Search

Background/objectives: The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts., Methods: This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems., Results: We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses., Conclusion: In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat.

The continual social and economic impact of infectious diseases on nations has maintained sustained attention on their control and treatment, of which self-medication has been one of the means employed by some individuals. Self-medication complicates the attempt of their control and treatment as it conflicts with some of the measures implemented by health authorities. Added to these complications is the stigmatization of individuals with some diseases in some jurisdictions. This study investigates the co-infection of COVID-19 and malaria and its related deaths and further highlights how self-medication and stigmatization add to the complexities of the fight against these two diseases using Nigeria as a study case. Using a mathematical model on COVID-19 and malaria co-infection, we address the question: to what degree does the impact of the interaction between COVID-19 and malaria amplify infections and deaths induced by both diseases via self-medication and stigmatization? We demonstrate that COVID-19 related self-medication due to misdiagnoses contributes substantially to the prevalence of disease. The control reproduction numbers for these diseases and quantification of model parameters uncertainties and sensitivities are presented., Competing Interests: Declaration of competing interest The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of their respective institutions. The authors declare no conflict of interest. Ethics and consent: All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Global artificial intelligence (AI) governance must prioritize equity, embrace a decolonial mindset, and provide the Global South countries the authority to spearhead solution creation. Decolonization is crucial for dismantling Western-centric cognitive frameworks and mitigating biases. Integrating a decolonial approach to AI governance involves recognizing persistent colonial repercussions, leading to biases in AI solutions and disparities in AI access based on gender, race, geography, income and societal factors. This paradigm shift necessitates deliberate efforts to deconstruct imperial structures governing knowledge production, perpetuating global unequal resource access and biases. This research evaluates Sub-Saharan African progress in AI governance decolonization, focusing on indicators like AI governance institutions, national strategies, sovereignty prioritization, data protection regulations, and adherence to local data usage requirements. Results show limited progress, with only Rwanda notably responsive to decolonization among the ten countries evaluated; 80% are 'decolonization-aware', and one is 'decolonization-blind'. The paper provides a detailed analysis of each nation, offering recommendations for fostering decolonization, including stakeholder involvement, addressing inequalities, promoting ethical AI, supporting local innovation, building regional partnerships, capacity building, public awareness, and inclusive governance. This paper contributes to elucidating the challenges and opportunities associated with decolonization in SSA countries, thereby enriching the ongoing discourse on global AI governance., Competing Interests: We have no competing interests., (© 2024 The Authors.)

In 2022, there was a global resurgence of mpox, with different clinical-epidemiological features compared with previous outbreaks. Sexual contact was hypothesized as the primary transmission route, and the community of men having sex with men (MSM) was disproportionately affected. Because of the stigma associated with sexually transmitted infections, the real burden of mpox could be masked. We quantified the basic reproduction number ( R 0 ) and the underestimated fraction of mpox cases in 16 countries, from the onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-/low-risk populations) and two-route (sexual/non-sexual transmission) mathematical model. Machine learning (ML) was harnessed to identify underestimation determinants. Estimated R 0 ranged between 1.37 (Canada) and 3.68 (Germany). The underestimation rates for the high- and low-risk populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the reported cases, is highest in Colombia (3.60) and lowest in Canada (1.08). In the ML analysis, two clusters of countries could be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and the importance of religion. Given the substantial mpox underestimation, surveillance should be enhanced, and country-specific campaigns against the stigmatization of MSM should be organized, leveraging community-based interventions.

Background: According to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants., Methods: We used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries., Results: 21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central., Conclusion: Overall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Han, Rutayisire, Mbogning Fonkou, Avusuglo, Ahmadi, Asgary, Orbinski, Wu and Kong.)

Objective: To assess the impact of self-medication on the transmission dynamics of COVID-19 across different age groups, examine the interplay of vaccination and self-medication in disease spread, and identify the age group most prone to self-medication., Methods: We developed an age-structured compartmentalized epidemiological model to track the early dynamics of COVID-19. Age-structured data from the Government of Gauteng, encompassing the reported cumulative number of cases and daily confirmed cases, were used to calibrate the model through a Markov Chain Monte Carlo (MCMC) framework. Subsequently, uncertainty and sensitivity analyses were conducted on the model parameters., Results: We found that self-medication is predominant among the age group 15-64 (74.52%), followed by the age group 0-14 (34.02%), and then the age group 65+ (11.41%). The mean values of the basic reproduction number, the size of the first epidemic peak (the highest magnitude of the disease), and the time of the first epidemic peak (when the first highest magnitude occurs) are 4.16499, 241,715 cases, and 190.376 days, respectively. Moreover, we observed that self-medication among individuals aged 15-64 results in the highest spreading rate of COVID-19 at the onset of the outbreak and has the greatest impact on the first epidemic peak and its timing., Conclusion: Studies aiming to understand the dynamics of diseases in areas prone to self-medication should account for this practice. There is a need for a campaign against COVID-19-related self-medication, specifically targeting the active population (ages 15-64)., (© 2024. The Author(s).)

