Effects of home confinement on physical activity, nutrition, and sleep quality during the COVID-19 outbreak in amateur and elite athletes.

Introduction: Despite the progress in the management of the pandemic caused by COVID-19, it is necessary to continue exploring and explaining how this situation affected the athlete population around the world to improve their circumstances and reduce the negative impact of changes in their lifestyle conditions that were necessitated due to the pandemic. The aim of this study was to analyze the moderating influence of physical activity (PA) and dietary habits on the impact of the COVID-19 pandemic experience on sleep quality in elite and amateur athletes. Materials and methods: A total of 1,420 elite (40.1%) and amateur (59.9%) athletes (41% women; 59% men) from 14 different countries participated in a cross-sectional design study. Data were collected using a battery of questionnaires that identified sociodemographic data, sleep quality index, PA levels, dietary habits, and the athletes' perception of their experience during the COVID-19 pandemic. Means and standard deviations were calculated for each variable. The analysis of variances and the correlation between variables were carried out with non-parametric statistics. A simple moderation effect was calculated to analyze the interaction between PA or dietary habits on the perception of the COVID-19 experience effect on sleep quality in elite and amateur athletes. Results: The PA level of elite athletes was higher than amateur athletes during COVID-19 (p < 0.001). However, the PA level of both categories of athletes was lower during COVID-19 than pre-COVID-19 (p < 0.01). In addition, amateurs had a higher diet quality than elite athletes during the pandemic (p = 0.014). The perception of the COVID-19 experience as controllable was significantly higher (p = 0.020) among elite athletes. In addition, two moderating effects had significant interactions. For amateur athletes, the PA level moderated the effect of controllable COVID-19 experience on sleep quality [F (3,777) = 3.05; p = 0.028], while for elite athletes, the same effect was moderated by dietary habits [F (3,506) = 4.47, p = 0.004]. Conclusion: Elite athletes had different lifestyle behaviors compared to amateurs during the COVID-19 lockdown. Furthermore, the relevance of maintaining high levels of PA for amateurs and good quality dietary habits by elite athletes was noted by the moderating effect that both variables had on the influence of the controllable experience during the COVID-19 pandemic on sleep quality.

A Twitter dataset for Monkeypox, May 2022.

After struggling with COVID-19 pandemic for two years, the world is finally recovering from this crisis. Nonetheless, another virus, Monkeypox, is quickly spreading throughout the world and in non-endemic regions and continents, threatening the world to a new pandemic. Twitter as a popular social media has successfully been used for predicting and controlling outbreaks. Much research previously has been done for building early warning systems, trend prediction, and misinformation and fake news detection. Since tweets are not accessible to all researchers, in this work, a publicly available dataset containing 2400202 tweets gathered from May first to December twenty-fifth, 2022 is presented. Twitter developers academic researcher API which returns all the tweets matching a given query was used to gather the dataset. To this end, the full archive search and keywords related to Monkeypox and its equivalents in other languages, i.e. Monkeypox or "monkey pox" or "viruela dei mono" or "variole du singe" or "variola do macoco" were used. The retweets were excluded using the negation operator, and the tweet ids and user ids were extracted and shared with public. Approximately, 1.79 percent (43047 number) of tweets were geotagged. To visualize the geotagged tweets, the longitude and latitude of the bounding box coordinates were averaged. This work will help researchers shed light on the news, patterns, and on-going discussions of Monkeypox on social media, identify hotspots, and help contain the Monkeypox virus.

Mitigating co-circulation of seasonal influenza and COVID-19 pandemic in the presence of vaccination: A mathematical modeling approach.

The co-circulation of two respiratory infections with similar symptoms in a population can significantly overburden a healthcare system by slowing the testing and treatment. The persistent emergence of contagious variants of SARS-CoV-2, along with imperfect vaccines and their waning protections, have increased the likelihood of new COVID-19 outbreaks taking place during a typical flu season. Here, we developed a mathematical model for the co-circulation dynamics of COVID-19 and influenza, under different scenarios of influenza vaccine coverage, COVID-19 vaccine booster coverage and efficacy, and testing capacity. We investigated the required minimal and optimal coverage of COVID-19 booster (third) and fourth doses, in conjunction with the influenza vaccine, to avoid the coincidence of infection peaks for both diseases in a single season. We show that the testing delay brought on by the high number of influenza cases impacts the dynamics of influenza and COVID-19 transmission. The earlier the peak of the flu season and the greater the number of infections with flu-like symptoms, the greater the risk of flu transmission, which slows down COVID-19 testing, resulting in the delay of complete isolation of patients with COVID-19 who have not been isolated before the clinical presentation of symptoms and have been continuing their normal daily activities. Furthermore, our simulations stress the importance of vaccine uptake for preventing infection, severe illness, and hospitalization at the individual level and for disease outbreak control at the population level to avoid putting strain on already weak and overwhelmed healthcare systems. As such, ensuring optimal vaccine coverage for COVID-19 and influenza to reduce the burden of these infections is paramount. We showed that by keeping the influenza vaccine coverage about 35% and increasing the coverage of booster or fourth dose of COVID-19 not only reduces the infections with COVID-19 but also can delay its peak time. If the influenza vaccine coverage is increased to 55%, unexpectedly, it increases the peak size of influenza infections slightly, while it reduces the peak size of COVID-19 as well as significantly delays the peaks of both of these diseases. Mask-wearing coupled with a moderate increase in the vaccine uptake may mitigate COVID-19 and prevent an influenza outbreak.

