourcing Contest.

As the coronavirus disease (COVID-19) pandemic continued to progress into 2021, appeals were made to take a stronger focus on the perceptions and practices of youth and young adults (YYAs) regarding COVID-19 mitigation, as well as the impact of mitigation strategies on the overall wellbeing of YYAs. In this paper, we describe our efforts to increase YYA engagement in Arizona's COVID-19 response by pairing embedded values from youth participatory action research (YPAR) with a crowdsourcing challenge contest design. The research protocol and implementation are described, followed by a thematic analysis of YYA-led messaging portrayed in 23 contest submissions and reflections formed by 223 community voters after viewing contest submissions. The authors conclude that a YYA-led crowdsourcing contest presented an opportunity to (a.) investigate the perceptions and behaviors of YYAs and their networks regarding the COVID-19 pandemic and mitigation efforts and (b.) amplify the voices of YYAs in the pandemic response. Perhaps even more importantly, this approach also offered insight into the exacerbated impact of the pandemic on YYA mental health and wellbeing, and the utility of YPAR in raising awareness of these effects among the contexts and social networks of YYAs.

Crowdsourcing in Plastic and Reconstructive Surgery: A Systematic Review with Reporting Recommendations.

BACKGROUND: Crowdsourcing uses online platforms to collect large data from laypersons and has been increasingly used over the past 5 years to answer questions about aesthetic and functional outcomes following plastic and reconstructive surgery. This systematic review evaluates crowdsourcing articles in plastic and reconstructive surgery based on study topic, participants, and effect size in the hopes of describing best practices. METHODS: A systematic search strategy was developed with a licensed librarian and attending plastic surgeon to query all articles using crowdsourcing in plastic and reconstructive surgery. Covidence systematic review manager was used by two independent reviewers to import articles, screen abstracts, evaluate full texts, and extract data. RESULTS: A search run on October 8, 2021, yielded 168 studies, of which 45 were ultimately included. Craniofacial surgery and aesthetic surgery collectively constituted over half of studies. Participants in plastic surgery crowdsourcing studies are more commonly from the United States, female, straight, 25 to 35 years old; have completed college; and earn $20,000 to $50,000 per year. Studies typically assessed aesthetic perceptions, cost approximately $350, ran a median of 9 days, included approximately 60 unique survey items, and included approximately 40 unique human images. CONCLUSIONS: Crowdsourcing is a relatively new, low-cost method of garnering high-volume data from laypersons that may further our understanding of public perception in plastic and reconstructive surgery. As with other nascent fields, there is significant variability in number of subjects used, subject compensation, and methodology, indicating an opportunity for quality improvement.

Applying and Testing a Crowdsourcing Platform to Support Home-Based Simulation.

The purpose of this report is to: (1) highlight challenges of transitioning the delivery of simulation from centralized, in-person laboratory to decentralized, home-based, online format; (2) suggest a solution that involves the use of crowdsourcing community-based 3-dimensional printers to produce affordable simulators; and (3) present exploratory research and a test case aiming to identify crowdsourcing frameworks to accomplish this. We present a test case that shows the potential of the proposed solution to scale up the decentralized simulation practices during and beyond the COVID-19 pandemic. As a largely uncharted territory, the test case highlighted successes and areas for improvement that need to be addressed through both theoretical and empirical research and testing before full implementation and scale-up.

Patients' Experiences With Therapeutic Approaches for Post-COVID Syndrome: Results of a Crowdsourced Research Survey.

Some patients develop multiple protracted sequelae after infection with SARS-CoV-2, collectively known as post-COVID syndrome or long COVID. To date, there is no evidence showing benefit of specific therapies for this condition, and patients likely resort to self-initiated therapies. We aimed to obtain information about therapies used by and needs of this population via inductive crowdsourcing research. Patients completed an online questionnaire about their symptoms and experiences with therapeutic approaches. Responses of 499 participants suggested few approaches (eg, mind-body medicine, respiratory therapy) had positive effects and showed a great need for patient-centered communication (eg, more recognition of this syndrome). Our findings can help design clinical studies and underscore the importance of the holistic approach to care provided by family medicine.

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening.

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.

The future of digital donation crowdfunding.

