Public Perceptions and Attitudes Toward COVID-19 Nonpharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data

COVIDHealth: A novel labeled dataset and machine learning-based web application for classifying COVID-19 discourses on Twitter

The COVID-19 pandemic has sparked widespread health-related discussions on social media platforms like Twitter (now named 'X'). However, the lack of labeled Twitter data poses significant challenges for theme-based classification and tweet aggregation. To address this gap, we developed a machine learning-based web application that automatically classifies COVID-19 discourses into five categories: health risks, prevention, symptoms, transmission, and treatment. We collected and labeled 6,667 COVID-19-related tweets using the Twitter API, and applied various feature extraction methods to extract relevant features. We then compared the performance of seven classical machine learning algorithms (Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbor, Logistic Regression, and Linear SVC) and four deep learning techniques (LSTM, CNN, RNN, and BERT) for classification. Our results show that the CNN achieved the highest precision (90.41%), recall (90.4%), F1 score (90.4%), and accuracy (90.4%). The Linear SVC algorithm exhibited the highest precision (85.71%), recall (86.94%), and F1 score (86.13%) among classical machine learning approaches. Our study advances the field of health-related data analysis and classification, and offers a publicly accessible web-based tool for public health researchers and practitioners. This tool has the potential to support addressing public health challenges and enhancing awareness during pandemics. The dataset and application are accessible at https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.

Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing

PURPOSE

This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processing (NLP) and machine learning (ML) techniques on race-related spatiotemporal social media text data. Specifically, this study establishes the extent to which Latent Dirichlet Allocation (LDA) and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM)-based topic modeling determines social determinants of health (SDOH) categories, and how adequately custom named-entity recognition (NER) detects key SDOH factors from a race/ethnicity-related Reddit data corpus.

METHODS

In this study, we collected race/ethnicity-specific data from 5 location subreddits including New York City, NY; Los Angeles, CA; Chicago, IL; Philadelphia, PA; and Houston, TX from March to December 2019 (before COVID-19 pandemic) and from March to December 2020 (during COVID-19 pandemic). Next, we applied methods from natural language processing and machine learning to analyze SDOH issues from extracted Reddit comments and conversation threads using feature engineering, topic modeling, and custom named-entity recognition (NER).

RESULTS

Topic modeling identified 35 SDOH-related topics. The SDOH-based custom NER analyses revealed that the COVID-19 pandemic significantly impacted SDOH issues of marginalized Black and Asian communities. On average, the Social and Community Context (SCC) category of SDOH had the highest percent increase (366%) from the pre-pandemic period to the pandemic period across all locations and population groups. Some of the detected SCC issues were racism, protests, arrests, immigration, police brutality, hate crime, white supremacy, and discrimination.

CONCLUSION

Reddit social media platform can be an alternative source to assess the SDOH issues of marginalized Black and Asian communities during the COVID-19 pandemic. By employing NLP/ML techniques such as LDA/GSDMM-based topic modeling and custom NER on a race/ethnicity-specific Reddit corpus, we uncovered various SDOH issues affecting marginalized Black and Asian communities that were significantly worsened during the COVID-19 pandemic. As a result of conducting this research, we recommend that researchers, healthcare providers, and governments utilize social media and collaboratively formulate responses and policies that will address SDOH issues during public health crises.

Socio-economic pandemic modelling: case of Spain

A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances.

The effects of the COVID-19 lockdowns on motor skill development of 6- and 7-year old children in the Netherlands: a longitudinal study

BACKGROUND

The closing of schools and sports clubs during the COVID-19 lockdown raised questions about the possible impact on children's motor skill development. Therefore, we compared motor skill development over a one-year period among four different cohorts of primary school children of which two experienced no lockdowns during the study period (control cohorts) and two cohorts experienced one or two lockdowns during the study period (lockdown cohorts).

METHODS

A total of 992 children from 9 primary schools in Amsterdam (the Netherlands) participated in this study (age 5 - 7; 47.5% boys, 52.5% girls). Their motor skill competence was assessed twice, first in grade 3 (T1) and thereafter in grade 4 (T2). Children in control group 1 and lockdown group 1 were assessed a third time after two years (T3). Motor skill competence was assessed using the 4-Skills Test, which includes 4 components of motor skill: jumping force (locomotion), jumping coordination (coordination), bouncing ball (object control) and standing still (stability). Mixed factorial ANOVA's were used to analyse our data.

RESULTS

No significant differences in motor skill development over the study period between the lockdown groups and control groups (p > 0.05) were found, but a difference was found between the two lockdown groups: lockdown group 2 developed significantly better than lockdown group 1 (p = 0.008). While socioeconomic status was an effect modifier, sex and motor ability did not modify the effects of the lockdowns.

CONCLUSIONS

The COVID-19 lockdowns in the Netherlands did not negatively affect motor skill development of young children in our study. Due to the complexity of the factors related to the pandemic lockdowns and the dynamic systems involved in motor skill development of children, caution must be taken with drawing general conclusions. Therefore, children's motor skill development should be closely monitored in the upcoming years and attention should be paid to individual differences.

Adaptive COVID-19 Mitigation Strategies: Tradeoffs between Trigger Thresholds, Response Timing, and Effectiveness

Background. To support proactive decision making during the COVID-19 pandemic, mathematical models have been leveraged to identify surveillance indicator thresholds at which strengthening nonpharmaceutical interventions (NPIs) is necessary to protect health care capacity. Understanding tradeoffs between different adaptive COVID-19 response components is important when designing strategies that balance public preference and public health goals. Methods. We considered 3 components of an adaptive COVID-19 response: 1) the threshold at which to implement the NPI, 2) the time needed to implement the NPI, and 3) the effectiveness of the NPI. Using a compartmental model of SARS-CoV-2 transmission calibrated to Minnesota state data, we evaluated different adaptive policies in terms of the peak number of hospitalizations and the time spent with the NPI in force. Scenarios were compared with a reference strategy, in which an NPI with an 80% contact reduction was triggered when new weekly hospitalizations surpassed 8 per 100,000 population, with a 7-day implementation period. Assumptions were varied in sensitivity analysis. Results. All adaptive response scenarios substantially reduced peak hospitalizations relative to no response. Among adaptive response scenarios, slower NPI implementation resulted in somewhat higher peak hospitalization and a longer time spent under the NPIs than the reference scenario. A stronger NPI response resulted in slightly less time with the NPIs in place and smaller hospitalization peak. A higher trigger threshold resulted in greater peak hospitalizations with little reduction in the length of time under the NPIs. Conclusions. An adaptive NPI response can substantially reduce infection circulation and prevent health care capacity from being exceeded. However, population preferences as well as the feasibility and timeliness of compliance with reenacting NPIs should inform response design.

Highlights

This study uses a mathematical model to compare different adaptive nonpharmaceutical intervention (NPI) strategies for COVID-19 management across 3 dimensions: threshold when the NPI should be implemented, time it takes to implement the NPI, and the effectiveness of the NPI.All adaptive NPI response scenarios considered substantially reduced peak hospitalizations compared with no response.Slower NPI implementation results in a somewhat higher peak hospitalization and longer time spent with the NPI in place but may make an adaptive strategy more feasible by allowing the population sufficient time to prepare for changing restrictions.A stronger, more effective NPI response results in a modest reduction in the time spent under the NPIs and slightly lower peak hospitalizations.A higher threshold for triggering the NPI delays the time at which the NPI starts but results in a higher peak hospitalization and does not substantially reduce the time the NPI remains in force.

