The Impact of Comorbidities among Ethnic Minorities on COVID-19 Severity and Mortality in Canada and the USA: A Scoping Review.

(1) Current literature on ethnic minorities, comorbidities, and COVID-19 tends to investigate these factors separately, leaving gaps in our understanding about their interactions. Our review seeks to identify a relationship between ethnicity, comorbidities, and severe COVID-19 outcomes (ICU admission and mortality). We hope to enhance our understanding of the various factors that exacerbate COVID-19 severity and mortality in ethnic minorities in Canada and the USA. (2) All articles were received from PubMed, Scopus, CINAHL, and Ovid EMBASE from November 2020 to June 2022. Included articles contain information regarding comorbidities among ethnic minorities in relation to COVID-19 severity and mortality. (3) A total of 59 articles were included that examined various ethnic groups, including Black/African American, Asian, Hispanic, White/Caucasian, and Indigenous people. We found that the most examined comorbidities were diabetes, hypertension, obesity, and chronic kidney disease. A total of 76.9% of the articles (40 out of 52) found a significant association between different races and COVID-19 mortality, whereas 21.2% of the articles (11 out of 52) did not. (4) COVID-19 ICU admissions and mortality affect various ethnic groups differently, with Black patients generally having the most adverse outcomes. These outcomes may also interact with sex and age, though more research is needed assessing these variables together with ethnicity.

Factors Associated With Worsened Mental Health of Health Care Workers in Canada During the COVID-19 Pandemic: Cross-Sectional Survey Study.

BACKGROUND: Health care workers (HCWs) in Canada have endured difficult conditions during the COVID-19 pandemic. Many worked long hours while attending to patients in a contagious environment. This introduced an additional burden that may have contributed to worsened mental health conditions. OBJECTIVE: In this study, we examine the factors associated with worsened mental health conditions of HCWs as compared to before the start of the pandemic. METHODS: We use data from a survey of HCWs by Statistics Canada. A regression model is used to estimate the odds ratios (ORs) of worsened mental health after the start of the pandemic. The estimated odds ratio (OR) is associated with different independent variables that include demographics (age, sex, immigration status, and geographic area), occupational factors (work status, occupational group, and exposure category), and different access levels to personal protective equipment (PPE). RESULTS: Of 18,139 eligible participants surveyed, 13,990 (77.1%) provided valid responses. We found that HCWs younger than 35 years old were more likely (OR 1.14, 95% CI 1.03-1.27; P=.01) to exhibit worsened mental health as compared to the reference group (35-44 years old). As for sex, male HCWs were less likely (OR 0.76, 95% CI 0.67-0.86; P<.001) to exhibit worsened mental health as compared to female HCWs. Immigrant HCWs were also less likely (OR 0.57, 95% CI 0.51-0.64; P<.001) to exhibit worsened mental health as compared to nonimmigrant HCWs. Further, HCWs working in Alberta had the highest likelihood of exhibiting worsened mental health as compared to HCWs working elsewhere (Atlantic provinces, Quebec, Manitoba, Saskatchewan, Ontario, British Columbia, and Northern Territories). Frontline workers were more likely (OR 1.26, 95% CI 1.16-1.38; P<.001) to exhibit worsened mental health than nonfrontline HCWs. Part-time HCWs were less likely (OR 0.85, 95% CI 0.76-0.93; P<.001) to exhibit worsened mental health than full-time HCWs. HCWs who reported encountering COVID-19 cases were more likely (OR 1.55, 95% CI 1.41-1.70; P<.001) to exhibit worsened mental health as compared to HCWs who reported no contact with the disease. As for PPE, HCWs who never had access to respirators, eye protection, and face shields are more likely to exhibit worsened mental health by 1.31 (95% CI 1.07-1.62; P<.001), 1.51 (95% CI 1.17-1.96; P<.001), and 1.41 (95% CI 1.05-1.92; P=.02) than those who always had access to the same PPE, respectively. CONCLUSIONS: Different HCW groups experienced the pandemic differently based on their demographic and occupational backgrounds as well as access to PPE. Such findings are important to stakeholders involved in the planning of personalized support programs and aid mental health mitigation in future crises. Certain groups require more attention.