Amidst the Coronavirus crisis, many fundraising projects have emerged to relieve financial burdens resulting from social distancing policies. Crowdfunding is a way to raise money to fund a business, project or charity, through either social media or other online platforms to reach hundreds of potential sponsors. We developed guidelines for effective donation-based crowdfunding through online platforms. Using Futures Research (FR) technique, we conducted our analyses in 3 phases. In Phase 1, we reviewed relevant literature and conducted in-depth interviews of related parties. In Phase 2, we interviewed experts using Ethnographic Futures Research (EFR) technique. In Phase 3, we visualized the future using the principles of Futures Wheel, Cross-impact Matrix and Scenarios. Based on our findings, effective donation-based crowdfunding platforms should adopt Blockchain technology for transparency and accountability, and incentivize donations to keep backers loyal. Founders should be required to obtain fundraising licenses from relevant regulators. Finally, laws and regulations that protect platform users should be standardized internationally. Our proposed guidelines hope to improve the quality and transparency of future fundraising activities.

Crowdsourcing homemade facemasks: 772 U.S. health facilities' responses to personal protective equipment shortages in the first half of 2020.

PURPOSE: We examined 772 U.S. health facilities' responses to Personal Protective Equipment (PPE) shortages in the first half of 2020, as they crowdsourced face coverings from volunteer makers to be used as respiratory protection during crisis surge capacity. The purpose was to examine facemask specification requests from health facilities and develop a framework for crowdsourcing last resort PPE. DESIGN/METHODOLOGY/APPROACH: Homemade facemask donation requests from health facilities in 47 states systematically recorded in a public database maintained by public health graduate students at a major U.S. university were analysed. Open coding was used to content analyse facemask types and specifications, intended uses, delivery logistics and donation management strategies. FINDINGS: Our analysis revealed information gaps: Science-based information was scarce in 2020, leading to improvised specifications for facemask materials and designs. It also revealed the emergence of a crowdsourcing structure: Task specifications for volunteer facemasks makers, delivery logistics, and practical management of donations within the pandemic context. In anticipation of future pandemics and localised PPE shortages, we build on this empirical evidence to propose a framework for crowdsourcing science-informed facemasks from volunteers. Categorised within (a) logistics and workflow management, (b) task specifications and management, and (c) practical management of contributions functional areas, the framework outlines the required tasks and specifications for crowdsourcing. ORIGINALITY: A novel empirically derived framework for crowdsourcing homemade facemasks is proposed, based on empirical analysis and crowdsourcing system design strategies. Our findings and the framework may be used for refining crisis capacity guidelines, as part of strategic planning and preparation for future pandemics that disrupt supply chains and cause shortages in protective equipment.

COVID-19 cluster size and transmission rates in schools from crowdsourced case reports.

The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter ß, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates.

During the COVID-19 pandemic, public health officials promoted social distancing as a way to reduce SARS-CoV-2 transmission. The goal of social distancing is to reduce the number, proximity, and duration of face-to-face interactions between people. To achieve this, people shifted many activities online or canceled events outright. In education, some schools closed and shifted to online learning, while others continued classes in person with safety precautions. Better information about SARS-CoV-2 transmission in schools could help public health officials to make decisions of what activities to keep in person and when to suspend classes. If safety measures lower transmission in schools considerably, then closing schools may not be worth online education's social, educational, and economic costs. However, if transmission of SARS-CoV-2 in schools remains high despite measures, closing schools may be essential, despite the costs. Tupper et al. used data about COVID-19 cases in children attending in-person school in four Canadian provinces between 2020 and 2021 to fit a computer model of school transmission. On average, their analysis shows that one infected person in a school leads to between two and three further cases. Most of the time, no more students are infected, indicating that normally infection clusters are small; and only rarely does one infected person set off a large outbreak. The model also showed that measures to reduce transmission, like masking or small class sizes, were more effective than interventions such as keeping students with the same cohort all day (bubbling). Tupper et al. caution that their findings apply to the variants of SARS-CoV-2 circulating in Canada during the 2020-2021 school year, and may not apply to newer, highly transmissible strains like Omicron. However, the model could always be adapted to assess school or workplace transmission of more recent strains of SARS-CoV-2, and more generally of other diseases. Thus, Tupper et al. provide a new approach to estimating the rate of disease transmission and comparing the impact of different prevention strategies.

