COVID-19 Event Research Articles

Spam Review Detection Using Weighted Swarm Support Vector Machines and Pre-Trained Word Embedding for Multiple Languages

Before making a purchase, many find internet reviews to be a vital source of information. On top of that, companies may learn a lot about their products and services via these reviews. Having faith in these reviews was especially important during the COVID-19 pandemic, when many stayed inside and read reviews at a dizzying pace. The pandemic altered the atmosphere and people's preferences in addition to increasing the number of evaluations. Spam reviewers keep an eye on these changes and try to improve their sneaky techniques. In order to deceive customers or harm competitors, reviews that are deemed spam may include inaccurate, misleading, or dishonest information. Consequently, this work introduces a WSVM plus an HHO to identify spam reviews. The HHO is similar to an algorithm in that it optimises hyperparameters and uses feature weights. Using English, Spanish, and Arabic language corpora as datasets, the multilingual difficulty in spam reviews has been tackled. Ngram-3, TFIDF, whereas One-hot encoding are three methods for representing words, while pre-trained word incorporation (BERT) is another one that has been used. Each of the four such studies has shed light on and provided a solution to a different facet. From start to finish, the proposed technique beat rival cutting-edge algorithms in every test. For the Multi dataset, the WSVM-HHO achieved a success rate of 84.270 percent; for the English information set, 89.565 percent; for the Spanish information set, 71.913 percent; and for the Arabic dataset, 88.565 percent. Furthermore, we have extensively researched the review environment before to and during the COVID-19 event. To further enhance detection performance, it has been designed to merge its existing textual attributes with statistical information to build a new dataset.

Transforming Hope Labour: Badiou, the COVID-19 Event, and Contingent Academic Work

Transforming Hope Labour: Badiou, the COVID-19 Event, and Contingent Academic Work

Not Tourism Worldmaking: A Critical Dialogue with Hollinshead and Vellah on Post-Covid-19 Sensible Entanglements

Hollinshead proclaimed tourism worldmaking as the creative/imaginative and often false/faux processes that management agencies and mediating bodies use to favor particular representations of places and people. While this remains valid at an organizational level, the COVID-19 pandemic has radically (and maybe also hopefully) changed the very regimes of sensory apprehension on which tourism is based, thus also suggesting that we rethink the worldmaking aspects of its postindustrial creation (rather than production and consumption chains). Considering some recent discussions on what may happen to tourism after the end of the pandemic I claim that: 1) we should begin by reassessing the realm of the sensible, 2) talk more about “travel worldmaking”, and 3) reconsider the centrality of the traveler’s emotional work during world travels. The thesis develops at the intersection of the “must” (urgency to sustain), the “ought” (call to respect), and the “desire” (drive to enjoy). It calls for a reassessment of worldmaking agency as a structured form of action, which gestures towards a durable change in sensible entanglements between humans and the world. I engage in a critical collegial dialogue with Hollinshead and Vellah’s thesis that tourism as a postcolonial or postindustrial moment per se contributes to postidentity. Instead, I argue that after the COVID-19 event (among other viral worldmaking events threatening to eliminate humanity), world travelers resort to what is deemed accessible through their sensory capabilities within structured conditions. Tourism is thus also reimagined at a sensible level separately from the organizational/institutional processes that Hollinshead and Vellah placed center stage in their thesis.

Tail risk transmission from the United States to emerging stock Markets: Empirical evidence from multivariate quantile analysis

This paper explores the transmission of risk from the United States equity market to the equity markets of the BRICS countries using a multivariate quantile process. The focus is on the contagion effect at the extreme quantiles, both upside and downside. In addition, a pseudo-impulse-response function (PIRF) analysis is conducted to investigate the responses of the five emerging stock markets to a shock in the US market. We conduct an empirical study against the backdrop of the COVID-19 event and the Russia-Ukraine conflict, finding that risk spillovers between the US stock market and the five emerging stock markets are significantly enhanced during the COVID-19 period. Moreover, a shock in the US market produces a stronger and more persistent negative effect at the downside quantiles compared to upside quantiles. However, we find little evidence of cross-market risk spillovers among the investigated variables during the Russia-Ukraine conflict period. We also discuss the implications of these findings for investors and policymakers in terms of portfolio holdings and policy coordination.