Impact of hepatitis B birth dose on immune response in Pakistani children: an open-label, non-inferiority randomized controlled trial, implications for achieving SDG target.

BACKGROUND: Despite presence of hyperendemic areas, the national immunisation schedule in Pakistan does not include a hepatitis B birth dose, placing newborns at an additional risk of acquiring hepatitis B. This study aimed to assess the impact of adding hepatitis B birth dose in existing national vaccination schedule. METHODS: An open label, randomised controlled non-inferiority trial enrolled 296 healthy near-term mothers to intervention and control groups. Newborns in the intervention group received a hepatitis B birth dose along with routine immunisation vaccines, while control group newborns received vaccinations under the national schedule. Seroprotection was measured and compared at birth and 8 weeks after administering the third dose of pentavalent vaccine. The risk ratio of seroprotection was computed and compared with the delta value set at 5%. RESULTS: The study found that 95.8% of infants in the intervention group achieved seroprotection, which was significantly higher than the control group's 58.7%. The difference in risk ratio of seroprotection was 1.62 (CI95: 1.37-1.93), with the upper limit of the CI below the delta margin, confirming non-inferiority. The time interval between birth and the first hepatitis B immunisation shot was a predictor of seroprotection, with an odds ratio of 1.79 (CI95: 1.01-2.9). CONCLUSION: Our study indicates that adding a hepatitis B birth dose to the immunisation schedule in Pakistan is non-inferior to the existing one. This can also contribute towards Pakistan's achievement of the SDG target of reducing hepatitis B surface antigen seroprevalence in children under 5 years of age. TRIAL REGISTRATION NUMBER: NCT04870021.

Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study.

BACKGROUND: Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis-driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests. OBJECTIVE: This study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time. METHODS: A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software. RESULTS: We identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward. CONCLUSIONS: Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population.

Tweeting for Health Using Real-time Mining and Artificial Intelligence-Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter.

BACKGROUND: Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. OBJECTIVE: This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics. METHODS: U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis. RESULTS: U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave-related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately. CONCLUSIONS: The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics.

Applying the syndemic framework to cancer research for effective cancer control in low- and middle-income countries.

Cancer burden is increasing rapidly globally, especially in low- and middle-income countries (LMICs), which already face a double burden of infectious diseases and other non-communicable diseases (NCDs). LMICs also struggle with poor social determinants of health, leading to cancer health disparities, such as delayed diagnoses and increased death rates due to cancer. Contextually, relevant research needs to be prioritised in these regions to ensure feasible, evidence-based healthcare planning and delivery for cancer prevention and control. A syndemic framework has been used to study the disease clustering of infectious diseases and NCDs across varied social contexts to understand how diseases interact adversely and how the wider environmental context and other socioeconomic factors contribute to poor health outcomes within specific populations. We propose using this model to study the 'syndemic of cancers' in the disadvantaged population of LMICs and suggest ways for the clear operationalisation of the syndemic framework through multidisciplinary evidence-generation models for the delivery of integrated, socially conscious interventions for effective cancer control.

Understanding the COVID-19 Vaccine Policy Terrain in Ontario Canada: A Policy Analysis of the Actors, Content, Processes, and Context.

(1) Background: Canada had a unique approach to COVID-19 vaccine policy making. The objective of this study was to understand the evolution of COVID-19 vaccination policies in Ontario, Canada, using the policy triangle framework. (2) Methods: We searched government websites and social media to identify COVID-19 vaccination policies in Ontario, Canada, which were posted between 1 October 2020, and 1 December 2021. We used the policy triangle framework to explore the policy actors, content, processes, and context. (3) Results: We reviewed 117 Canadian COVID-19 vaccine policy documents. Our review found that federal actors provided guidance, provincial actors made actionable policy, and community actors adapted policy to local contexts. The policy processes aimed to approve and distribute vaccines while continuously updating policies. The policy content focused on group prioritization and vaccine scarcity issues such as the delayed second dose and the mixed vaccine schedules. Finally, the policies were made in the context of changing vaccine science, global and national vaccine scarcity, and a growing awareness of the inequitable impacts of pandemics on specific communities. (4) Conclusions: We found that the triad of vaccine scarcity, evolving efficacy and safety data, and social inequities all contributed to the creation of vaccine policies that were difficult to efficiently communicate to the public. A lesson learned is that the need for dynamic policies must be balanced with the complexity of effective communication and on-the-ground delivery of care.