The recent mpox outbreak (in 2022-2023) has different clinical and epidemiological features compared with previous outbreaks of the disease. During this outbreak, sexual contact was believed to be the primary transmission route of the disease. In addition, the community of men having sex with men (MSM) was disproportionately affected by the outbreak. This population is also disproportionately affected by HIV infection. Given that both diseases can be transmitted sexually, the endemicity of HIV, and the high sexual behavior associated with the MSM community, it is essential to understand the effect of the two diseases spreading simultaneously in an MSM population. Particularly, we aim to understand the potential effects of HIV on an mpox outbreak in the MSM population. We develop a mechanistic mathematical model of HIV and mpox co-infection. Our model incorporates the dynamics of both diseases and considers HIV treatment with anti-retroviral therapy (ART). In addition, we consider a potential scenario where HIV infection increases susceptibility to mpox, and investigate the potential impact of this mechanism on mpox dynamics. Our analysis shows that HIV can facilitate the spread of mpox in an MSM population, and that HIV treatment with ART may not be sufficient to control the spread of mpox in the population. However, we showed that a moderate use of condoms or reduction in sexual contact in the population combined with ART is beneficial in controlling mpox transmission. Based on our analysis, it is evident that effective control of HIV, specifically through substantial ART use, moderate condom compliance, and reduction in sexual contact, is imperative for curtailing the transmission of mpox in an MSM population and mitigating the compounding impact of these intertwined epidemics., Competing Interests: The authors declare that there is no competing interest with this publication titled “Understanding the impact of HIV on mpox transmission in an MSM population: a mathematical modeling study’’., (© 2024 The Authors.)

Background: Most of the disability-related scholarly literature focuses on high-income countries, whereas there is a lack of data concerning challenges (barriers and obstacles) and opportunities (participatory research and community engagement) in the Global South. Moreover, many frameworks for interventions for people with disabilities (PWDs) have been designed for resource-rich contexts, and little is known about their translatability to low- and middle-income countries (LMICs)., Objective: The main objective of this study was to design and pilot an interventional approach based on an innovative framework aimed at improving the livelihood of PWDs in LMICs., Methodology: The present mixed-method study was conducted in Bamenda, North-West Region of Cameroon, through an intervention of household visits by community health workers using innovation and best practices informed by a systematic literature review and embedded into an evidence toolkit called the eBASE Family-Centered Evidence Toolkit for Disabilities (EFCETD), adapted from the WHO matrix and consisting of 43 questions across five categories (health, education, social wellbeing, empowerment, and livelihood). Out of 56 PWDs identified, 30 were randomly sampled, with an attrition of four participants. Three datasets (baseline, qualitative, and quantitative) were collected. The Washington Group tool, exploring the type of disability, gender, how long one has had the disability, their facility situation coupled with their coping strategies, and the context of livelihood, was used to design the questionnaire for baseline data collection. Qualitative data were collected through key informant interviews and focus group discussions analyzed with MAXQDA, while quantitative data were collected through the EFCETD and analyzed by means of descriptive statistics., Results: In total, 69.2% of PWDs were female individuals. Many PWDs were aged 10-20 years (57% of the sample size). Physical/motor disability was the most common type of disability recorded (84.6%). The mean percentile for education increased from 29.9% during the first visit to 70.2% during the last visit, while the mean percentile for health increased from 65.4 to 78.7% and the mean percentile for social wellbeing moved from 73.1 to 84.9%. The livelihood and empowerment standards increased from 16.3 to 37.2% and from 27.7 to 65.8%, respectively. Overall, the temporal trend was statistically significant ( F  = 35.11, p  < 0.0001). The adjusted score increased from the baseline value of 45.02 ± 2.38 to 61.07 ± 2.25, 65.24 ± 2.67, and 68.46 ± 2.78, at 4, 8, and 12 months, respectively. Compared to the baseline, all timepoints were significantly different, indicating a significant impact of the intervention, which became stable after 4 months and was preserved until 12 months., Conclusion: PWDs faced many endeavors for sustainability and challenges resulting from a lack of inclusive policies and practices, leading to their exclusion from education, employment, and healthcare. Using implementation science approaches could bridge the gap and make policies and practices more effective., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yuh, Ndum Okwen, Miong, Bragazzi, Kong, Movahedi Nia, Kinlabel and Patrick Mbah.)