Evaluating and volunteering for crowdsourced interventions: Cross-sectional data on COVID-19 safety from a University Survey.

Despite many innovative ideas generated in response to COVID-19, few studies have examined community preferences for these ideas. Our study aimed to determine university community members' preferences for three novel ideas identified through a crowdsourcing open call at the University of North Carolina (UNC) for making campus safer in the pandemic, as compared to existing (i.e. pre-COVID-19) resources. An online survey was conducted from March 30, 2021 -May 6, 2021. Survey participants included UNC students, staff, faculty, and others. The online survey was distributed using UNC's mass email listserv and research directory, departmental listservs, and student text groups. Collected data included participant demographics, COVID-19 prevention behaviors, preferences for finalist ideas vs. existing resources in three domains (graduate student supports, campus tours, and online learning), and interest in volunteering with finalist teams. In total 437 survey responses were received from 228 (52%) staff, 119 (27%) students, 78 (18%) faculty, and 12 (3%) others. Most participants were older than age 30 years (309; 71%), women (332, 78%), and white (363, 83.1%). Five participants (1%) were gender minorities, 66 (15%) identified as racial/ethnic minorities, and 46 (10%) had a disability. Most participants preferred the finalist idea for a virtual campus tour of UNC's lesser-known history compared to the existing campus tour (52.2% vs. 16.0%). For graduate student supports, 41.4% of participants indicated no preference between the finalist idea and existing supports; for online learning resources, the existing resource was preferred compared to the finalist idea (41.6% vs. 30.4%). Most participants agreed that finalists' ideas would have a positive impact on campus safety during COVID-19 (81.2%, 79.6%, and 79.2% for finalist ideas 1, 2 and 3 respectively). 61 (14.1%) participants indicated interest in volunteering with finalist teams. Together these findings contribute to the development and implementation of community-engaged crowdsourced campus safety interventions during COVID-19.

UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio.

We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augmentation and cost-sensitive loss are incorporated (infected samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50 has been found to be effective. The unified framework also integrates deep ensemble learning and uncertainty estimation to integrate predictions from various base classifiers for generalisation and reliability. We ran a series of tests using the DiCOVA2021 challenge dataset to assess the efficacy of our proposed method, and the results show that our method has an AUC-ROC of 85.43 percent, making it a promising method for COVID-19 detection. The unified framework also demonstrates that audio may be used to quickly diagnose different respiratory disorders.

COVID-19 Misinformation and Social Network Crowdfunding: Cross-sectional Study of Alternative Treatments and Antivaccine Mandates.

BACKGROUND: Crowdfunding is increasingly used to offset the financial burdens of illness and health care. In the era of the COVID-19 pandemic and associated infodemic, the role of crowdfunding to support controversial COVID-19 stances is unknown. OBJECTIVE: We sought to examine COVID-19-related crowdfunding focusing on the funding of alternative treatments not endorsed by major medical entities, including campaigns with an explicit antivaccine, antimask, or antihealth care stances. METHODS: We performed a cross-sectional analysis of GoFundMe campaigns for individuals requesting donations for COVID-19 relief. Campaigns were identified by key word and manual review to categorize campaigns into "Traditional treatments," "Alternative treatments," "Business-related," "Mandate," "First Response," and "General." For each campaign, we extracted basic narrative, engagement, and financial variables. Among those that were manually reviewed, the additional variables of "mandate type," "mandate stance," and presence of COVID-19 misinformation within the campaign narrative were also included. COVID-19 misinformation was defined as "false or misleading statements," where cited evidence could be provided to refute the claim. Descriptive statistics were used to characterize the study cohort. RESULTS: A total of 30,368 campaigns met the criteria for final analysis. After manual review, we identified 53 campaigns (0.17%) seeking funding for alternative medical treatment for COVID-19, including popularized treatments such as ivermectin (n=14, 26%), hydroxychloroquine (n=6, 11%), and vitamin D (n=4, 7.5%). Moreover, 23 (43%) of the 53 campaigns seeking support for alternative treatments contained COVID-19 misinformation. There were 80 campaigns that opposed mandating masks or vaccination, 48 (60%) of which contained COVID-19 misinformation. Alternative treatment campaigns had a lower median amount raised (US $1135) compared to traditional (US $2828) treatments (P<.001) and a lower median percentile of target achieved (11.9% vs 31.1%; P=.003). Campaigns for alternative treatments raised substantially lower amounts (US $115,000 vs US $52,715,000, respectively) and lower proportions of fundraising goals (2.1% vs 12.5%) for alternative versus conventional campaigns. The median goal for campaigns was significantly higher (US $25,000 vs US $10,000) for campaigns opposing mask or vaccine mandates relative to those in support of upholding mandates (P=.04). Campaigns seeking funding to lift mandates on health care workers reached US $622 (0.15%) out of a US $410,000 goal. CONCLUSIONS: A small minority of web-based crowdfunding campaigns for COVID-19 were directed at unproven COVID-19 treatments and support for campaigns aimed against masking or vaccine mandates. Approximately half (71/133, 53%) of these campaigns contained verifiably false or misleading information and had limited fundraising success. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1001/jamainternmed.2019.3330.