Ethical principles for infodemiology and infoveillance studies concerning infodemic management on social media.

Big data originating from user interactions on social media play an essential role in infodemiology and infoveillance outcomes, supporting the planning and implementation of public health actions. Notably, the extrapolation of these data requires an awareness of different ethical elements. Previous studies have investigated and discussed the adoption of conventional ethical approaches in the contemporary public health digital surveillance space. However, there is a lack of specific ethical guidelines to orient infodemiology and infoveillance studies concerning infodemic on social media, making it challenging to design digital strategies to combat this phenomenon. Hence, it is necessary to explore if traditional ethical pillars can support digital purposes or whether new ones must be proposed since we are confronted with a complex online misinformation scenario. Therefore, this perspective provides an overview of the current scenario of ethics-related issues of infodemiology and infoveillance on social media for infodemic studies.

Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada.

Modelling the spatiotemporal spread of a highly transmissible disease is challenging. We developed a novel spatiotemporal spread model, and the neighbourhood-level data of COVID-19 in Toronto was fitted into the model to visualize the spread of the disease in the study area within two weeks of the onset of first outbreaks from index neighbourhood to its first-order neighbourhoods (called dispersed neighbourhoods). We also model the data to classify hotspots based on the overall incidence rate and persistence of the cases during the study period. The spatiotemporal spread model shows that the disease spread to 1-4 neighbourhoods bordering the index neighbourhood within two weeks. Some dispersed neighbourhoods became index neighbourhoods and further spread the disease to their nearby neighbourhoods. Most of the sources of infection in the dispersed neighbourhood were households and communities (49%), and after excluding the healthcare institutions (40%), it becomes 82%, suggesting the expansion of transmission was from close contacts. The classification of hotspots informs high-priority areas concentrated in the northwestern and northeastern parts of Toronto. The spatiotemporal spread model along with the hotspot classification approach, could be useful for a deeper understanding of spatiotemporal dynamics of infectious diseases and planning for an effective mitigation strategy where local-level spatially enabled data are available.

COVID-19, Mental Health, and Chronic Illnesses: A Syndemic Perspective.

BACKGROUND: The COVID-19 pandemic is an epidemiological and psychological crisis; what it does to the body is quite well known by now, and more research is underway, but the syndemic impact of COVID-19 and mental health on underlying chronic illnesses among the general population is not completely understood. METHODS: We carried out a literature review to identify the potential impact of COVID-19 and related mental health issues on underlying comorbidities that could affect the overall health of the population. RESULTS: Many available studies have highlighted the impact of COVID-19 on mental health only, but how complex their interaction is in patients with comorbidities and COVID-19, the absolute risks, and how they connect with the interrelated risks in the general population, remain unknown. The COVID-19 pandemic can be recognized as a syndemic due to; synergistic interactions among different diseases and other health conditions, increasing overall illness burden, emergence, spread, and interactions between infectious zoonotic diseases leading to new infectious zoonotic diseases; this is together with social and health interactions leading to increased risks in vulnerable populations and exacerbating clustering of multiple diseases. CONCLUSION: There is a need to develop evidence to support appropriate and effective interventions for the overall improvement of health and psychosocial wellbeing of at-risk populations during this pandemic. The syndemic framework is an important framework that can be used to investigate and examine the potential benefits and impact of codesigning COVID-19/non-communicable diseases (NCDs)/mental health programming services which can tackle these epidemics concurrently.

Proposing a New Conceptual Syndemic Framework for COVID-19 Vaccine Hesitancy: A Narrative Review.