"Do as I say but not as I do": Influence of political leaders' populist communication styles on public adherence in a crisis using the global case of COVID-19 movement restrictions

This paper explores the influence of political leaders' populist communication styles on public adherence to government policies regarding COVID-19 containment. We adopt a mixed-methods approach that combines: theory building with a nested multicase study design for Study 1 and an empirical study in a natural setting for Study 2. Based on the results from Studies 1 and 2, we develop two propositions that we further explain theoretically: (P1) countries with political leaders associated with engaging or intimate populist communication styles (i.e., the UK, Canada, Australia, Singapore, and Ireland) exhibit better public adherence to their governments' COVID-19 movement restrictions than do countries with political leaders associated with communication styles that combine the champion of the people and engaging styles (i.e., the US); (P2) the country whose political leader is associated with a combination of engaging and intimate populist communication styles (i.e., Singapore) exhibits better public adherence to the government's COVID-19 movement restrictions than do countries whose political leaders adopted solely engaging or solely intimate styles, namely, the UK, Canada, Australia, and Ireland. This paper contributes to the research on political leadership in crises and populist political communication.

A systematic review of the use of topic models for short text social media analysis

Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models' limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-023-10471-x.

Diachronic profile of startup companies through social media

Social media platforms have become powerful tools for startups, helping them find customers and raise funding. In this study, we applied a social media intelligence-based methodology to analyze startups’ content and to understand how their communication strategies may differ during their scaling process. To understand if a startup’s social media content reflects its current business maturation position, we first defined an adequate life cycle model for startups based on funding rounds and product maturity. Using Twitter as the source of information and selecting a sample of known Portuguese IT startups at different phases of their life cycle, we analyzed their Twitter data. After preprocessing the data, using latent Dirichlet allocation, topic modeling techniques enabled the categorization of the data according to the topics arising in the published contents of the startups, making it possible to discover that contents can be grouped into five specific topics: “Fintech and ML,” “IT,” “Business Operations,” “Product/Service R&D,” and “Bank and Funding.” By comparing those profiles against the startup’s life cycle, we were able to understand how contents change over time. This provided a diachronic profile for each company, showing that while certain topics remain prevalent in the startup’s scaling, others depend on a particular phase of the startup’s cycle. Our analysis revealed that startups’ social media content differs along their life cycle, highlighting the importance of understanding how startups use social media at different stages of their development.

A local government area based Covid-19 vulnerability analysis in Nigeria

Vulnerability to COVID-19 has been widely studied from varying perspectives, but susceptibility at the grassroots has not been adequately considered. The vulnerability of the 774 local government areas (LGAs) in Nigeria to COVID-19 and its predictors were determined using road distances from the two major entry airports in the country to each of the LGAs, socio-economic indices, quality of hygiene, and the level of exposure to mass media. LGA population data were obtained from the National Population Commission of Nigeria, while socio-economic and socio-demographic data were extracted from the 2018 NDHS report. Distances were determined with the aid of an online distance calculator, distantias. The single most important predictor was female non-exposure to mass media. Distances from the COVID-19 epicentres of Lagos and Abuja did not contribute much to the model. Thus, the result highlighted the strategic position of women in the African community and the potential of the mass media in disease control. The suppression of the established effect of distance suggests that vulnerability to COVID-19 in developing countries may not follow the path assumed in developed countries. Thus, the way to contain the disease in developing countries might be the active enlightenment of women.

Detecting and analyzing topics of massive COVID-19 related tweets for various countries

With the flare-up of the COVID-19 infection since 2020, COVID-19 has been one of the hottest topics on Twitter. Topic modeling is one of the most popular analyses, which extracts the topics from the text. This paper proposes a method to extract the most-discussed topics for 32 countries of the world. In this regard, more than five million related tweets have been studied, and a method based on content analysis is proposed to identify the exact location of each tweet. Then, by using the statistical algorithm of Latent Dirichlet Allocation, the main topics of the tweets are identified. By leveraging sentiment analysis, the topics are afterward divided into positive and negative groups, and their trends in a quarterly period are investigated for the countries under study. The outcome of the analysis of time trends shows that for most countries, the trend of negative topics is highly correlated with the number of confirmed cases of COVID-19.

Persuasive Messages for Improving Adherence to COVID-19 Prevention Behaviors: Randomized Online Experiment

BACKGROUND

Adherence to nonpharmaceutical interventions for COVID-19, including physical distancing, masking, staying home while sick, and avoiding crowded indoor spaces, remains critical for limiting the spread of COVID-19.

OBJECTIVE

The aim of this study was to test the effectiveness of using various persuasive appeals (deontological moral frame, empathy, identifiable victim, goal proximity, and reciprocity) at improving intentions to adhere to prevention behaviors.

METHODS

A randomized online experiment using a representative sample of adult Canadian residents with respect to age, ethnicity, and province of residence was performed from March 3 to March 6, 2021. Participants indicated their intentions to follow public health guidelines, saw one of six flyers featuring a persuasive appeal or no appeal, and then rated their intentions a second time. Known correlates of attitudes toward public health measures were also measured.

RESULTS

Intentions to adhere to public health measures increased in all appeal conditions. The message featuring an empathy appeal resulted in a greater increase in intentions than the control (no appeal) message. Moreover, the effectiveness of persuasive appeals was moderated by baseline intentions. Deontological, empathy, identifiable victim, and reciprocity appeals improved intentions more than the control message, but only for people with lower baseline intentions to adhere to nonpharmaceutical interventions.

CONCLUSIONS

Public health marketing campaigns aiming to increase adherence to COVID-19 protective behaviors could achieve modest gains by employing a range of persuasive appeals. However, to maximize impact, it is important that these campaigns be targeted to the right individuals.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05722106; https://clinicaltrials.gov/ct2/show/NCT05722106.

Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Using Primary Care Clinical Text Data and Natural Language Processing to Identify Indicators of COVID-19 in Toronto, Canada

The objective of this study was to investigate whether a rule-based natural language processing (NLP) system, applied to primary care clinical text data, could be used to monitor COVID-19 viral activity in Toronto, Canada. We employed a retrospective cohort design. We included primary care patients with a clinical encounter between January 1, 2020 and December 31, 2020 at one of 44 participating clinical sites. During the study timeframe, Toronto first experienced a COVID-19 outbreak between March-2020 and June-2020; followed by a second viral resurgence from October-2020 through December-2020. We used an expert derived dictionary, pattern matching tools and contextual analyzer to classify primary care documents as 1) COVID-19 positive, 2) COVID-19 negative, or 3) unknown COVID-19 status. We applied the COVID-19 biosurveillance system across three primary care electronic medical record text streams: 1) lab text, 2) health condition diagnosis text and 3) clinical notes. We enumerated COVID-19 entities in the clinical text and estimated the proportion of patients with a positive COVID-19 record. We constructed a primary care COVID-19 NLP-derived time series and investigated its correlation with independent/external public health series: 1) lab confirmed COVID-19 cases, 2) COVID-19 hospitalizations, 3) COVID-19 ICU admissions, and 4) COVID-19 intubations. A total of 196,440 unique patients were observed over the study timeframe, of which 4,580 (2.3%) had at least one positive COVID-19 document in their primary care electronic medical record. Our NLP-derived COVID-19 time series describing the temporal dynamics of COVID-19 positivity status over the study timeframe demonstrated a pattern/trend which strongly mirrored that of other external public health series under investigation. We conclude that primary care text data passively collected from electronic medical record systems represent a high quality, low-cost source of information for monitoring/surveilling COVID-19 impacts on community health.