Background: The COVID-19 pandemic has highlighted gaps in the current handling of medical resource demand surges and the need for prioritizing scarce medical resources to mitigate the risk of health care facilities becoming overwhelmed., Objective: During a health care emergency, such as the COVID-19 pandemic, the public often uses social media to express negative sentiment (eg, urgency, fear, and frustration) as a real-time response to the evolving crisis. The sentiment expressed in COVID-19 posts may provide valuable real-time information about the relative severity of medical resource demand in different regions of a country. In this study, Twitter (subsequently rebranded as X) sentiment analysis was used to investigate whether an increase in negative sentiment COVID-19 tweets corresponded to a greater demand for hospital intensive care unit (ICU) beds in specific regions of the United States, Brazil, and India., Methods: Tweets were collected from a publicly available data set containing COVID-19 tweets with sentiment labels and geolocation information posted between February 1, 2020, and March 31, 2021. Regional medical resource shortage data were gathered from publicly available data sets reporting a time series of ICU bed demand across each country. Negative sentiment tweets were analyzed using the Granger causality test and convergent cross-mapping (CCM) analysis to assess the utility of the time series of negative sentiment tweets in forecasting ICU bed shortages., Results: For the United States (30,742,934 negative sentiment tweets), the results of the Granger causality test (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a stochastic system) were significant (P<.05) for 14 (28%) of the 50 states that passed the augmented Dickey-Fuller test at lag 2, and the results of the CCM analysis (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a dynamic system) were significant (P<.05) for 46 (92%) of the 50 states. For Brazil (3,004,039 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (22%) of the 27 federative units, and the results of the CCM analysis were significant (P<.05) for 26 (96%) of the 27 federative units. For India (4,199,151 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (23%) of the 26 included regions (25 states and the national capital region of Delhi), and the results of the CCM analysis were significant (P<.05) for 26 (100%) of the 26 included regions., Conclusions: This study provides a novel approach for identifying the regions of high hospital bed demand during a health care emergency scenario by analyzing Twitter sentiment data. Leveraging analyses that take advantage of natural language processing-driven tweet extraction systems has the potential to be an effective method for the early detection of medical resource demand surges., (©Mahakprit Kaur, Taylor Cargill, Kevin Hui, Minh Vu, Nicola Luigi Bragazzi, Jude Dzevela Kong. Originally published in JMIR Formative Research (https://formative.jmir.org), 29.01.2024.)

Environmental factors have a significant impact on the transmission of infectious diseases. Existing results show that the novel coronavirus can persist outside the host. We propose a susceptible-exposed-presymptomatic-infectious-asymptomatic-recovered-susceptible (SEPIARS) model with a vaccination compartment and indirect incidence to explore the effect of environmental conditions, temperature and humidity, on the transmission of the SARS-CoV-2 virus. Using climate data and daily confirmed cases data in two Canadian cities with different atmospheric conditions, we evaluate the mortality rates of the SARS-CoV-2 virus and further estimate the transmission rates by the inverse method, respectively. The numerical results show that high temperature or humidity can be helpful in mitigating the spread of COVID-19 during the warm summer months. Our findings verify that nonpharmaceutical interventions are less effective if the virus can persist for a long time on surfaces. Based on climate data, we can forecast the transmission rate and the infection cases up to four weeks in the future by a generalized boosting machine learning model., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest concerning the publication of this manuscript., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Competing Interests: Competing interests: None declared.

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. Owing to inadequate public awareness, unestablished monitoring practices, limited testing and stigmas, there might exist extensive under-ascertainment of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (s.d. 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria). The mean overall report rate was estimated to be 5.37% (s.d. 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in São Tomé and Príncipe. An average of 5.46% (s.d. 6.4%) of all infected cases were severe cases and 66.74% (s.d. 17.28%) were asymptomatic ones. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance systems in these countries., Competing Interests: We declare we have no competing interests., (© 2023 The Authors.)

Although rejected by the World Health Organization, the human and even veterinary formulation of ivermectin has widely been used for prevention and treatment of COVID-19. In this work we leverage Twitter to understand the reasons for the drug use from ivermectin supporters, their source of information, their emotions, their gender demographics, and location information, in Nigeria and South Africa. Topic modelling is performed on a Twitter dataset gathered using keywords 'ivermectin' and 'ivm'. A model is fine-tuned on RoBERTa to find the stance of the tweets. Statistical analysis is performed to compare the stance and emotions. Most ivermectin supporters either redistribute conspiracy theories posted by influencers, or refer to flawed studies confirming ivermectin efficacy in vitro . Three emotions have the highest intensity, optimism, joy and disgust. The number of anti-ivermectin tweets has a significant positive correlation with vaccination rate. All the provinces in South Africa and most of the provinces of Nigeria are pro-ivermectin and have higher disgust polarity. This work makes the effort to understand public discussions regarding ivermectin during the COVID-19 pandemic to help policy-makers understand the rationale behind its popularity, and inform more targeted policies to discourage self-administration of ivermectin. Moreover, it is a lesson to future outbreaks.