BACKGROUND: Discussions regarding syndemics have dominated research in recent years. Vaccine hesitancy has also been propelled to the forefront. In this narrative review, we aim to frame a novel syndemic framework to understand the interaction between vaccine hesitancy, COVID-19, and negative health outcomes. METHODS: A non-systematic electronic search was conducted in PubMed and Google Scholar. Search criteria were limited to articles published between November 2019 and June 2022. Articles related to the COVID-19 syndemic and vaccine hesitancy were included. RESULTS: Our review revealed that the adherence to COVID-19 regulations-although they were effective in preventing COVID-19 transmission, cases, and deaths-created a dynamically unstable 'vicious cycle' between undesirable health, economic, and social outcomes. The "accumulation" of complex stressors decreased individuals' cognitive flexibility and hindered them from making decisions and getting vaccinated. Furthermore, it increased individuals' risk of acquiring COVID-19, losing their employment, increasing poverty, and decreasing healthcare utilization. We illustrated how the amalgamation of sociodemographic and contextual factors associated with COVID-19 might impact people's vaccine decisions, making them more hesitant toward COVID-19 vaccination. Failing to receive vaccinations increases the chances of COVID-19 transmission, hospitalization, and other negative health outcomes. CONCLUSIONS: Understanding the interaction between these factors is essential to provide policymakers with inspiration to set appropriate interventions for promoting COVID-19 vaccination acceptance to decrease the overall burden of pandemics.

Public Attitudes Towards Vaccine Passports in Alberta During the "Pandemic of the Unvaccinated": A Qualitative Analysis of Reddit Posts.

Objective: The goal of this study is to understand the attitudes and beliefs towards mandatory vaccination policies in Alberta, Canada in September 2021, during the fourth wave of COVID-19. Methods: 9400 posts between 1st September and 30th September 2021 were collected from the subreddit r/Alberta with Pushshift.io. Posts and comments were manually screened to determine their relevance to research objectives, and then coded using inductive coding and iterative qualitative analysis methods. Results: Inductive coding methods yielded five key themes: 1) opinions related to autonomy and consent, 2) concerns about COVID-19 vaccine passport enforcement, 3) concerns about government, 4) concerns about the logistics of passports, and 5) opinions relating to the necessity of passports to prevent lockdowns. Conclusion: Overall, the data presented favorable opinions towards an Albertan vaccine passport within r/Alberta. Anti-vaccine and anti-mandate opinions were often less extreme than those present in the literature, although this may be due to r/Alberta subreddit moderators removing those more extreme comments. Most reservations were due to issues of bodily autonomy, though concerns about the government and logistics also played a meaningful role.

Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults.

Background: As global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives. This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults. Methods: The study was conducted in a residential environment equipped with various contactless smart home sensors. We recruited 40 participants, each of whom was instructed to perform 23 types of predefined daily living activities across five phases. Concurrently, heart rate data were collected through Empatica E4 wristband as the benchmark. Analysis of data involved five prominent machine learning models: Support Vector Regression, K-nearest neighbor, Random Forest, Decision Tree, and Multilayer Perceptron. Results: All machine learning models achieved commendable prediction performance, with an average Mean Absolute Error of 7.329. Particularly, Random Forest model outperformed the other models, achieving a Mean Absolute Error of 6.023 and a Scatter Index value of 9.72%. The Random Forest model also showed robust capabilities in capturing the relationship between individuals' daily living activities and their corresponding heart rate responses, with the highest R2 value of 0.782 observed during morning exercise activities. Environmental factors contribute the most to model prediction performance. Conclusions: The utilization of the proposed non-intrusive approach enabled an innovative method to observe heart rate fluctuations during different activities. The findings of this research have significant implications for public health. By predicting heart rate based on contactless smart home technologies for individuals' daily living activities, healthcare providers and public health agencies can gain a comprehensive understanding of an individual's cardiovascular health profile. This valuable information can inform the implementation of personalized interventions, preventive measures, and lifestyle modifications to mitigate the risk of cardiovascular diseases and improve overall health outcomes.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada.

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Monkeypox: A Mini-Review on the Globally Emerging Orthopoxvirus.