Actitudes y opiniones de la población adolescente sobre las recomendaciones de protección de la salud frente a la COVID-19

Objetivo: determinar las opiniones y actitudes de los estudiantes de enseñanza secundaria sobre las medidas de prevención frente al virus SARS-CoV-2, así como analizar los factores asociados a una mejor actitud hacia ellas. Diseño: estudio descriptivo transversal. Emplazamiento: centros de enseñanza secundaria. Participantes: 1.886 adolescentes que cursaban enseñanza secundaria de quince institutos de cinco comunidades autónomas. Mediciones principales: mediante encuesta anónima autocumplimentada, se valoraron aspectos sociodemográficos, hábitos de salud (actividad física, consumo de alcohol y tabaco) y opiniones, percepciones y actitudes sobre las recomendaciones preventivas durante la pandemia de la COVID-19. Resultados: edad media: 13,8 años (desviación estándar [DE]:1,3). El 88,6% (intervalo de confianza [IC] 95%: 87,1-90,1) conocía las recomendaciones. El 43,4% (IC 95%: 41,2-45,7) cumplió todas las medidas durante el confinamiento y el 31,3% (IC 95%: 29,2-33,5) cumplió todas las medidas durante el resto de pandemia. La medida peor considerada fue el adelanto del cierre de establecimientos (36,1%), seguida del cierre entre ciudades (35%) y de evitar reuniones (26,6%). Mediante regresión logística, fueron variables asociadas de forma independiente a una mejor actitud hacia las medidas durante la pandemia: menor edad (odds ratio [OR]: 1,14), no consumir alcohol (OR: 2,11), conocer las recomendaciones (OR: 2,11) y creer que existirán consecuencias negativas para el país (OR: 1,48). Conclusiones: los jóvenes y las jóvenes mostraron una actitud positiva hacia las medidas frente a la COVID-19 y una mayoría las conocía. Cumplieron en gran medida las recomendaciones preventivas, aunque no en su totalidad, siendo superior su cumplimiento durante el confinamiento. La opinión sobre las medidas se relaciona con la edad, la actitud con el alcohol, el conocimiento de las medidas y las posibles consecuencias de la pandemia. Palabras clave: promoción de la salud, prevención de enfermedades, salud del adolescente, COVID-19.

Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates

Background

Non-pharmaceutical interventions (NPIs) aim to reduce the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections mostly by limiting contacts between people where virus transmission can occur. However, NPIs limit social interactions and have negative impacts on economic, physical, mental and social well-being. It is, therefore, important to assess the impact of NPIs on reducing the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations to justify their use.

Methods

Dynamic regression models accounting for autocorrelation in time series data were used with data from six Canadian provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Québec) to assess 1) the effect of NPIs (measured using a stringency index) on SARS-CoV-2 transmission (measured by the effective reproduction number), and 2) the effect of the number of hospitalized COVID-19 patients on the stringency index.

Results

Increasing stringency index was associated with a statistically significant decrease in the transmission of SARS-CoV-2 in Alberta, Saskatchewan, Manitoba, Ontario and Québec. The effect of stringency on transmission was time-lagged in all of these provinces except for Ontario. In all provinces except for Saskatchewan, increasing hospitalization rates were associated with a statistically significant increase in the stringency index. The effect of hospitalization on stringency was time-lagged.

Conclusion

These results suggest that NPIs have been effective in Canadian provinces, and that their implementation has been, in part, a response to increasing hospitalization rates of COVID-19 patients.

Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia

Background

Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging.

Objective

The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit.

Methods

We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19–related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country.

Results

Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19–related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits.

Conclusions

This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.

In it together?: Exploring solidarity with frontline workers in the United Kingdom and Ireland during COVID-19

The phrase 'in it together' has been used liberally since the outbreak of COVID-19, but the extent that frontline workers felt 'in it together' is not well understood. Here, we consider the factors that built (or eroded) solidarity while working through the pandemic, and how frontline workers navigated their lives through periods of disconnection. Semi-structured interviews with 21 frontline workers, across all sectors, were conducted in the United Kingdom and Ireland. The qualitative data were analysed systematically using reflexive thematic analysis. The three themes identified in the data were: (1) Solidarity as central to frontline experiences; (2) Leadership as absent, shallow and divisive: highlighting 'us-them' distinctions and (3) The rise of 'us' and 'we' among colleagues. Our research offers insights into how frontline workers make sense of their experiences of solidarity and discordance during the first year of the COVID-19 pandemic, with relevance for government and organizational policy-makers shaping future conditions for frontline workers.

Assessing the impact of non-pharmaceutical interventions (NPIs) and BCG vaccine cross-protection in the transmission dynamics of SARS-CoV-2 in eastern Africa

Objective

The outbreak of the novel coronavirus disease 2019 (COVID-19) is still affecting African countries. The pandemic presents challenges on how to measure governmental, and community responses to the crisis. Beyond health risks, the socio-economic implications of the pandemic motivated us to examine the transmission dynamics of COVID-19 and the impact of non-pharmaceutical interventions (NPIs). The main objective of this study was to assess the impact of BCG vaccination and NPIs enforced on COVID-19 case-death-recovery counts weighted by age-structured population in Ethiopia, Kenya, and Rwanda. We applied a semi-mechanistic Bayesian hierarchical model (BHM) combined with Markov Chain Monte Carlo (MCMC) simulation to the age-structured pandemic data obtained from the target countries.

Results

The estimated mean effective reproductive number (R_t) for COVID-19 was 2.50 (C1: 1.99–5.95), 3.51 (CI: 2.28–7.28) and 3.53 (CI: 2.97–5.60) in Ethiopia, Kenya and Rwanda respectively. Our results indicate that NPIs such as lockdowns, and curfews had a large effect on reducing R_t. Current interventions have been effective in reducing R_t and thereby achieve control of the epidemic. Beyond age-structure and NPIs, we found no significant association between COVID-19 and BCG vaccine-induced protection. Continued interventions should be strengthened to control transmission of SARS-CoV-2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-022-06171-4.

Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster-Based BERT Topic Modeling Approach

BACKGROUND

The COVID-19 pandemic has spotlighted the politicization of public health issues. A public health monitoring tool must be equipped to reveal a public health measure's political context and guide better interventions. In its current form, infoveillance tends to neglect identity and interest-based users, hence being limited in exposing how public health discourse varies by different political groups. Adopting an algorithmic tool to classify users and their short social media texts might remedy that limitation.

OBJECTIVE

We aimed to implement a new computational framework to investigate discourses and temporal changes in topics unique to different user clusters. The framework was developed to contextualize how web-based public health discourse varies by identity and interest-based user clusters. We used masks and mask wearing during the early stage of the COVID-19 pandemic in the English-speaking world as a case study to illustrate the application of the framework.

METHODS

We first clustered Twitter users based on their identities and interests as expressed through Twitter bio pages. Exploratory text network analysis reveals salient political, social, and professional identities of various user clusters. It then uses BERT Topic modeling to identify topics by the user clusters. It reveals how web-based discourse has shifted over time and varied by 4 user clusters: conservative, progressive, general public, and public health professionals.

RESULTS

This study demonstrated the importance of a priori user classification and longitudinal topical trends in understanding the political context of web-based public health discourse. The framework reveals that the political groups and the general public focused on the science of mask wearing and the partisan politics of mask policies. A populist discourse that pits citizens against elites and institutions was identified in some tweets. Politicians (such as Donald Trump) and geopolitical tensions with China were found to drive the discourse. It also shows limited participation of public health professionals compared with other users.

CONCLUSIONS

We conclude by discussing the importance of a priori user classification in analyzing web-based discourse and illustrating the fit of BERT Topic modeling in identifying contextualized topics in short social media texts.

Strategic exploration of the COVID-19 prevention campaign message: based on South Koreans' perception type

Background

Many questions have been raised in the ongoing battle against COVID-19: How does the public perceive the COVID-19 prevention campaign as a member of the community?; What made the perception of the experts and the public on COVID-19 change from ‘simple’ to ‘serious’ epidemic?; What is the risk perception on health?; and what are the effective messages of the government’s campaign about disease prevention? As such, this study aimed to examine the perception of the public about the government’s campaign against COVID-19. Moreover, this study investigated the more effective messaging strategies for the campaign through subjective values, thoughts, and attitudes about the information dissemination, which became the basis for the degree of people’s participation in the disease prevention campaign. 