It is imperative that resources are channelled towards programs that are efficient and cost effective in combating the spread of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study proposed and analyzed control strategies for that purpose. We developed a mathematical disease model within an optimal control framework that allows us to investigate the best approach for curbing COVID-19 epidemic. We address the following research question: what is the role of community compliance as a measure for COVID-19 control? Analyzing the impact of community compliance of recommended guidelines by health authorities-examples, social distancing, face mask use, and sanitizing-coupled with efforts by health authorities in areas of vaccine provision and effective quarantine-showed that the best intervention in addition to implementing vaccination programs and effective quarantine measures, is the active incorporation of individuals' collective behaviours, and that resources should also be directed towards community campaigns on the importance of face mask use, social distancing, and frequent sanitizing, and any other collective activities. We also demonstrated that collective behavioral response of individuals influences the disease dynamics; implying that recommended health policy should be contextualized., (© 2023. Springer Nature Limited.)

Social media contains useful information about people and society that could help advance research in many different areas of health (e.g. by applying opinion mining, emotion/sentiment analysis and statistical analysis) such as mental health, health surveillance, socio-economic inequality and gender vulnerability. User demographics provide rich information that could help study the subject further. However, user demographics such as gender are considered private and are not freely available. In this study, we propose a model based on transformers to predict the user's gender from their images and tweets. The image-based classification model is trained in two different methods: using the profile image of the user and using various image contents posted by the user on Twitter. For the first method a Twitter gender recognition dataset, publicly available on Kaggle and for the second method the PAN-18 dataset is used. Several transformer models, i.e. vision transformers (ViT), LeViT and Swin Transformer are fine-tuned for both of the image datasets and then compared. Next, different transformer models, namely, bidirectional encoders representations from transformers (BERT), RoBERTa and ELECTRA are fine-tuned to recognize the user's gender by their tweets. This is highly beneficial, because not all users provide an image that indicates their gender. The gender of such users could be detected from their tweets. The significance of the image and text classification models were evaluated using the Mann-Whitney U test. Finally, the combination model improved the accuracy of image and text classification models by 11.73 and 5.26% for the Kaggle dataset and by 8.55 and 9.8% for the PAN-18 dataset, respectively. This shows that the image and text classification models are capable of complementing each other by providing additional information to one another. Our overall multimodal method has an accuracy of 88.11% for the Kaggle and 89.24% for the PAN-18 dataset and outperforms state-of-the-art models. Our work benefits research that critically require user demographic information such as gender to further analyze and study social media content for health-related issues.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Background: Understanding of the early immune response in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) breakthrough infections is limited., Methods: Ninety-eight patients with coronavirus disease 2019 (COVID-19) breakthrough infections were divided into two groups, with intervals from receiving the second dose of inactivated vaccine to the onset of illness <60 or ≥60 days., Results: The median lymphocyte count and the median anti-SARS-CoV-2 spike immunoglobulin G (IgG) and immunoglobulin M (IgM) titers were higher in the <60-day interval group compared with the corresponding medians in the ≥60-day interval group (p = 0.005, p = 0.001, and p = 0.001, respectively). The median interleukin-6 (IL-6) level in the <60-day interval group was significantly lower than the median IL-6 level in the ≥60-day interval group (p < 0.001)., Conclusions: Our results highlight the different anti-SARS-CoV-2 spike IgG and IgM antibody titers among patients with different intervals from receiving the second dose of inactivated vaccine to the onset of illness., (© 2023 The Authors. The Clinical Respiratory Journal published by John Wiley & Sons Ltd.)