Prevalence and correlates of mental health problems among different occupations of medical workers during COVID-19 outbreak in China.

Health workers involved in the fight to prevent the COVID-19 outbreak were exposed to hazards. Detailed information on mental health problems in different medical occupations is crucial. To examined the prevalence of mental health issues in three medical occupations as well as the relationships between mental health problems and correlates in each occupation. This study utilizing the Questionnaire Star program was conducted among medical workers working at medical institutions in China from February 17 to 24, 2020. The Self-Reporting Questionnaire (SRQ-20), the Zung Self-rating Anxiety Scale (SAS), and the Zung Self-rating Depression Scale (SDS) were used to assess mental health problems. The prevalence of any mental health problems in the three occupations was 43.6, 34.6, and 32.9% for nurses, paramedical workers (PMWs), and doctors, respectively. Three occupations shared some correlates, such as being overworked, not having enough time to rest, support from colleagues, and previous mental health status. There were specific factors for each occupation. For doctors, age, educational level, living status, support from family, and previous physical status were related factors in mental health problems. Working in a designated hospital for treating COVID-19, having COVID-19 event exposures, and receiving support from family were associated with the mental health problems of the nurses. PMWs' mental health problems was linked to educational level and care from supervisors or heads of department. Different medical occupations have distinct impacts on mental health issues. Policy makers and mental health professionals working to prepare for potential disease outbreaks should be aware of multiple factors in different occupations.

Do Financial Performance and Sustainability Activities Matter for the Stock Performance? Investigating Non-Primary Consumer Goods in Indonesia

Purpose: This study seeks to investigate the impact of sustainability reporting index, particularly focusing on non-primary consumer goods, on stock returns during the period of 2018 to 2022, with a specific emphasis on the COVID-19 pandemic period. By analyzing the relationship between sustainability reporting and stock returns, the research aims to provide insights into the resilience and stability of companies in the face of market volatility induced by the COVID-19 crisis, particularly in the Indonesian stock market. Methods: The research methodology involves the application of multiple linear regression, incorporating dummy variables to empirically test the influence of sustainability reporting index on company stock returns during the COVID-19 event. Data collection entails gathering sustainability report data from each company within the non-primary consumer goods sector. Statistical analysis techniques are used to assess the impact of sustainability performance on stock return volatility and stability during the specified time period. Results and Discussion: The findings of the study reveal that firms within the non-primary consumer goods sector experienced substantial impacts on their leverage and profitability during the COVID-19 pandemic, significantly influencing stock returns. Notably, companies with strong performance in ESG (Environment, Social, and Governance) aspects demonstrate relatively stable stock prices amidst the epidemic. However, the analysis indicates that social reporting index performance exhibits higher volatility in stock prices compared to other ESG components. Moreover, the study observes a minimal increase in stock return volatility for companies with good ESG performance. Implications of the Research: The research outcomes have important implications for investors, policymakers, and corporate stakeholders, particularly in understanding the role of sustainability reporting in mitigating stock return volatility during times of crisis. The study underscores the significance of integrating ESG considerations into investment decisions and corporate strategies to enhance resilience and stability in the face of market disruptions such as the COVID-19 pandemic. Additionally, the research provides policy recommendations for government intervention through fiscal incentives to promote ESG investment and sustainable corporate practices. Originality/Value: This study contributes to the literature by examining the impact of sustainability reporting index on stock returns within the context of the COVID-19 pandemic, particularly focusing on the non-primary consumer goods sector in Indonesia. By providing empirical evidence of the relationship between ESG performance and stock return volatility during a period of crisis, the research offers valuable insights into the role of sustainability reporting in driving investor confidence and market stability. The findings highlight the importance of sustainable investment practices and provide actionable recommendations for policymakers and corporate decision-makers alike.