Method 

In order to investigate the public perception on the campaign messages that are promoted by the government for prevention of COVID-19, this study implemented the Q methodology that studies subjective attributes of humans, unlike existing empirical studies. The Q methodology is an approach that endeavors to discover complex issues in human subjectivity through empirical studies. In order to determine the factors that trigger people’s voluntary and active practices and the motivation for disease prevention, the Q methodology is implemented to examine human subjectivity, thoughts, and attitudes. When it comes to the disease prevention campaigns that require strong civic awareness as members of the society, the rationale that induces people to participate in the campaign voluntarily and actively is based on their subjectivities, such as values, thoughts, and thinking. The voluntary awareness and behavior of the public campaign participants are based on their subjective perception about the given message.

Results

In this study, it was ascertained that there were four different types of perceptions among Koreans on the message of the COVID-19 prevention campaign. The four perceptions are as follows: Type 1 is ‘the social threat caused by people with COVID-19 related symptoms;’ Type 2 is ‘the relational measures through personal hygiene;’ Type 3 is ‘the dependence on the social system due to the disease;’ and Type 4 is ‘the avoidance of the symptoms caused by human contact.’

Conclusion

As a result of this study, it was possible to draw a correlation between people’s perception of the campaign message for COVID-19 prevention and campaign messages. The response method of the campaign message must be differentiated according to the type of people’s perception of the disease prevention campaign, and the message development required by stages. The different characteristics of each type are clearly explained by keywords: symptomatic person for Type 1, personal hygiene for Type 2, social system for Type 3, and etiquette for Type 4. Type 1 perceived the messages about symptomatic persons as important to prevent the disease spread in the community whereas Type 2 tried to protect themselves from physical threats by developing proactive prevention through personal hygiene management prior to infection. Type 3 responded actively by relying on social systems, such as medical institutions or management organizations, while Type 4 positively responded to the messages related to etiquette that allowed them to avoid virus infection caused by contact with others.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13671-2.

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

Background

The emergence of COVID-19 as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, intervention measures including self-isolation, schools and border closures were implemented to varying degrees of success. Moreover, there are a limited number of empirical studies on the effectiveness of non-pharmaceutical interventions (NPIs) to control COVID-19. In this study, we considered two models to inform policy decisions about pandemic planning and the implementation of NPIs based on case-death-recovery counts.

Methods

We applied an extended susceptible-infected-removed (eSIR) model, incorporating quarantine, antibody and vaccination compartments, to time series data in order to assess the transmission dynamics of COVID-19. Additionally, we adopted the susceptible-exposed-infectious-recovered (SEIR) model to investigate the robustness of the eSIR model based on case-death-recovery counts and the reproductive number (R₀). The prediction accuracy was assessed using the root mean square error and mean absolute error. Moreover, parameter sensitivity analysis was performed by fixing initial parameters in the SEIR model and then estimating R₀, β and γ.

Results

We observed an exponential trend of the number of active cases of COVID-19 since March 02 2020, with the pandemic peak occurring around August 2021. The estimated mean R₀ values ranged from 1.32 (95% CI, 1.17–1.49) in Rwanda to 8.52 (95% CI: 3.73–14.10) in Kenya. The predicted case counts by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda were 115,505; 7,072,584; 18,248,566; 410,599; 386,020; 107,265, and 3,145,602 respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of the infected and mortality cases.

Conclusion

The current NPIs can delay the pandemic pea and effectively reduce further spread of COVID-19 and should therefore be strengthened. The observed reduction in R₀ is consistent with the interventions implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions on the economy and food systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07510-3.

COVID-19 infodemic on Facebook and containment measures in Italy, United Kingdom and New Zealand

The COVID-19 pandemic has been characterized by a social media “infodemic”: an overabundance of information whose authenticity may not always be guaranteed. With the potential to lead individuals to harmful decisions for the society, this infodemic represents a severe threat to information security, public health and democracy. In this paper, we assess the interplay between the infodemic and specific aspects of the pandemic, such as the number of cases, the strictness of containment measures, and the news media coverage. We perform a comparative study on three countries that employed different managements of the COVID-19 pandemic in 2020—namely Italy, the United Kingdom, and New Zealand. We first analyze the three countries from an epidemiological perspective to characterize the impact of the pandemic and the strictness of the restrictions adopted. Then, we collect a total of 6 million posts from Facebook to describe user news consumption behaviors with respect to the reliability of such posts. Finally, we quantify the relationship between the number of posts published in each of the three countries and the number of confirmed cases, the strictness of the restrictions adopted, and the online news media coverage about the pandemic. Our results show that posts referring to reliable sources are consistently predominant in the news circulation, and that users engage more with reliable posts rather than with posts referring to questionable sources. Furthermore, our modelling results suggest that factors related to the epidemiological and informational ecosystems can serve as proxies to assess the evolution of the infodemic.

Cognitive Factors Associated With Public Acceptance of COVID-19 Nonpharmaceutical Prevention Measures: Cross-sectional Study

Background

During the COVID-19 crisis, protests against restrictions emerged and rule violations increased, provoking peaks in new positive cases, forcing authorities in France to impose fines to slow down the spread of the disease. Due to these challenges, subsequent implementations of preventive measures in response to COVID-19 recurrences or other pandemics could present difficulties for decision makers. A better understanding of the factors underlying the public acceptance of COVID-19 nonpharmaceutical preventive measures may therefore contribute greatly to the design of more effective public communication during future pandemics.

Objective

The aim of this study was to evaluate the acceptance of COVID-19 nonpharmaceutical prevention measures in France. The specific objectives were (1) to examine the public’s acceptance of COVID-19 nonpharmaceutical prevention measures and (2) to assess the association of the public’s acceptance of these prevention measures and their perception of COVID-19.

Methods

Data were collected from 2004 individuals through an online survey conducted 6-8 weeks after the first lockdown in France. For objective 1, participants were asked the extent to which they supported 8 COVID-19 nonpharmaceutical preventive measures using a 4-point Likert scale. For objective 2, COVID-19–related perceptions were assessed using a 5-point Likert scale from an adapted version of Witte’s Extended Parallel Process Model. Sociodemographic and environmental variables were also collected. The public’s acceptance factors were estimated using an unweighted least squares factorial analysis, and their associations with perceptions of COVID-19, expressed as rate ratios (RR) and 95% CIs, were estimated using generalized linear Poisson regression models. Statistical analyses were performed using the SPSS statistical package.

Results

The acceptance rate reached 86.1% for individual protective measures, such as making masks mandatory in public open spaces, and 70.0% for collective restrictions, such as isolating the most vulnerable people (1604/2004, 80%) or forbidding public gatherings (n=1590, 79.3%). The least popular restrictions were closing all schools/universities and nonessential commerce such as bars and restaurants (n=1146, 57.2%). Acceptance of collective restrictions was positively associated with their perceived efficacy (RR 1.02, 95% CI 1.01-1.03), fear of COVID-19 (RR 1.04, 95% CI 1.03-1.05), and perceived severity of COVID-19 (RR 1.04, 95% CI 1.03-1.06), and negatively with age >60 years (RR 0.89, 95% CI 0.81-0.98). Acceptance of individual protective measures was associated with their perceived efficacy (RR 1.03, 95% CI 1.03-1.04), fear of COVID-19 (RR 1.02, 1.01-1.03), and perceived severity of COVID-19 (RR 1.03, 1.01-1.05).

Conclusions

Acceptance rates of COVID-19 nonpharmaceutical measures were rather high, but varied according to their perceived social cost, and were more related to collective than personal protection. Nonpharmaceutical measures that minimize social costs while controlling the spread of the disease are more likely to be accepted during pandemics.