Leveraging Responsible, Explainable, and Local Artificial Intelligence Solutions for Clinical Public Health in the Global South.

In the present paper, we will explore how artificial intelligence (AI) and big data analytics (BDA) can help address clinical public and global health needs in the Global South, leveraging and capitalizing on our experience with the "Africa-Canada Artificial Intelligence and Data Innovation Consortium" (ACADIC) Project in the Global South, and focusing on the ethical and regulatory challenges we had to face. "Clinical public health" can be defined as an interdisciplinary field, at the intersection of clinical medicine and public health, whilst "clinical global health" is the practice of clinical public health with a special focus on health issue management in resource-limited settings and contexts, including the Global South. As such, clinical public and global health represent vital approaches, instrumental in (i) applying a community/population perspective to clinical practice as well as a clinical lens to community/population health, (ii) identifying health needs both at the individual and community/population levels, (iii) systematically addressing the determinants of health, including the social and structural ones, (iv) reaching the goals of population's health and well-being, especially of socially vulnerable, underserved communities, (v) better coordinating and integrating the delivery of healthcare provisions, (vi) strengthening health promotion, health protection, and health equity, and (vii) closing gender inequality and other (ethnic and socio-economic) disparities and gaps. Clinical public and global health are called to respond to the more pressing healthcare needs and challenges of our contemporary society, for which AI and BDA can help unlock new options and perspectives. In the aftermath of the still ongoing COVID-19 pandemic, the future trend of AI and BDA in the healthcare field will be devoted to building a more healthy, resilient society, able to face several challenges arising from globally networked hyper-risks, including ageing, multimorbidity, chronic disease accumulation, and climate change.

A tale of two stories: COVID-19 and disability. A critical scoping review of the literature on the effects of the pandemic among athletes with disabilities and para-athletes.

The still ongoing COVID-19 pandemic has dramatically impacted athletes, and, in particular, para-athletes and athletes with disabilities. However, there is no scholarly appraisal on this topic. Therefore, a critical scoping review of the literature was conducted. We were able to retrieve sixteen relevant studies. The sample size ranged from 4 to 183. Most studies were observational, cross-sectional, and questionnaire-based surveys, two studies were interventional, and two were longitudinal. One study was a technical feasibility study. Almost all studies were conducted as single-country studies, with the exception of one multi-country investigation. Five major topics/themes could be identified: namely, 1) impact of COVID-19-induced confinement on training and lifestyles in athletes with disabilities/para-athletes; 2) impact of COVID-19-induced confinement on mental health in athletes with disabilities/para-athletes; 3) impact of COVID-19-induced confinement on performance outcomes in athletes with disabilities/para-athletes; 4) risk of contracting COVID-19 among athletes with disabilities/para-athletes; and, finally, 5) impact of COVID-19 infection on athletes with disabilities/para-athletes. The scholarly literature assessed was highly heterogeneous, with contrasting findings, and various methodological limitations. Based on our considerations, we recommend that standardized, reliable tools should be utilized and new, specific questionnaires should be created, tested for reliability, and validated. High-quality, multi-center, cross-countries, longitudinal surveys should be conducted to overcome current shortcomings. Involving all relevant actors and stakeholders, including various national and international Paralympic Committees, as a few studies have done, is fundamental: community-led, participatory research can help identify gaps in the current knowledge about sports-related practices among the population of athletes with disabilities during an unprecedented period of measures undertaken that have significantly affected everyday life. Moreover, this could advance the field, by capturing the needs of para-athletes and athletes with disabilities and enabling the design of a truly "disability-inclusive response" to COVID-19 and similar future conditions/situations. Furthermore, follow-up studies on COVID-19-infected para-athletes and athletes with disabilities should be conducted. Evidence of long-term effects of COVID-19 is available only for able-bodied athletes, for whom cardiorespiratory residual alterations and mental health issues a long time after COVID-19 have been described.

Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study.

The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster's severity, progression and whether it can be defined as a hot-spot.

Facial atopic dermatitis may be exacerbate by masks: Insights from a multicenter, teledermatology, prospective study during COVID-19 pandemic.

BACKGROUND: Patients with atopic dermatitis (AD) display a defective skin barrier, consequently they may experience inflammatory flares with different exposures, including masks. Actually, beside scattering case reports, no study focused on the possible AD flaring due to masks. METHODS: In this multicenter prospective study AD patients with facial manifestation were followed with teledermatology and evaluated by two board-certified dermatologists at the baseline (T0) and after 1 month (T1) in which patients started to wear masks >6 hours per day. Demographics and clinical parameters, included and not limited to Eczema Area and Severity Index (EASI) and Dermatology Life Quality Index (DLQI), were carefully collected and analyzed. RESULTS: We enrolled 57 AD patients (M/F 28/29, 33.91 ± 12.26 yoa) that wore surgical masks (38 (66.7%)), community masks (11 (19.3%) and N95 (8 (14.0%)). Both DLQI and EASI increase during the time period (p<0.0001). DLQI variation was not influenced by age, BMI, and gender, mask type used and AD therapy (p=0.99), whilst EASI variation was significantly influenced by BMI, gender, and therapy (p=0.004). CONCLUSIONS: Mask wearing may prove detrimental to patients with atopic eczema and the same may not necessarily be the case for asthma patients.

Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

A tale of two stories: COVID-19 and disability. A critical scoping review of the literature on the effects of the pandemic among athletes with disabilities and para-athletes

The still ongoing COVID-19 pandemic has dramatically impacted athletes, and, in particular, para-athletes and athletes with disabilities. However, there is no scholarly appraisal on this topic. Therefore, a critical scoping review of the literature was conducted. We were able to retrieve sixteen relevant studies. The sample size ranged from 4 to 183. Most studies were observational, cross-sectional, and questionnaire-based surveys, two studies were interventional, and two were longitudinal. One study was a technical feasibility study. Almost all studies were conducted as single-country studies, with the exception of one multi-country investigation. Five major topics/themes could be identified: namely, 1) impact of COVID-19-induced confinement on training and lifestyles in athletes with disabilities/para-athletes;2) impact of COVID-19-induced confinement on mental health in athletes with disabilities/para-athletes;3) impact of COVID-19-induced confinement on performance outcomes in athletes with disabilities/para-athletes;4) risk of contracting COVID-19 among athletes with disabilities/para-athletes;and, finally, 5) impact of COVID-19 infection on athletes with disabilities/para-athletes. The scholarly literature assessed was highly heterogeneous, with contrasting findings, and various methodological limitations. Based on our considerations, we recommend that standardized, reliable tools should be utilized and new, specific questionnaires should be created, tested for reliability, and validated. High-quality, multi-center, cross-countries, longitudinal surveys should be conducted to overcome current shortcomings. Involving all relevant actors and stakeholders, including various national and international Paralympic Committees, as a few studies have done, is fundamental: community-led, participatory research can help identify gaps in the current knowledge about sports-related practices among the population of athletes with disabilities during an unprecedented period of measures undertaken that have significantly affected everyday life. Moreover, this could advance the field, by capturing the needs of para-athletes and athletes with disabilities and enabling the design of a truly “disability-inclusive response” to COVID-19 and similar future conditions/situations. Furthermore, follow-up studies on COVID-19-infected para-athletes and athletes with disabilities should be conducted. Evidence of long-term effects of COVID-19 is available only for able-bodied athletes, for whom cardiorespiratory residual alterations and mental health issues a long time after COVID-19 have been described.

COVID-19 Vaccination and the Rate of Immune and Autoimmune Adverse Events Following Immunization: Insights From a Narrative Literature Review.