Monkeypox, a zoonotic disease, is emerging as a potential sexually transmitted infection/disease, with underlying transmission mechanisms still unclear. We devised a risk-structured, compartmental model, incorporating sexual behavior dynamics. We compared different strategies targeting the high-risk population: a scenario of control policies geared toward the use of condoms and/or sexual abstinence (robust control strategy) with risk compensation behavior change, and a scenario of control strategies with behavior change in response to the doubling rate (adaptive control strategy). Monkeypox's basic reproduction number is 1.464, 0.0066, and 1.461 in the high-risk, low-risk, and total populations, respectively, with the high-risk group being the major driver of monkeypox spread. Policies imposing condom use or sexual abstinence need to achieve a 35% minimum compliance rate to stop further transmission, while a combination of both can curb the spread with 10% compliance to abstinence and 25% to condom use. With risk compensation, the only option is to impose sexual abstinence by at least 35%. Adaptive control is more effective than robust control where the daily sexual contact number is reduced proportionally and remains constant thereafter, shortening the time to epidemic peak, lowering its size, facilitating disease attenuation, and playing a key role in controlling the current outbreak., (© 2023 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Global well-being (GWB) is a complex, multi-dimensional, and multi-faceted construct that can be explored from two different, but often overlapping, complementary perspectives: the subjective and the objective ones. The subjective perspective, in turn, is comprised of two dimensions: namely, the hedonic and the eudaimonic standpoints. Within the former dimension, researchers have developed the concept of subjective hedonic well-being (SHWB), whereas, within the latter, they have built the framework of psychological and social well-being (PSWB). Disabled people have poorer well-being due to their pathology and may more frequently suffer from anxiety and depressive disorders than their able-bodied counterparts. Sports participation is an essential way to cope with disability. On the other hand, compared with their able-bodied peers, athletes with disabilities and para-athletes undergo a unique series of stressors. Little is known in terms of hedonic and eudaimonic well-being and quality of life in this specific population. Here, we review the literature, with an emphasis on the current state-of-art and gaps in knowledge that need to be addressed by future research. High-quality, large-scale investigations are needed to have a better understanding of the self-perceived (hedonic) and objective (eudaimonic) well-being and quality of life of disabled people practicing sports, athletes with disabilities, and para-athletes., Competing Interests: PO, MY, GA, and RP were employed by Effective Basic Services (eBASE). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Puce, Okwen, Yuh, Akah Ndum Okwen, Pambe Miong, Kong and Bragazzi.)

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.

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., (© 2023. The Author(s).)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R&#95;c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19.

Monkeypox, a milder disease compared to smallpox, is caused by a virus initially discovered and described in 1958 by the prominent Danish virologist von Magnus, who was investigating an infectious outbreak affecting monkey colonies. Currently, officially starting from May 2022, an outbreak of monkeypox is ongoing, with 51 000 cases being notified as of September 1, 2022-51 408 confirmed, 28 suspected, and 12 fatalities, for a grand total of 51 448 cases. More than 100 countries and territories are affected, from all the six World Health Organization regions. There are some striking features, that make this outbreak rather unusual when compared with previous outbreaks, including a shift on average age and the most affected age group, affected sex/gender, risk factors, clinical course, presentation, and the transmission route. Initially predominantly zoonotic, with an animal-to-human transmission, throughout the last decades, human-to-human transmission has become more and more sustained and effective. In particular, clusters of monkeypox have been described among men having sex with men, some of which have been epidemiologically linked to international travel to nonendemic countries and participation in mass gathering events/festivals, like the "Maspalomas (Gran Canaria) 2022 pride." This review will specifically focus on the "emerging" transmission route of the monkeypox virus, that is to say, the sexual transmission route, which, although not confirmed yet, seems highly likely in the diffusion of the infectious agent., (© 2022 Wiley Periodicals LLC.)

An emerging outbreak of monkeypox infection is quickly spreading worldwide, being currently reported in more than 30 countries, with slightly less than 1000 cases. In the present preliminary report, we collected and synthesized early data concerning epidemiological trends and clinical features of the ongoing outbreak and we compared them with those of previous outbreaks. Data were pooled from six clusters in Italy, Australia, the Czech Republic, Portugal, and the United Kingdom, totaling 124 cases (for 35 of which it was possible to retrieve detailed information). The ongoing epidemic differs from previous outbreaks in terms of age (54.29% of individuals in their thirties), sex/gender (most cases being males), risk factors, and transmission route, with sexual transmission being highly likely. Also, the clinical presentation is atypical and unusual, being characterized by anogenital lesions and rashes that relatively spare the face and extremities. The most prevalent sign/symptom reported was fever (in 54.29% of cases) followed by inguinal lymphadenopathy (45.71%) and exanthema (40.00%). Asthenia, fatigue, and headache were described in 22.86% and 25.71% of the subjects, respectively. Myalgia was present in 17.14% of the cases. Both genital and anal lesions (ulcers and vesicles) were reported in 31.43% of the cases. Finally, cervical lymphadenopathy was described in 11.43% of the sample, while the least commonly reported symptoms were diarrhea and axillary lymphadenopathy (5.71% of the case series for both symptoms). Some preliminary risk factors can be identified (being a young male, having sex with other men, engaging in risky behaviors and activities, including condomless sex, human immunodeficiency virus positivity (54.29% of the sample analyzed), and a story of previous sexually transmitted infections, including syphilis). On the other hand, being fully virally suppressed and undetectable may protect against a more severe infectious course. However, further research in the field is urgently needed., (© 2022 Wiley Periodicals LLC.)