Challenges and recommendations for COVID-19 public health messaging: a Canada-wide qualitative study using virtual focus groups

OBJECTIVES

To understand Canadian's attitudes and current behaviours towards COVID-19 public health measures (PHM), vaccination and current public health messaging, to provide recommendations for a public health intervention.

DESIGN

Ten focus groups were conducted with 2-7 participants/group in December 2020. Focus groups were transcribed verbatim and analysed using content and inductive thematic analysis. The capability opportunity motivation behaviour Model was used as our conceptual framework.

SETTING

Focus groups were conducted virtually across Canada.

PARTICIPANTS

Participants were recruited from a pool of individuals who previously completed a Canada-wide survey conducted by our research team.

MAIN OUTCOME MEASURE

Key barriers and facilitators towards COVID-19 PHM and vaccination, and recommendations for public health messaging.

RESULTS

Several themes were identified (1) participants' desire to protect family and friends was the main facilitator for adhering to PHM, while the main barrier was inconsistent PHM messaging and (2) participants were optimistic that the vaccine offers a return to normal, however, worries of vaccine efficacy and effectiveness were the main concerns. Participants felt that current public health messaging is inconsistent, lacks transparency and suggested that messaging should include scientific data presented by a trustworthy source.

CONCLUSIONS

We suggest six public health messaging recommendations to increase adherence to PHM and vaccination (1) use an unbiased scientist as a spokesperson, (2) openly address any unknowns, (3) more is better when sharing data, (4) use personalised stories to reinforce PHM and vaccinations, (5) humanise the message by calling out contradictions and (6) focus on the data and keep politics out.

5-Year Analysis of Twitter Usage Among Physicians: 2016-2020 (Preprint)

Background

Physicians are increasingly using Twitter as a channel for communicating with colleagues and the public. Identifying physicians on Twitter is difficult due to the varied and imprecise ways that people self-identify themselves on the social media platform. This is the first study to describe a reliable, repeatable methodology for identifying physicians on Twitter. By using this approach, we characterized the longitudinal activity of US physicians on Twitter.

Objective

We aimed to develop a reliable and repeatable methodology for identifying US physicians on Twitter and to characterize their activity on Twitter over 5 years by activity, tweeted topic, and account type.

Methods

In this study, 5 years of Twitter data (2016-2020) were mined for physician accounts. US physicians on Twitter were identified by using a custom-built algorithm to screen for physician identifiers in the Twitter handles, user profiles, and tweeted content. The number of tweets by physician accounts from the 5-year period were counted and analyzed. The top 100 hashtags were identified, categorized into topics, and analyzed.

Results

Approximately 1 trillion tweets were mined to identify 6,399,146 (<0.001%) tweets originating from 39,084 US physician accounts. Over the 5-year period, the number of US physicians tweeting more than doubled (ie, increased by 112%). Across all 5 years, the most popular themes were general health, medical education, and mental health, and in specific years, the number of tweets related to elections (2016 and 2020), Black Lives Matter (2020), and COVID-19 (2020) increased.

Conclusions

Twitter has become an increasingly popular social media platform for US physicians over the past 5 years, and their use of Twitter has evolved to cover a broad range of topics, including science, politics, social activism, and COVID-19. We have developed an accurate, repeatable methodology for identifying US physicians on Twitter and have characterized their activity.

Identity salience moderates the effect of social dominance orientation on COVID-19 'rule bending'

Amidst the economic, political, and social turmoil caused by the COVID-19 pandemic, contrasting responses to government mandated and recommended mitigation strategies have posed many challenges for governments as they seek to persuade individuals to adhere to prevention guidelines. Much research has subsequently examined the tendency of individuals to either follow (or not) such guidelines, and yet a 'grey area' also exists wherein many rules are subject to individual interpretation. In a large study of Canadians (N = 1032, Mage = 34.39, 52% female; collected April 6, 2020), we examine how social dominance orientation (SDO) as an individual difference predicts individual propensity to 'bend the rules' (i.e., engaging in behaviors that push the boundaries of adherence), finding that SDO is significantly and positively associated with greater intentions toward rule-bending behaviors. We further find that highlighting a self-oriented or in-group identity enhances the relationship between SDO and rule-bending, whereas making salient a superordinate-level identity (e.g., Canada) attenuates this effect. Implications for theory and practice are discussed.

Comparison of the Erectile Dysfunction Drugs Sildenafil and Tadalafil Using Patient Medication Reviews: Topic Modeling Study

BACKGROUND

Topic modeling of patient medication reviews of erectile dysfunction (ED) drugs can help identify patient preferences regarding ED treatment options. The identification of a set of topics important to the patient from social network service drug reviews would inform the design of patient-centered medication counseling.

OBJECTIVE

This study aimed to (1) identify the distinctive topics from patient medication reviews unique to tadalafil versus sildenafil; (2) determine if the primary topics are distributed differently for each drug and for each patient characteristic (age and time on ED drug therapy); and (3) test if the primary topics affect satisfaction with ED drug therapy controlling for patient characteristics.

METHODS

Data were collected from the patient medication reviews of sildenafil and tadalafil posted on WebMD and Ask a Patient. The latent Dirichlet allocation method of natural language processing was used to identify 5 distinctive topics from the patient medication reviews on each drug. Analysis of variance and a 2-sample t test were conducted to compare the topic distribution and assess whether patient satisfaction varies with the primary topics, age, and time on medication for each ED drug. Statistical significance was tested at an alpha of .05.

RESULTS

The patient medication reviews of sildenafil (N=463) had 2 topics on treatment benefit and 1 each on medication safety, marketing claim, and treatment comparison, while the patient medication reviews of tadalafil (N=919) had 2 topics on medication safety and 1 each on the remaining subjects. Sildenafil's reviewers quite frequently (94/463, 20.4%) mentioned erection sustainability as their primary topic, whereas tadalafil's reviewers were more concerned about severe medication safety. Those who mentioned erection sustainability as their primary topic were quite satisfied with their treatment as opposed to those who mentioned severe medication safety as their primary topic (score 3.85 vs 2.44). The discovered topics reflected the marketing claims of blue magic and amber romance for sildenafil and tadalafil, respectively. The topic of blue magic was preferred among younger patients, while the topic of amber romance was preferred among older patients. The topic alternative choices, which appeared for both the ED drugs, reflected patient interest in the comparative effectiveness and price outside the drug labeling information.

CONCLUSIONS

The patient medication reviews of ED drugs reflect patient preferences regarding drug labeling information, marketing claims, and alternative treatment choices. The patient preferences concerning ED treatment attributes inform the design of patient-centered communication for improved ED drug therapy.

Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space

Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.

Predictors of attitudes and adherence to COVID-19 public health guidelines in Western countries: a rapid review of the emerging literature

BACKGROUND

Physical distancing, wearing face masks and hand hygiene are evidence-based methods to protect the public from coronavirus disease 2019 (COVID-19) infection. There has been a proliferation of research examining characteristics that can be targeted by public health interventions. This rapid review sought to identify predictors of attitudes toward and adherence to COVID-19 public health guidelines, and identify interventions aiming to improve adherence.

METHODS

Articles were retrieved from multiple databases (e.g. MEDLINE, CINAHL and medRxiv) on 6 August 2020. Studies were limited to samples collected from Western countries. Studies were classified according to the types of factor (s) examined as independent variables. The consistency of evidence for each factor was scored by two reviewers.

RESULTS

In total, 1323 unique articles were identified in the initial search, resulting in 29 studies in the final synthesis. The available evidence suggests individuals who are older, identify as women, trust governments, perceive COVID-19 as threatening and access information through traditional news media are more likely to adhere with COVID-19 public health guidelines. Interventions for improving adherence have not yet been investigated thoroughly, and this review identified only three experimental studies.

CONCLUSIONS

This review has identified several characteristics that impact attitudes and adherence to COVID-19 public health guidelines.

Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study

BACKGROUND

COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines.