‚Learning from our mistakes and doing better‘

Which of the German corona measures were good, which were bad? What can we learn from the years 2020 to 2022 for future pandemics and other potential crisis situations? These were the key questions posed by MVF editor Prof Dr Reinhold Roski at the start of the MVF Congress ‚Pandemic impact on healthcare‘, the first part of which took place online in December 2023. After the opening statement by Sabine Dittmar MdB, Parliamentary State Secretary at the Federal Ministry of Health, who spoke about the ‚Corona impact on healthcare policy‘, Prof Dr Bertram Häussler, IGES Director and key idea generator of the congress, presented a somewhat different view of the political reality in his lecture ‚Effects of the corona pandemic - an overview‘. A view shared by Prof Dr Matthias Schrappe, University of Cologne, who was not stingy with constructive criticism in his presentation ‚Politics: fear instead of information?‘. The aim of the ‚Monitor Versorgungsforschung‘ congress was precisely not to apportion blame, but to analyse what can not only be learned from the Covid-19 event of the century for the German healthcare system, but what must be learned for future crises of the same or a completely different kind.

COVID-19 Event Extraction from Twitter via Extractive Question Answering with Continuous Prompts.

As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions surrounding perceptions and beliefs in preventative or treatment options. The 2020 shared task on COVID-19 event extraction (conducted as part of the W-NUT workshop during the EMNLP conference) introduced a new Twitter dataset for benchmarking event extraction from COVID-19 tweets. In this paper, we cast the problem of event extraction as extractive question answering using recent advances in continuous prompting in language models. On the shared task test dataset, our approach leads to over 5% absolute micro-averaged F1-score improvement over prior best results, across all COVID-19 event slots. Our ablation study shows that continuous prompts have a major impact on the eventual performance.

A Multi-Center Study Investigating Long COVID-19 in Healthcare Workers from North-Eastern Italy: Prevalence, Risk Factors and the Impact of Pre-Existing Humoral Immunity-ORCHESTRA Project.