Despite their proven efficacy and huge contribution to the health of humankind, vaccines continue to be a source of concern for some individuals around the world. Vaccinations against COVID-19 increased the number of distressed people and intensified their distrust, particularly as the pandemic was still emerging and the populations were encouraged to be vaccinated under various slogans like "back to normal life" and "stop coronavirus", goals which are still to be achieved. As fear of vaccination-related adverse events following immunization (AEFIs) is the main reason for vaccine hesitancy, we reviewed immune and autoimmune AEFIs in particular, though very rare, as the most worrisome aspect of the vaccines. Among others, autoimmune AEFIs of the most commonly administered COVID-19 vaccines include neurological ones such as Guillain-Barre syndrome, transverse myelitis, and Bell's palsy, as well as myocarditis. In addition, the newly introduced notion related to COVID-19 vaccines, "vaccine-induced immune thrombotic thrombocytopenia/vaccine-induced prothrombotic immune thrombotic thrombocytopenia" (VITT/VIPITT)", is of importance as well. Overviewing recent medical literature while focusing on the major immune and autoimmune AEFIs, demonstrating their rate of occurrence, presenting the cases reported, and their link to the specific type of COVID-19 vaccines represented the main aim of our work. In this narrative review, we illustrate the different vaccine types in current use, their associated immune and autoimmune AEFIs, with a focus on the 3 main COVID-19 vaccines (BNT162b2, mRNA-1273, and ChAdOx1). While the rate of AEFIs is extremely low, addressing the issue in this manner, in our opinion, is the best strategy for coping with vaccine hesitancy.

Masks trigger facial seborrheic dermatitis and psoriasis: evidence from a multicenter, case-control study during COVID-19 pandemic.

BACKGROUND: Wearing masks is an optimal preventive strategy during COVID-19 pandemic, but it may increase facial sebum production. However, few case reports have described seborrheic dermatitis (SeBD) and psoriasis (PsO) flares due to masks. Hence, we conducted a multicenter study to clarify the possibility of increased SeBD and PsO flares in association with mask wearing during the COVID-19 pandemic. METHODS: This multicenter study enrolled patients with a diagnosis of facial SeBD and PsO. All dermatological consultations were conducted in teledermatology at baseline (T0) and after 1 month (T1) Of >6 hours/day wearing mask. PsO patients were assessed using PsO Area and Severity Index (PASI) and self-administered PASI (SAPASI), whilst SeBD patients with symptom scale of seborrheic dermatitis' (SSSD) and seborrheic dermatitis area and severity index (SEDASI). RESULTS: A total of 33 (20 males, 13 females, average age 43.61±9.86) patients with PsO and 33 (20 males, 13 females, average age 44.00±8.58) with SeBD were enrolled. After 1 month, PsO patients displayed higher values of both PASI and SAPASI (P<0.0001), while SeBD patients experienced a flare, as testified by the increment of both SSSD and SEDASI (P<0.0001). Mask type did not seem to influence the flare severity. CONCLUSIONS: Masks remain an optimal preventive strategy during COVID-19 pandemic, but patients with PsO and SeBD may experience facial flares. Thus, therapeutic approach should be more aggressive in these groups of patients to counteract the triggering effect of masks.

The prevalence and severity of insomnia symptoms during COVID-19: A global systematic review and individual participant data meta-analysis.

INTRODUCTION: There have been no previous meta-analytic studies that have looked at the prevalence of insomnia symptoms in different COVID-19 groups using a single assessment instrument to evaluate insomnia symptoms while maintaining data homogeneity. The current review's associated goal is to undertake an individual participant data (IPD) analysis to further investigate past meta-analyses, a method that has been shown to be more robust than standard meta-analyses. MEETHODS: Only studies that used the Insomnia Severity Index (ISI) to assess insomnia are used in this analysis. The IPDMA was performed and registered in PROSPERO in compliance with the PRISMA IPD Statement (CRD42021275817). From November 2019 to August 2021, researchers explored seventeen databases and six preprint services for relevant studies. RESULTS: The pooled estimate of insomnia symptoms (subthreshold and clinically significant) was 52.57%. An estimated 16.66% of the population suffered from clinically significant insomnia, of which 13.75% suffered from moderate insomnia, and 2.50% suffered from severe insomnia. The different populations' grouping had no statistically significant differences in the prevalence of insomnia symptoms. Insomnia symptoms did not appear to be associated with age or sex. CONCLUSION: Our findings imply that the COVID-19 pandemic is linked to a significant rise in subthreshold insomnia symptoms, but not to moderate or severe insomnia. Educating people from all walks of life about the importance of sleep and the risk of acquiring insomnia symptoms during this or future pandemics should be a top concern.

Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts.

Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community-based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.

Estimating social contacts in mass gatherings for disease outbreak prevention and management: case of Hajj pilgrimage.

BACKGROUND: Most mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals. METHODS: We sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram. RESULTS: The simulation results showed that a plethora of risky contacts may occur during the rituals. Also, as the total number of pilgrims increases at each site, the number of risky contacts increases, and physical distancing measures may be challenging to maintain beyond a certain number of pilgrims in the site. CONCLUSIONS: This study presented a simulation tool that can be relevant for the risk assessment of a variety of (respiratory) infectious diseases, in addition to COVID-19 in the Hajj season. This tool can be expanded to include other contributing elements of disease transmission to quantify the risk of the mass gathering events.

A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments.

The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.