OBJECTIVE

The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization.

METHODS

We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets.

RESULTS

Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people's views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people's views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets.

CONCLUSIONS

This study suggests the public interest in and demand for information about vaccine development and people's views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter.

Characterization of non-adopters of COVID-19 non-pharmaceutical interventions through a national cross-sectional survey to assess attitudes and behaviours

Adoption of non-pharmaceutical interventions (NPIs) remains critical to curtail the spread of COVID-19. Using self-reported adherence to NPIs in Canada, assessed through a national cross-sectional survey of 4498 respondents, we aimed to identify and characterize non-adopters of NPIs, evaluating their attitudes and behaviours to understand barriers and facilitators of adoption. A cluster analysis was used to group adopters separately from non-adopters of NPIs. Associations with sociodemographic factors, attitudes towards COVID-19 and the public health response were assessed using logistic regression models comparing non-adopters to adopters. Of the 4498 respondents, 994 (22%) were clustered as non-adopters. Sociodemographic factors significantly associated with the non-adoption cluster were: (1) being male, (2) age 18–34 years, (3) Albertans, (4) lower education level and (5) higher conservative political leaning. Participants who expressed low concern for COVID-19 and distrust towards several institutions had greater odds of being non-adopters. This information characterizes individuals at greatest odds for non-adoption of NPIs to inform targeted marketing interventions.

"Salus Populi Suprema Lex": Considerations on the Initial Response of the United Kingdom to the SARS-CoV-2 Pandemic

In several countries worldwide, the initial response to coronavirus disease 2019 (COVID-19) has been heavily criticized by general public, media, and healthcare professionals, as well as being an acrimonious topic in the political debate. The present article elaborates on some aspects of the United Kingdom (UK) primary reaction to SARS-CoV-2 pandemic; specifically, from February to July 2020. The fact that the UK showed the highest mortality rate in Western Europe following the first wave of COVID-19 certainly has many contributing causes; each deserves an accurate analysis. We focused on three specific points that have been insofar not fully discussed in the UK and not very well known outside the British border: clinical governance, access to hospital care or intensive care unit, and implementation of non-pharmaceutical interventions. The considerations herein presented on these fundamental matters will likely contribute to a wider and positive discussion on public health, in the context of an unprecedented crisis.

Public health effects of travel-related policies on the COVID-19 pandemic: A mixed-methods systematic review

OBJECTIVES

To map travel policies implemented due to COVID-19 during 2020, and conduct a mixed-methods systematic review of health effects of such policies, and related contextual factors.

DESIGN

Policy mapping and systematic review. DATA SOURCES AND ELIGIBILITY CRITERIA: for the policy mapping, we searched websites of relevant government bodies and used data from the Oxford COVID-19 Government Response Tracker for a convenient sample of 31 countries across different regions. For the systematic review, we searched Medline (Ovid), PubMed, EMBASE, the Cochrane Central Register of Controlled Trials and COVID-19 specific databases. We included randomized controlled trial, non-randomized studies, modeling studies, and qualitative studies. Two independent reviewers selected studies, abstracted data and assessed risk of bias.

RESULTS

Most countries adopted a total border closure at the start of the pandemic. For the remainder of the year, partial border closure banning arrivals from some countries or regions was the most widely adopted measure, followed by mandatory quarantine and screening of travelers. The systematic search identified 69 eligible studies, including 50 modeling studies. Both observational and modeling evidence suggest that border closure may reduce the number of COVID-19 cases, disease spread across countries and between regions, and slow the progression of the outbreak. These effects are likely to be enhanced when implemented early, and when combined with measures reducing transmission rates in the community. Quarantine of travelers may decrease the number of COVID-19 cases but its effectiveness depends on compliance and enforcement and is more effective if followed by testing, especially when less than 14 day-quarantine is considered. Screening at departure and/or arrival is unlikely to detect a large proportion of cases or to delay an outbreak. Effectiveness of screening may be improved with increased sensitivity of screening tests, awareness of travelers, asymptomatic screening, and exit screening at country source. While four studies on contextual evidence found that the majority of the public is supportive of travel restrictions, they uncovered concerns about the unintended harms of those policies.

CONCLUSION

Most countries adopted full or partial border closure in response to COVID-19 in 2020. Evidence suggests positive effects on controlling the COVID-19 pandemic for border closure (particularly when implemented early), as well as quarantine of travelers (particularly with higher levels of compliance). While these positive effects are enhanced when implemented in combination with other public health measures, they are associated with concerns by the public regarding some unintended effects.

Public Attitudes and Factors of COVID-19 Testing Hesitancy in the United Kingdom and China: Comparative Infodemiology Study

Background

Massive community-wide testing has become the cornerstone of management strategies for the COVID-19 pandemic.

Objective

This study was a comparative analysis between the United Kingdom and China, which aimed to assess public attitudes and uptake regarding COVID-19 testing, with a focus on factors of COVID-19 testing hesitancy, including effectiveness, access, risk perception, and communication.

Methods

We collected and manually coded 3856 UK tweets and 9299 Chinese Sina Weibo posts mentioning COVID-19 testing from June 1 to July 15, 2020. Adapted from the World Health Organization's 3C Model of Vaccine Hesitancy, we employed social listening analysis examining key factors of COVID-19 testing hesitancy (confidence, complacency, convenience, and communication). Descriptive analysis, time trends, geographical mapping, and chi-squared tests were performed to assess the temporal, spatial, and sociodemographic characteristics that determine the difference in attitudes or uptake of COVID-19 tests.

Results

The UK tweets demonstrated a higher percentage of support toward COVID-19 testing than the posts from China. There were much wider reports of public uptake of COVID-19 tests in mainland China than in the United Kingdom; however, uncomfortable experiences and logistical barriers to testing were more expressed in China. The driving forces for undergoing COVID-19 testing were personal health needs, community-wide testing, and mandatory testing policies for travel, with major differences in the ranking order between the two countries. Rumors and information inquiries about COVID-19 testing were also identified.

Conclusions

Public attitudes and acceptance toward COVID-19 testing constantly evolve with local epidemic situations. Policies and information campaigns that emphasize the importance of timely testing and rapid communication responses to inquiries and rumors, and provide a supportive environment for accessing tests are key to tackling COVID-19 testing hesitancy and increasing uptake.

Social Determinants of Health Influencing the New Zealand COVID-19 Response and Recovery: A Scoping Review and Causal Loop Diagram

The Coronavirus pandemic of 2019–20 (COVID-19) affected multiple social determinants of health (SDH) across the globe, including in New Zealand, exacerbating health inequities. Understanding these system dynamics can support decision making for the pandemic response and recovery measures. This study combined a scoping review with a causal loop diagram to further understanding of the connections between SDH, pandemic measures, and both short- and long-term outcomes in New Zealand. The causal loop diagram showed the reinforcing nature of structural SDH, such as colonization and socio-economic influences, on health inequities. While balancing actions taken by government eliminated COVID-19, the diagram showed that existing structural SDH inequities could increase health inequities in the longer term, unless the opportunity is taken for socio-economic policies to be reset. Such policy resets would be difficult to implement, as they are at odds with the current socio-economic system. The causal loop diagram highlighted that SDH significantly influenced the dynamics of the COVID-19 impact and response, pointing to a need for purposeful systemic action to disrupt the reinforcing loops which increase health inequities over time. This will require strong systems leadership, and coordination between policy makers and implementation at local level.

Control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries

COVID-19 is the most severe pandemic globally since the 1918 influenza pandemic. Effectively responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and solve. This study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19, such as lockdowns, social distancing, wearing masks, hand hygiene, using Fangcang shelter hospitals, large-scale nucleic acid testing, close-contacts tracking, and pandemic information monitoring, as well as their prevention and control effects. We hope it can help improve the efficiency and effectiveness of pandemic prevention and control in future.