Introduction: The impact of long-COVID-19 syndrome is rather variable, since it is influenced by several residual confounders. This study aimed to investigate the prevalence of long COVID-19 in healthcare workers (HCWs) from four university hospitals in north-eastern Italy: Trieste, Padua, Verona, and Modena-Reggio Emilia. Methods: During the period June 2022-August 2022, HCWs were surveyed for past COVID-19 infections, medical history, and any acute as well as post-COVID-19 symptoms. The prevalence of long COVID-19 was estimated at 30-60 days or 61+ days since first negative swab following first and second COVID-19 episode. Furthermore, the risk of long COVID-19 was investigated by multivariable logistic regression. Results were expressed as the adjusted odds ratio (aOR) with a 95% confidence interval (95%CI). Results: 5432 HCWs returned a usable questionnaire: 2401 were infected with SARS-CoV-2 at least once, 230 were infected at least twice, and 8 were infected three times. The prevalence of long COVID-19 after a primary COVID-19 infection was 24.0% at 30-60 days versus 16.3% at 61+ days, and 10.5% against 5.5% after the second SARS-CoV-2 event. The most frequent symptoms after a first COVID-19 event were asthenia (30.3%), followed by myalgia (13.7%), cough (12.4%), dyspnea (10.2%), concentration deficit (8.1%), headache (7.3%), and anosmia (6.5%), in decreasing order of prevalence. The risk of long COVID-19 at 30-60 days was significantly higher in HCWs hospitalized for COVID-19 (aOR = 3.34; 95%CI: 1.62; 6.89), those infected with SARS-CoV-2 during the early pandemic waves-namely the Wuhan (aOR = 2.16; 95%CI: 1.14; 4.09) or Alpha (aOR= 2.05; 95%CI: 1.25; 3.38) transmission periods-and progressively increasing with viral shedding time (VST), especially 15+ days (aOR = 3.20; 95%CI: 2.07; 4.94). Further determinants of long COVID-19 at 30-60 days since primary COVID-19 event were female sex (aOR = 1.91; 95%CI: 1.30; 2.80), age >40 years, abnormal BMI, or administrative services (reference category). In contrast, HCWs vaccinated with two doses before their primary infection (aOR = 0.57; 95%CI: 0.34; 0.94), undergraduate students, or postgraduate medical trainees were less likely to experience long COVID-19 at 30-60 days. Apart from pandemic waves, the main determinants of long COVID-19 at 30-60 days were confirmed at 61+ days. Conclusions: The risk of long COVID-19 following primary infection increased with the severity of acute disease and VST, especially during the initial pandemic waves, when more virulent viral strains were circulating, and susceptibility to SARS-CoV-2 was higher since most HCWs had not been infected yet, COVID-19 vaccines were still not available, and/or vaccination coverage was still building up. The risk of long COVID-19 therefore decreased inversely with humoral immunity at the individual level. Nevertheless, the prevalence of long COVID-19 was remarkably lower after SARS-CoV-2 reinfections regardless of vaccination status, suggesting that hybrid humoral immunity did not increase protection against the syndrome compared to immunity mounted by either natural infection or vaccination separately. Since the risk of long COVID-19 is currently low with Omicron and patients who developed the syndrome following SARS-CoV-2 infection in the early pandemic waves tend to return to a state of full health with time, a cost-effective approach to screen post-COVID-19 symptoms during the Omicron time could be restricted to vulnerable individuals developing severe disease and/or with prolonged VST.

Changes of Public Sector Human Resource Management (HRM)

Public sector human resource management is a way to manage human in a hierarchical activity which acts as the main rotor of efficiency and effectiveness of public-scale growth and development. As our environment changed in 2020 caused by covid-19 pandemic, so does the human resource management. Substantial changes in society impacted the way we shape management activity. This study aims to map the research trend changes of public sector human resource management in pre and post covid-19 period. We conduct bibliometric study to explore the factors and criteria of a toxic leader from open access scopus indexed journals using biblioshiny application limited to pre (2016 to 2019) and post (2020 to 2023) covid-19 periods. The criteria for filtering journal articles are based on PRISMA and JBI standards. Data suggested that covid-19 event ignite the research production. The research domains for pre covid-19 were focused on human resource growth and development, while post covid-19 domains were focused on survival and resilient. Country rank of most published paper related to the locus field also related to worldwide survey of economic growth in pre to post covid-19 pandemic. However, rapid increase of publication in post covid-19 period did not followed by study continuity and relatedness.

Making sense of the impact of COVID-19 event on college students’ online deviant behavior: does stress-is-enhancing mindset matter?

Making sense of the impact of COVID-19 event on college students’ online deviant behavior: does stress-is-enhancing mindset matter?

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation.

During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals' risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool (https://covid19risk.biosci.gatech.edu/) between 9/22/21 and 1/22/22. The website featured interactive elements, including a dynamic risk map, survey questions, and a risk quiz with accuracy feedback. After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in events that could spread COVID-19, especially for high-risk large events. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events but underestimate the risk of large events. Importantly, even participants who voluntarily sought information about COVID risks tended to misestimate exposure risk, demonstrating the need for intervention. Participants from liberal-leaning counties were more likely to use the website tools and more responsive to feedback about risk misestimation, indicating that political partisanship influences how individuals seek and engage with COVID-19 information. Lastly, we explored temporal dynamics and found that user engagement and risk estimation fluctuated over the course of the Omicron variant outbreak. Overall, we report an effective large-scale method for communicating viral exposure risk; our findings are relevant to broader research on risk communication, epidemiological modeling, and risky decision-making.