Monkeypox is a zoonotic infectious disease belonging to the orthopoxvirus family that has predominantly occurred in West and Central Africa since it was initially discovered in 1958. In May 2022, a global outbreak of monkeypox began to occur on an international scale, with case numbers still rising as this review is being written. This mini review sought to analyze the existing literature on monkeypox published from 2017 onward to provide epidemiological context to current outbreaks. PubMed and Google Scholar databases were used to gather both peer-reviewed and grey literature on the routes of transmission, case definitions, clinical characteristics, diagnosis, management, prevention, vaccination, and epidemiology of monkeypox. Epidemiological studies indicate that the age of onset of monkeypox has increased over time. Antivirals, such as Tecovirimat and Brincidofovir, are recommended to manage confirmed cases of monkeypox. Although mass vaccination is not currently recommended, the smallpox vaccine can be used as a preventative measure for at-risk groups, such as men who have sex with men and frontline healthcare workers. Further peer-reviewed research addressing animal reservoirs and sexual transmission dynamics is needed.

Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada.

Objectives: This study aimed to investigate public discourses and sentiments regarding the Freedom Convoy in Canada on Twitter. Methods: English tweets were retrieved from Twitter API from 15 January to 14 February 2022 when the Freedom Convoy occurred. Unsupervised topic modelling and sentiment analysis were applied to identify topics and sentiments for each topic. Results: Five topics resulted from the topic modelling, including convoy support, political arguments toward the current prime minister, lifting vaccine mandates, police activities, and convoy fundraising. Overall, sentiments for each topic began with more positive or negative sentiments but approached to neutral over time. Conclusion: The results show that sentiments towards the Freedom Convoy generally tended to be positive. Five topics were identified from the data collected, and these topics highly correlated with the events of the convoy. Our study also demonstrated that a mixed approach of unsupervised machine learning techniques and manual validation could generate timely evidence.

Cancer research in Pakistan: Opportunities, challenges and the way forward.

Chronic disease disparities, in low and middle income countries (LMICs), need to be addressed through research to improve health equity. Most LMICs including Pakistan have a huge burden of non communicable disease (NCDs) like cancer and a high number of deaths due to cancer. National Cancer Control Programmes are recommended for countries with high cancer burden like Pakistan to ensure systematic implementation of evidence based strategies for cancer control however there is limited capacity for evidence generation through research. We describe the existing status for cancer registries and research in Pakistan and suggest a syndemic framework to prioritize contextually relevant research which can advance cancer health equity for Pakistani population.

Knowledge, Attitudes, and Practices concerning Black Fungus during COVID-19 Pandemic among Students of Bangladesh: An Online-Based Cross-Sectional Survey.

BACKGROUND: Infection with COVID-19 and its control entailing steroids and immunomodulatory medications disrupted normal immune function. The ensuing immunological disorder led to the rise of another infection-Black Fungus (Mucormycosis). However, the spread of Black Fungus can be minimized through proper knowledge, informed attitude, and conscious preventive practice. This study aimed to assess students' knowledge, attitude, and practice (KAP) regarding Black Fungus amid the COVID-19 pandemic in Bangladesh. METHODS: This cross-sectional study was carried out among Bangladeshi students from June to July 2021. Using Google Forms, an e-questionnaire was developed for this web-based survey, and the participants were selected through a snowball sampling approach. RESULTS: Out of the 2009 participants, more than half were female (53.5%), and the majority were at an age between 18 and 25 years (31.5%) and had received higher secondary (HSC) schooling (77.8%), while around 61% resided in urban areas. Findings revealed that most of the students (63.8%) spent around 2 h on electronic and social media to become informed about COVID-19 and Black Fungus. Approximately 33% of the students showed low KAP scores (32.9%), whereas around 26% had high KAP scores. Our results show a significant association between KAP and sex, schooling, living status, residence, and media exposure. CONCLUSION: The knowledge of Black Fungus considerably varies among Bangladeshi students considering the place of residence, age, sex, living arrangement, and media exposure. Policymakers should emphasize awareness among people focusing on the results of this study to increase deterrent attitudes and protective practices to minimize the risks of being infected.