Predictors of COVID-19 Preventive Behavior Adoption Intention in Malaysia

Preventive behavior adoption is the key to reduce the possibility of getting COVID-19 infection. This paper aims to examine the determinants of intention to adopt preventive behavior by incorporating perception of e-government information and services and perception of social media into the theory of reasoned action. A cross-sectional online survey was carried out among Malaysian residents. Four hundred four valid responses were obtained and used for data analysis. A partial least-square-based path analysis revealed direct effects of attitude and subjective norm in predicting intention to adopt preventive behavior. In addition, perception of e-government information and services and perception of social media were found to be significant predictors of attitude toward preventive behavior. The findings highlight the importance of digital platforms in improving people’s attitudes toward preventive behavior and in turn contain the spread of the infectious disease.

"It's Like a Kick in the Teeth": The Emergence of Novel Predictors of Burnout in Frontline Workers During Covid-19

The context of Covid-19 has offered an unusual cultural landscape for examining how workers view their own position relative to others, and how individuals respond to prolonged exposure to workplace stress across different sectors and cultures. Through our recent work tracking the well-being of frontline workers in the UK and Ireland (the CV19 Heroes project), we have uncovered additional psychological factors that have not been accounted for in previous models of occupational stress or burnout. In recent months, frontline workers have worked to protect the community from the threat of SARS-CoV-2 and, simultaneously, have evaluated their perceptions of collective efforts of others as either congruent or incongruent with collective goals (e.g., lowered mortality and morbidity): we call this novel aspect solidarity appraisal. These frontline workers have been hailed as heroes, which we argue has led to the creation of an implicit psychological contract (the hero contract) between frontline workers and the public. Here, the heroes are willing to "go above and beyond" for the greater good, with the expectation that we (the public) do our part by adhering to public health guidelines. Where frontline workers perceive incongruence between the words and actions of others in working toward collective goals this drives negative affect and subsequent burnout. In this perspective article, we evaluate the cultural context of the pandemic in the UK and Ireland and suggest important socio-cultural factors that contribute to perceptions of solidarity, and how this may relate to burnout and worker welfare during and beyond the pandemic context.

Political partisanship and stigma against people who use drugs in opinions about allocating COVID-19 prevention resources to vulnerable populations

Background

The distribution of resources during the COVID-19 pandemic has been politicized and contentious in the United States. Vulnerable populations, such as those living in poverty, experiencing homelessness, or who use drugs, are particularly susceptible to becoming infected with COVID-19 and often have limited access to protective supplies, such as masks and hand sanitizer. Our aim was to understand public opinion on increasing the allocation of COVID-19 prevention resources to vulnerable populations.

Methods

Data were from an online survey of 680 United States adults. Participants’ opinions on the allocation of COVID-19 prevention resources to people with low income, experiencing homelessness, or who use drugs were assessed using a five-item Likert scale. We examined the prevalence of these opinions and their relationship to sociodemographic characteristics, COVID-19 beliefs, and drug-related experiences.

Results

Most participants supported increasing resources for individuals with low incomes (79.6%) and experiencing homelessness (74.6%), while a minority supported increasing resources for people who use drugs (33.5%). Politically conservative participants were less likely to support increasing resources for all three populations than those who were politically liberal. Skepticism about the severity of COVID-19 was also associated with less support for increasing resources across groups.

Discussion

Our results demonstrate that people who use drugs continue to be stigmatized in the context of the COVID-19 pandemic, resulting in popular opinion not supporting people who use drugs with potentially lifesaving resources. Overcoming this stigma is essential to prevent COVID-19 among people who use drugs, a population which experiences elevated risk of COVID-19 infection.

Social Media Use for Health Purposes: Systematic Review

Background

Social media has been widely used for health-related purposes, especially during the COVID-19 pandemic. Previous reviews have summarized social media uses for a specific health purpose such as health interventions, health campaigns, medical education, and disease outbreak surveillance. The most recent comprehensive review of social media uses for health purposes, however, was conducted in 2013. A systematic review that covers various health purposes is needed to reveal the new usages and research gaps that emerge in recent years.

Objective

This study aimed to provide a systematic review of social media uses for health purposes that have been identified in previous studies.

Methods

The researchers searched for peer-reviewed journal articles published between 2006 and 2020 in 12 databases covering medicine, public health, and social science. After coding the articles in terms of publication year, journal area, country, method, social media platform, and social media use for health purposes, the researchers provided a review of social media use for health purposes identified in these articles.

Results

This study summarized 10 social media uses for various health purposes by health institutions, health researchers and practitioners, and the public.

Conclusions

Social media can be used for various health purposes. Several new usages have emerged since 2013 including advancing health research and practice, social mobilization, and facilitating offline health-related services and events. Research gaps exist regarding advancing strategic use of social media based on audience segmentation, evaluating the impact of social media in health interventions, understanding the impact of health identity development, and addressing privacy concerns.

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients' feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic

BACKGROUND

The Covid-19 pandemic is characterized by uncertainty and constant change, forcing governments and health authorities to ramp up risk communication efforts. Consequently, visuality and social media platforms like Twitter have come to play a vital role in disseminating prevention messages widely. Yet to date, only little is known about what characterizes visual risk communication during the Covid-19 pandemic. To address this gap in the literature, this study's objective was to determine how visual risk communication was used on Twitter to promote the World Health Organisations (WHO) recommended preventative behaviours and how this communication changed over time.

METHODS

We sourced Twitter's 500 most retweeted Covid-19 messages for each month from January-October 2020 using Crowdbreaks. For inclusion, tweets had to have visuals, be in English, come from verified accounts, and contain one of the keywords 'covid19', 'coronavirus', 'corona', or 'covid'. Following a retrospective approach, we then performed a qualitative content analysis of the 616 tweets meeting inclusion criteria.

RESULTS

Our results show communication dynamics changed over the course of the pandemic. At the start, most retweeted preventative messages came from the media and health and government institutions, but overall, personal accounts with many followers (51.3%) predominated, and their tweets had the highest spread (10.0%, i.e., retweet count divided by followers). Messages used mostly photographs and images were found to be rich with information. 78.1% of Tweets contained 1-2 preventative messages, whereby 'stay home' and 'wear a mask' frequented most. Although more tweets used health loss framing, health gain messages spread more.

CONCLUSION

Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more tweets used the emotive medium of photographs often combined with health loss framing raises concerns about persuasive tactics. More research is needed to understand the implications of framing and its impact on public perceptions and behaviours.

A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States

INTRODUCTION

Early reports of COVID-19 cases and deaths may not accurately convey community-level concern about the pandemic during early stages, particularly in the United States where testing capacity was initially limited. Social media interaction may elucidate public reaction and communication dynamics about COVID-19 in this critical period, during which communities may have formulated initial conceptions about the perceived severity of the pandemic.

METHODS

Tweets were collected from the Twitter public API stream filtered for keywords related to COVID-19. Using a pre-existing training set, a support vector machine (SVM) classifier was used to obtain a larger set of geocoded tweets with characteristics of user self-reporting COVID-19 symptoms, concerns, and experiences. We then assessed the longitudinal relationship between identified tweets and the number of officially reported COVID-19 cases using linear and exponential regression at the U.S. county level. Changes in tweets that included geospatial clustering were also assessed for the top five most populous U.S. cities.

RESULTS

From an initial dataset of 60 million tweets, we analyzed 459,937 tweets that contained COVID-19-related keywords that were also geolocated to U.S. counties. We observed an increasing number of tweets throughout the study period, although there was variation between city centers and residential areas. Tweets identified as COVID-19 symptoms or concerns appeared to be more predictive of active COVID-19 cases as temporal distance increased.