Do world stock markets “jump” together? A measure of high-frequency volatility risk spillover networks

We provide a systematic framework for studying high-frequency risk connectedness among world stock markets. Our framework, based on high-frequency realized volatility decompositions, TVP-VAR networks, and innovative extensions, shows risk connectedness among 16 world stock markets with respect to seven levels of high-frequency volatility. The findings are as follows. First, all seven levels of risk connectedness are significantly enhanced in response to extreme event shocks. The COVID-19 event led to a peak in total connectedness, and the event changed the structure of the spillover network the most, with markets in Asia-Pacific continent being the most affected by this event. The transmission of information is unique to different levels, with the “bad” volatility level being the strongest and the jump volatility level being the most sensitive. Second, there is a geographical effect on stock market performance, with the U.S. stock market being the most stable and dominant. The mainland Chinese stock market is the most vulnerable to volatility, being a net receiver of volatility 91.04% of the time. Third, we provide evidence of asymmetric spillovers at the level of “good-bad” volatility and “good-bad” jumps. The similarity between these two asymmetric effects is that the negative asymmetry is substantial and more pronounced in response to risky event shocks. The difference is that the asymmetry of “good-bad” jumps reflects more strongly the impact of crisis event shocks. Seven countries are dominated by pessimism most of the time, with the UK, Canada and Mexico being the main transmitters of pessimism, and their ability to transmit pessimism has increased during the crisis.

Machine learning augmentation reduces prediction error in collective forecasting: development and validation across prediction markets with application to COVID events

The recent COVID-19 pandemic highlighted the challenges for traditional forecasting. Prediction markets are a promising way to generate collective forecasts and could potentially be enhanced if high-quality crowdsourced inputs were identified and preferentially weighted for likely accuracy in real-time with machine learning. We aim to leverage human prediction markets with real-time machine weighting of likely higher accuracy trades to improve performance. The crowd sourced Almanis prediction market longitudinal platform (n=1822) and Next Generation Social Science (NGS2) platform (n=103) were utilised. A 43-feature model predicted accurate forecasters, those with top quintile relative Brier accuracy, with subsequent replication in two out-of-sample datasets (pboth <1×10-9). Trades graded by this model as having higher accuracy scores than others produced a greater AUC temporal gain in the overall market after vs before trade. Accuracy score-weighted forecasts had higher accuracy than market forecasts alone, particularly when the two systems disagreed by 5% or more for binary event prediction: the hybrid system demonstrating substantial % AUC gains of 13.2%, p=1.35×10-14 and 13.8%, p=0.003 in two out-of-sample datasets. When discordant, the hybrid model was correct for COVID-19 event occurrence 72.7% of the time vs 27.3% for market models, p=0.007. This net classification benefit was replicated in the separate Almanis B dataset, p=2.4×10-7. Real-time machine classification followed by weighting human trades according to likely accuracy improves collective forecasting performance. This could provide improved anticipation of and thus response to emerging risks. This work was supported by an AusIndustry R and D tax incentive program from the Department of Industry, Science, Energy and Resources, Australia, to SlowVoice Pty Ltd. (IR 2101990) and Fellowship (GNT 1110200) and Investigator grant (GNT 1197234) to A-L Ponsonby by the National Health and Medical Research Council of Australia.

Does Literacy Affect Business Sustainability? Evidence from MSMEs

After the Covid-19 event, most MSMEs faced problems that impacted the sustainability of MSMEs businesses. The problems faced by most MSMEs include a decrease in asset value. Based on resource-based theory, companies can utilize strategic assets in the form of both tangible and intangible assets so that companies can compete. This study aims to examine the effect of intangible assets in the form of examining the effect of financial and digital literacy on business sustainability. This research is a type of causal-comparative quantitative research. The sample in this study used convenient sampling and obtained a sample of 54 MSMEs. Multiple regression analysis is used to analyze research data. The authors collect data through a questionnaire with a Likert scale of 1 to 7. The results show that financial literacy has a positive effect on business sustainability and digital literacy has no effect on business sustainability. This research provides implications for MSMEs to be able to optimize intangible assets in company management.