Cholera is an acute enteric infectious disease caused by the Gram-negative bacterium Vibrio cholerae . Despite a huge body of research, the precise nature of its transmission dynamics has yet to be fully elucidated. Mathematical models can be useful to better understand how an infectious agent can spread and be properly controlled. We develop a compartmental model describing a human population, a bacterial population as well as a phage population. We show that there might be eight equilibrium points, one of which is a disease free equilibrium point. We carry out numerical simulations and sensitivity analyses and we show that the presence of phage can reduce the number of infectious individuals. Moreover, we discuss the main implications in terms of public health management and control strategies.

Paralympic powerlifting (PP), formerly known as "International Paralympic Committee" (IPC) powerlifting, is the format of powerlifting adapted for athletes with disabilities, and it differs from the version for able-bodied athletes in that it consists of bench press only. According to the mandate of the IPC, PP athletes should be enabled to achieve sporting excellence. As such, rigorous evidence is needed. However, to the best of our knowledge, there exists no systematic assessment of the body of scholarly evidence in the field of PP. Therefore, the present study was conducted to fill in this gap of knowledge, by conducting a scoping review of the literature enhanced by a bibliometrics analysis and by mining two major scholarly databases (MEDLINE via PubMed and Scopus). The aim was to provide a review/summary of the findings to date to help practitioners and athletes. Thirty-seven studies were retained in the present study. These covered the following thematic areas: (i) warm-up strategies ( n = 2); (ii) aspects of training ( n = 2); (iii) physiological aspects and responses ( n = 2); (iv) psychological aspects and responses ( n = 2); (v) biomechanics of bench press ( n = 8); (vi) recovery strategy ( n = 5); (vii) impact of the disability and type of disability ( n = 4); (viii) epidemiology of PP ( n = 6); and (ix) new analytical/statistical approaches for kinematics assessments, internal load monitoring, and predictions of mechanical outputs in strength exercises and in PP ( n = 6). Bibliometrics analysis of the PP-related scientific output revealed that, despite having already become a paralympic sports discipline in 1984, only in the last few years, PP has been attracting a lot of interest from the community of researchers, with the first scholarly contribution dating back to 2012, and with more than one-third of the scientific output being published this year (2022). As such, this scholarly discipline is quite recent and young. Moreover, the community dealing with this topic is poorly interconnected, with most authors contributing to just one article, and with one single author being a hub node of the author network. Distributions of the number of articles and the authors/co-authors were found to be highly asymmetrical, indicating that this research is still in its infancy and has great room as well as great potential to grow. Reflecting this, many research topics are also overlooked and underdeveloped, with the currently available evidence being based on a few studies.

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

We conducted an observational retrospective study on patients hospitalized with COVID-19, during March 05, 2020, to October 28, 2021, and developed an agent-based model to evaluate effectiveness of recommended healthcare resources (hospital beds and ventilators) management strategies during the COVID-19 pandemic in Gauteng, South Africa. We measured the effectiveness of these strategies by calculating the number of deaths prevented by implementing them. We observed differ ences between the epidemic waves. The length of hospital stay (LOS) during the third wave was lower than the first two waves. The median of the LOS was 6.73 days, 6.63 days and 6.78 days for the first, second and third wave, respectively. A combination of public and private sector provided hospital care to COVID-19 patients requiring ward and Intensive Care Units (ICU) beds. The private sector provided 88.4% of High care (HC)/ICU beds and 49.4% of ward beds, 73.9% and 51.4%, 71.8% and 58.3% during the first, second and third wave, respectively. Our simulation results showed that with a high maximum capacity, i.e., 10,000 general and isolation ward beds, 4,000 high care and ICU beds and 1,200 ventilators, increasing the resource capacity allocated to COVID- 19 patients by 25% was enough to maintain bed availability throughout the epidemic waves. With a medium resource capacity (8,500 general and isolation ward beds, 3,000 high care and ICU beds and 1,000 ventilators) a combination of resource management strategies and their timing and criteria were very effective in maintaining bed availability and therefore preventing excess deaths. With a low number of maximum available resources (7,000 general and isolation ward beds, 2,000 high care and ICU beds and 800 ventilators) and a severe epidemic wave, these strategies were effective in maintaining the bed availability and minimizing the number of excess deaths throughout the epidemic wave., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Alavinejad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Circadian rhythms are a series of endogenous autonomous oscillators that are generated by the molecular circadian clock which coordinates and synchronizes internal time with the external environment in a 24-h daily cycle (that can also be shorter or longer than 24 h). Besides daily rhythms, there exist as well other biological rhythms that have different time scales, including seasonal and annual rhythms. Circadian and other biological rhythms deeply permeate human life, at any level, spanning from the molecular, subcellular, cellular, tissue, and organismal level to environmental exposures, and behavioral lifestyles. Humans are immersed in what has been called the "circadian landscape," with circadian rhythms being highly pervasive and ubiquitous, and affecting every ecosystem on the planet, from plants to insects, fishes, birds, mammals, and other animals. Anthropogenic behaviors have been producing a cascading and compounding series of effects, including detrimental impacts on human health. However, the effects of climate change on sleep have been relatively overlooked. In the present narrative review paper, we wanted to offer a way to re-read/re-think sleep medicine from a planetary health perspective. Climate change, through a complex series of either direct or indirect mechanisms, including (i) pollution- and poor air quality-induced oxygen saturation variability/hypoxia, (ii) changes in light conditions and increases in the nighttime, (iii) fluctuating temperatures, warmer values, and heat due to extreme weather, and (iv) psychological distress imposed by disasters (like floods, wildfires, droughts, hurricanes, and infectious outbreaks by emerging and reemerging pathogens) may contribute to inducing mismatches between internal time and external environment, and disrupting sleep, causing poor sleep quantity and quality and sleep disorders, such as insomnia, and sleep-related breathing issues, among others. Climate change will generate relevant costs and impact more vulnerable populations in underserved areas, thus widening already existing global geographic, age-, sex-, and gender-related inequalities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bragazzi, Garbarino, Puce, Trompetto, Marinelli, Currà, Jahrami, Trabelsi, Mellado, Asgary, Wu and Kong.)