CONCLUSION

Results from this study suggest that social media communication dynamics during the early stages of a global pandemic may exhibit a number of geospatial-specific variations among different communities and that targeted pandemic communication is warranted. User engagement on COVID-19 topics may also be predictive of future confirmed case counts, though further studies to validate these findings are needed.

Attitudes, behaviours and barriers to public health measures for COVID-19: a survey to inform public health messaging

Background

Public support of public health measures including physical distancing, masking, staying home while sick, avoiding crowded indoor spaces and contact tracing/exposure notification applications remains critical for reducing spread of COVID-19. The aim of our work was to understand current behaviours and attitudes towards public health measures as well as barriers individuals face in following public health measures. We also sought to identify attitudes persons have regarding a COVID-19 vaccine and reasons why they may not accept a vaccine.

Methods

A cross-sectional online survey was conducted in August 2020, in Alberta, Canada in persons 18 years and older. This survey evaluated current behaviours, barriers and attitudes towards public health measures and a COVID-19 vaccine. Cluster analysis was used to identify key patterns that summarize data variations among observations.

Results

Of the 60 total respondents, the majority of persons were always or often physically distancing (73%), masking (65%) and staying home while sick (67%). Bars/pubs/lounges or nightclubs were visited rarely or never by 63% of respondents. Persons identified staying home while sick to provide the highest benefit (83%) in reducing spread of COVID-19. There were a large proportion of persons who had not downloaded or used a contact tracing/exposure notification app (77%) and who would not receive a COVID-19 vaccine when available (20%) or were unsure (12%). Reporting health authorities as most trusted sources of health information was associated with greater percentage of potential uptake of vaccine but not related to contact tracing app download and use. Individuals with lower concern of getting and spreading COVID-19 showed the least uptake of public health measures except for avoiding public places such as bars. Lower concern regarding COVID-19 was also associated with more negative responses to taking a potential COVID-19 vaccine.

Conclusion

These results suggest informational frames and themes focusing on individual risks, highlighting concern for COVID-19 and targeting improving trust for health authorities may be most effective in increasing public health measures. With the ultimate goal of preventing spread of COVID-19, understanding persons’ attitudes towards both public health measures and a COVID-19 vaccine remains critical to addressing barriers and implementing targeted interventions and messaging to improve uptake.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10790-0.

Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities

As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users' minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.

Comparison of Public Responses to Containment Measures During the Initial Outbreak and Resurgence of COVID-19 in China: Infodemiology Study

BACKGROUND

COVID-19 cases resurged worldwide in the second half of 2020. Not much is known about the changes in public responses to containment measures from the initial outbreak to resurgence. Monitoring public responses is crucial to inform policy measures to prepare for COVID-19 resurgence.

OBJECTIVE

This study aimed to assess and compare public responses to containment measures during the initial outbreak and resurgence of COVID-19 in China.

METHODS

We curated all COVID-19-related posts from Sina Weibo (China's version of Twitter) during the initial outbreak and resurgence of COVID-19 in Beijing, China. With a Python script, we constructed subsets of Weibo posts focusing on 3 containment measures: lockdown, the test-trace-isolate strategy, and suspension of gatherings. The Baidu open-source sentiment analysis model and latent Dirichlet allocation topic modeling, a widely used machine learning algorithm, were used to assess public engagement, sentiments, and frequently discussed topics on each containment measure.

RESULTS

A total of 8,985,221 Weibo posts were curated. In China, the containment measures evolved from a complete lockdown for the general population during the initial outbreak to a more targeted response strategy for high-risk populations during COVID-19 resurgence. Between the initial outbreak and resurgence, the average daily proportion of Weibo posts with negative sentiments decreased from 57% to 47% for the lockdown, 56% to 51% for the test-trace-isolate strategy, and 55% to 48% for the suspension of gatherings. Among the top 3 frequently discussed topics on lockdown measures, discussions on containment measures accounted for approximately 32% in both periods, but those on the second-most frequently discussed topic shifted from the expression of negative emotions (11%) to its impacts on daily life or work (26%). The public expressed a high level of panic (21%) during the initial outbreak but almost no panic (1%) during resurgence. The more targeted test-trace-isolate measure received the most support (60%) among all 3 containment measures in the initial outbreak, and its support rate approached 90% during resurgence.

CONCLUSIONS

Compared to the initial outbreak, the public expressed less engagement and less negative sentiments on containment measures and were more supportive toward containment measures during resurgence. Targeted test-trace-isolate strategies were more acceptable to the public. Our results indicate that when COVID-19 resurges, more targeted test-trace-isolate strategies for high-risk populations should be promoted to balance pandemic control and its impact on daily life and the economy.

Will COVID-19-related economic worries superimpose health worries, reducing nonpharmaceutical intervention acceptance in Germany? A prospective pre-registered study

Nonpharmaceutical interventions (NPI) such as stay‐at‐home orders aim at curbing the spread of the novel coronavirus, SARS‐COV‐2. In March 2020, a large proportion of the German population supported such interventions. In this article, we analyse whether the support for NPI dwindle with economic worries superimposing virus‐related worries in the months to follow. We test seven pre‐registered¹ hypotheses using data from the German COSMO survey (Betsch, Wieler, Habersaat, et al. 2020), which regularly monitors behavioural and psychological factors related to the pandemic. The present article covers the period from March 24, 2020 to July 7, 2020 (N_total = 13,094), and, in addition, includes a validation study providing evidence for the reliability and validity of the corresponding COSMO measures (N = 612). Results revealed that virus‐related worries decreased over time, whereas economic worries remained largely constant. Moreover, the acceptance of NPIs considerably decreased over time. Virus‐related worries were positively associated with acceptance of NPIs, whereas this relationship was negative regarding economic worries (albeit smaller and less consistent). Unexpectedly, no interactions between virus‐related worries and economic worries were found. We conclude that individual differences in virus‐related and economic threat perceptions related to COVID‐19 play an important role in the acceptance of NPIs.

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

Knowledge, Perceived Beliefs, and Preventive Behaviors Related to COVID-19 Among Chinese Older Adults: Cross-Sectional Web-Based Survey

BACKGROUND

The COVID-19 pandemic continues to pose an international public health threat. Prevention is of paramount importance to protect the high-risk group of older adults until specific treatments for COVID-19 become available; however, little work has been done to explore factors that promote preventive behaviors among this population.

OBJECTIVE

This study aims to investigate the knowledge, perceived beliefs, and preventive behaviors towards COVID-19 of older adults in China and determine the factors that influence their practice of preventive behaviors.

METHODS

From February 19 to March 19, 2020, a cross-sectional, web-based survey was administered to Chinese older adults in all 31 provinces of mainland China using a convenience sampling method to assess the respondents' knowledge, perceived beliefs, and preventive behaviors towards COVID-19. Standard descriptive statistics and hierarchical linear regression analyses were conducted to analyze the data.

RESULTS

A total of 1501 participants responded to the survey, and 1263 valid responses (84.1%) were obtained for further analysis. The overall correct rate on the knowledge questionnaire was 87%, overall positive beliefs regarding COVID-19 were found, and the mean behavior score was 13.73/15 (SD 1.62, range 5-15). The hierarchical linear regression showed that respondents who were married or cohabitating and who lived in areas with community-level control measures were more likely to practice preventive behaviors (P<.01). Knowledge (β=0.198, P<.001), perceived susceptibility (β=0.263, P=.03), perceived benefits (β=0.643, P<.001), and self-efficacy in preventing COVID-19 (β=0.468, P<.001) were also found to be significantly associated with preventive behaviors.

CONCLUSIONS

Most older residents had adequate knowledge and positive beliefs regarding COVID-19 and engaged in proactive behaviors to prevent the disease. Knowledge and beliefs were confirmed to be significantly associated with behavior responses. Our findings have significant implications in enhancing the effectiveness of COVID-19 prevention programs targeting the older population; these programs must be continued and strengthened as the epidemic continues.