Associations of COVID-19 Stressors and Postpartum Depression and Anxiety Symptoms in New Mothers.

Early reports during the COVID-19 pandemic showed pregnant and postpartum women have increased rates of anxiety and depression. We hypothesized that exposure to more COVID-19-related events (e.g., stay-at-home orders, school closures, work layoffs, family members ill with COVID-19; Event Exposure), greater perceived impact of COVID-19 events on the family (Family Impact), and less social support would be associated with more anxiety and depression symptoms among first-time mothers. We interviewed 125 first-time mothers of infants under 3 months of age from four pediatric primary care offices (June 2020 - February 2021) to assess COVID-19 experiences, anxiety and depression symptoms, and social support. Hierarchical linear regression evaluated relations between COVID-19 Event Exposure, COVID-19 Family Impact, and social support on maternal anxiety and depression symptoms. COVID-19 Event Exposure was not associated with depression or anxiety symptom scores. However, greater COVID-19 Family Impact was related to increased maternal depression and anxiety symptoms when controlling for COVID-19 Event Exposure. Reduced social support predicted higher depression symptom scores, but not anxiety symptom scores, when accounting for other variables. The number of COVID-19-related events experienced by first-time mothers did not predict anxiety or depression symptoms. However, greater perceived impact of COVID-19 on their family was associated with higher symptoms of anxiety and depression in these mothers. Pediatricians can promote resilience strategies to help new mothers adapt during the COVID-19 pandemic to help decrease anxiety and depression symptoms.

Research on COVID-19

It is proposed the description of the “circulatory system” (sensu Latour) of technoscience around COVID-19 - in particular regarding the development of vaccines and scientific evidence on treatment - as a didactic study case for Science Teaching, showing how current events can and should also be mobilized for the critical understanding of the Nature of Science in the classroom. From the description of the various actors involved in the COVID-19 event, we discuss how it is possible to articulate scientific knowledge and critical discussions about the Nature of Science to mobilize scientific literacy - that is, the mastery and internalization of a properly scientific way of reading the natural and social world, rejecting denialism and absolutism about a supposedly univocal and linear science. The goal of reaching scientific literacy can be developed by using the case study presented here, facilitating the articulation of these elements of science education through a contemporary episode. Thus, it is expected that the content and reasoning proposed in this paper can be used as a theoretical basis to be used in the classroom and can be adapted to a methodology that the teacher and students feel more comfortable with and to the educational context in question.

Republican Rules of Reproduction and "Flipping the Script" on U.S. Health Care Reform.

Given the relatively conservative and marketized nature of U.S. health care reform, it remains unclear both why Republican resistance has been so intractable through much of the Affordable Care Act's (ACA's) tenure and why it has so suddenly receded into the background. This article seeks an explanatory mechanism to make sense of the ACA's changing historical fortunes, from enactment to the present. It argues that the Republican Party's "rules of reproduction," a concept of historical sociology, best explains why the ACA met with such vociferous resistance and why that resistance has given way to surprising progress on coverage. It begins with a consideration of marketized U.S. health care, as well as the ACA's quest for expanded coverage-not structural rearrangement-as the basis for progressive change. Following this, I explore the "rules of reproduction" to explain Republican political actors' relentless attacks on the law. The final section considers how the historically-contingent COVID-19 event has dovetailed with ACA entrenchment, effectively "flipping the script" on Republican rules, making anti-Obamacare maneuvers far less politically palatable. It is in this political space that reform advocates have been able to seize opportunity and broaden access.