Monkeypox is an emerging zoonotic disease caused by the monkeypox virus, which is an infectious agent belonging to the genus Orthopoxvirus . Currently, commencing from the end of April 2022, an outbreak of monkeypox is ongoing, with more than 43,000 cases reported as of 23 August 2022, involving 99 countries and territories across all the six World Health Organization (WHO) regions. On 23 July 2022, the Director-General of the WHO declared monkeypox a global public health emergency of international concern (PHEIC), since the outbreak represents an extraordinary, unusual, and unexpected event that poses a significant risk for international spread, requiring an immediate, coordinated international response. However, the real magnitude of the burden of disease could be masked by failures in ascertainment and under-detection. As such, underestimation affects the efficiency and reliability of surveillance and notification systems and compromises the possibility of making informed and evidence-based policy decisions in terms of the adoption and implementation of ad hoc adequate preventive measures. In this review, synthesizing 53 papers, we summarize the determinants of the underestimation of sexually transmitted diseases, in general, and, in particular, monkeypox, in terms of all their various components and dimensions (under-ascertainment, underreporting, under-detection, under-diagnosis, misdiagnosis/misclassification, and under-notification)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bragazzi, Woldegerima, Iyaniwura, Han, Wang, Shausan, Badu, Okwen, Prescod, Westin, Omame, Converti, Mellado, Wu and Kong.)

Marine organisms normally swim at elevated speeds relative to cruising speeds only during strenuous activity, such as predation or escape. We measured swimming speeds of 29 ram ventilating sharks from 10 species and of three Atlantic bluefin tunas immediately after exhaustive exercise (fighting a capture by hook-and-line) and unexpectedly found all individuals exhibited a uniform mechanical response, with swimming speed initially two times higher than the cruising speeds reached approximately 6 h later. We hypothesized that elevated swimming behaviour is a means to increase energetic demand and drive the removal of lactate accumulated during capture via oxidation. To explore this hypothesis, we estimated the mechanical work that must have been spent by an animal to elevate its swim speed and then showed that the amount of lactate that could have been oxidized to fuel it comprises a significant portion of the amount of lactate normally observed in fishes after exhaustive exercise. An estimate for the full energetic cost of the catch-and-release event ensued., (© 2022 The Authors.)

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., Competing Interests: The authors have declared that no competing interests exist.

The rarity of parthenogenetic species is typically attributed to the reduced genetic variability that accompanies the absence of sex, yet natural parthenogens can be surprisingly successful. Ecological success is often proposed to derive from hybridization through enhanced genetic diversity from repetitive origins or enhanced phenotypic breadth from heterosis. Here, we tested and rejected both hypotheses in a classic parthenogen, the diploid grasshopper Warramaba virgo . Genetic data revealed a single hybrid mating origin at least 0.25 million years ago, and comparative analyses of 14 physiological and life history traits showed no evidence for altered fitness relative to its sexual progenitors. Our findings imply that the rarity of parthenogenesis is due to constraints on origin rather than to rapid extinction.