Doing 'our bit': Solidarity, inequality, and COVID-19 crowdfunding for the UK National Health Service.

The expanding phenomenon of crowdfunding for healthcare creates novel potential roles for members of the public as fundraisers and donors of particular forms of provision. While sometimes interpreted as an empowering phenomenon (Gonzales et al., 2018), or a potentially useful communication of unmet needs (Saleh et al., 2021), scholars have predominantly been critical of the way in which crowdfunding for healthcare normalises unmet needs and exacerbates entrenched inequalities (Berliner and Kenworthy, 2017; Igra et al., 2021; Paulus and Roberts, 2018). We report a thematic analysis of the text of 945 fundraising appeals created on JustGiving and GoFundMe in the first months of the COVID-19 pandemic, where the recipient was NHS Charities Together's dramatically successful COVID-19 Urgent Appeal. Unlike in existing accounts of individual healthcare crowdfunding, we identify the relative absence of both coherent problem definition and of a fundable solution within the pages. Instead, appeals are dominated by themes of solidarity and duty during the UK's 'hard' lockdown of 2020. A national appeal reduces the risks of crowdfunding exacerbating existing health inequalities, but we argue that two kinds of non-financial consequences of collective crowdfunding require further exploration. Specifically, we need to better understand how expanded practices of fundraising co-exist with commitment to dutiful, means-based funding of healthcare via taxation. We must also attend to how celebration of the NHS as a national achievement, might squeeze spaces for critique and challenge. Analyses of crowdfunding need to explore both financial and non-financial aspects of practices within different health system and historical contexts.

Crowdfunding narratives and the valuation of vaccines for COVID-19.

Social media spreads information about vaccines and can be used to better understand public attitudes about them. Using American crowdfunding campaigns that mentioned COVID-19 vaccines from January 2020 to March 2021, this paper investigates public attitudes towards vaccines, specifically the perceived role vaccines could (or couldn't) play in ending the pandemic. We identified 776 crowdfunding campaigns and coded each for their aims and whether they valued vaccines as returning their community to a pre-pandemic state (utopian), helping some but not all people (cautious), and doubtful about the likely positive impacts of vaccines (skeptical). Cautious and skeptical valuations increased over time whereas utopian views declined. This paper uniquely situates attitudes toward COVID-19 vaccines in the context of financial need (as characterized by the campaigners). It offers insight into the "vaccine class gap" in America and demonstrates the usefulness of crowdfunding campaigns for assessing public views on vaccines.

Engaging Remote Aboriginal Communities in COVID-19 Public Health Messaging via Crowdsourcing.

Health comunication is a critical component of pandemic mitigation, but mainstream prevention messaging often lacks social, cultural and linguistic relevance to vulnerable populations. This community case study presents a novel, highly participatory pandemic prevention communication campaign that engaged individuals in remote Aboriginal communities of the Northern Territory of Australia directly in prevention messaging via crowdsourcing, and distributed videos to remote area post-codes via targeted Facebook advertising. Facebook metrics, administrative campaign data and national statistics are used to assess campaign reach and engagement. The case study discusses lessons learned from the campaign, including how seeking unscripted COVID-19 prevention video messaging can support community ownership of pandemic messaging, rapid content generation, and a high level of Facebook user engagement. It also discusses the effectiveness of targeting remote area post-codes via Facebook advertising both to reach the target audience, and to support quality improvement assessments to inform health communication decision-making in a low resource setting.