Development of PeduliLindungi Application Features Using Design Thinking

The PeduliLindungi app has supported the government in controlling the spread of COVID-19. The purpose of this research is to explore new user needs and create design prototypes for the latest features of the PeduliLindungi application. Design thinking is used to know empathizing, define, ideate, and prototype. Figma Tools was chosen to develop a prototype that visualizes the latest features of the PeduliLindungi application. The results of this research are information about pain points from application users, problem statements, problem solving ideas, and user interface features for the new PeduliLindungi application. Based on the results of user pain points, the features developed are covid-19 event advertisements, Online Registration, Night Mode, Covid-19 educational videos, Tourist Visits, and Vaccine History. Based on the evaluation results using the Single Easy Question (SEQ), it is found that advertising, registration, night mode, and vaccine history are worth 7, meaning that the level of use is basic to use. Tourist visits and educational videos are worth 5.75, which means that improvements need to be made by taking into account notes from respondents during the testing process.

COVID-19-related stigma among infected people in Sweden; psychometric properties and levels of stigma in two cohorts as measured by a COVID-19 stigma scale.

Epidemics have historically been accompanied by stigma and discrimination. Disease-related stigma has often been shown to have severe consequences for physical, mental and social wellbeing and lead to barriers to diagnosis, treatment and prevention. The aims of this study were to investigate if a HIV-related stigma measure could be adapted and valid and reliable to measure COVID-19-related stigma, and also to investigate levels of self-reported stigma and related factors among people in Sweden with experience of COVID-19 and compare levels of COVID-19-related stigma versus HIV-related stigma among persons living with HIV who had experienced a COVID-19 event. Cognitive interviews (n = 11) and cross-sectional surveys were made after the acute phase of the illness using a new 12-item COVID-19 Stigma Scale and the established 12-item HIV Stigma Scale in two cohorts (people who had experienced COVID-19 (n = 166/209, 79%) and people living with HIV who had experienced a COVID-19 event (n = 50/91, 55%). Psychometric analysis of the COVID-19 Stigma Scale was performed by calculating floor and ceiling effects, Cronbach's α and exploratory factor analysis. Levels of COVID-19 stigma between groups were analysed using the Mann-Whitney U test. Levels of COVID-19 and HIV stigma among people living with HIV with a COVID-19 event were compared using the Wilcoxon signed-rank test. The COVID-19 cohort consisted of 88 (53%) men and 78 (47%) women, mean age 51 (19-80); 143 (87%) living in a higher and 22 (13%) in a lower income area. The HIV + COVID-19 cohort consisted of 34 (68%) men and 16 (32%) women, mean age 51 (26-79); 20 (40%) living in a higher and 30 (60%) in a lower income area. The cognitive interviews showed that the stigma items were easy to understand. Factor analysis suggested a four-factor solution accounting for 77% of the total variance. There were no cross loadings, but two items loaded on factors differing from the original scale. All subscales had acceptable internal consistency, showed high floor and no ceiling effects. There was no statistically significant difference between COVID-19 stigma scores between the two cohorts or between genders. People living in lower income areas reported more negative self-image and concerns about public attitudes related to COVID-19 than people in higher income areas (median score 3 vs 3 and 4 vs 3 on a scale from 3-12, Z = -1.980, p = 0.048 and Z = -2.023, p = 0.024, respectively). People from the HIV + COVID-19 cohort reported more HIV than COVID-19 stigma. The adapted 12-item COVID-19 Stigma Scale may be valid and reliable for measurement of COVID-19-related stigma. However, specific items may need to be rephrased or replaced to better correspond to the COVID-19 context. People who had experienced COVID-19 reported low levels of COVID-19-related stigma in general but people from lower income areas had higher levels of negative self-image and concerns about public attitudes related to COVID-19 than people from areas with higher income, which may call for targeted interventions. Although exhibiting more pronounced HIV stigma levels, people living with HIV who had experienced COVID-19 reported COVID-19-related stigma of the same low magnitude as their peers not living with HIV.