COVID-19 has been prevalent worldwide for about 2 years now and has brought unprecedented challenges to our society. Before vaccines were available, the main disease intervention strategies were non-pharmaceutical. Starting December 2020, in Ontario, Canada, vaccines were approved for administering to vulnerable individuals and gradually expanded to all individuals above the age of 12. As the vaccine coverage reached a satisfactory level among the eligible population, normal social activities resumed and schools reopened starting September 2021. However, when schools reopen for in-person learning, children under the age of 12 are unvaccinated and are at higher risks of contracting the virus. We propose an age-stratified model based on the age and vaccine eligibility of the individuals. We fit our model to the data in Ontario, Canada and obtain a good fitting result. The results show that a relaxed between-group contact rate may trigger future epidemic waves more easily than an increased within-group contact rate. An increasing mixed contact rate of the older group quickly amplifies the daily incidence numbers for both groups whereas an increasing mixed contact rate of the younger group mainly leads to future waves in the younger group alone. The results indicate the importance of accelerating vaccine rollout for younger individuals in mitigating disease spread., Competing Interests: We declare that the authors have no conflict of interest., (© 2022 The Authors.)

Background: Globally, nonpharmaceutical interventions for COVID-19, including stay-at-home policies, limitations on gatherings and closure of public spaces, are being lifted. We explored the effect of lifting a stay-at-home policy on virus resurgence under different conditions., Methods: Using confirmed case data from Toronto, Canada, between Feb. 24 and June 24, 2020, we ran a compartmental model with household structure to simulate the impact of the stay-at-home policy considering different levels of compliance. We estimated threshold values for the maximum number of contacts, probability of transmission and testing rates required for the safe reopening of the community., Results: After the implementation of the stay-at-home policy, the contact rate outside the household fell by 39% (from 11.58 daily contacts to 7.11). The effective reproductive number decreased from 3.56 (95% confidence interval [CI] 3.02-4.14) on Mar. 12 to 0.84 (95% CI 0.79-0.89) on May 6. Strong adherence to stay-at-home policies appeared to prevent SARS-CoV-2 resurgence, but extending the duration of stay-at-home policies beyond 2 months had little added effect on cumulative cases (25 958 for 65 days of a stay-at-home policy and 23 461 for 95 days, by July 2, 2020) and deaths (1404 for 65 days and 1353 for 95 days). To avoid a resurgence, the average number of contacts per person per day should be kept below 9, with strict nonpharmaceutical interventions in place., Interpretation: Our study demonstrates that the stay-at-home policy implemented in Toronto in March 2020 had a substantial impact on mitigating the spread of SARS-CoV-2. In the context of the early pandemic, before the emergence of variants of concern, reopening schools and workplaces was possible only with other nonpharmaceutical interventions in place., Competing Interests: Competing interests: None declared., (© 2022 CMA Impact Inc. or its licensors.)

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dai, Younis, Kong, Puce, Jabbour, Yuan and Bragazzi.)

Background: The "Coronavirus Disease 2019" (COVID-19) pandemic has become a major challenge for all healthcare systems worldwide, and besides generating a high toll of deaths, it has caused economic losses. Hospitals have played a key role in providing services to patients and the volume of hospital activities has been refocused on COVID-19 patients. Other activities have been limited/repurposed or even suspended and hospitals have been operating with reduced capacity. With the decrease in non-COVID-19 activities, their financial system and sustainability have been threatened, with hospitals facing shortage of financial resources. The aim of this study was to investigate the effects of COVID-19 on the revenues of public hospitals in Lorestan province in western Iran, as a case study., Method: In this quasi-experimental study, we conducted the interrupted time series analysis to evaluate COVID-19 induced changes in monthly revenues of 18 public hospitals, from April 2018 to August 2021, in Lorestan, Iran. In doing so, public hospitals report their earnings to the University of Medical Sciences monthly; then, we collected this data through the finance office., Results: Due to COVID-19, the revenues of public hospitals experienced an average monthly decrease of $172,636 thousand (P-value = 0.01232). For about 13 months, the trend of declining hospital revenues continued. However, after February 2021, a relatively stable increase could be observed, with patient admission and elective surgeries restrictions being lifted. The average monthly income of hospitals increased by $83,574 thousand., Conclusion: COVID-19 has reduced the revenues of public hospitals, which have faced many problems due to the high costs they have incurred. During the crisis, lack of adequate fundings can damage healthcare service delivery, and policymakers should allocate resources to prevent potential shocks., Competing Interests: All authors declare that there are no conflicts of interest.

Comparative analyses have a long history of macro-ecological and -evolutionary approaches to understand structure, function, mechanism and constraint. As the pace of science accelerates, there is ever-increasing access to diverse types of data and open access databases that are enabling and inspiring new research. Whether conducting a species-level trait-based analysis or a formal meta-analysis of study effect sizes, comparative approaches share a common reliance on reliable, carefully curated databases. Unlike many scientific endeavors, building a database is a process that many researchers undertake infrequently and in which we are not formally trained. This Commentary provides an introduction to building databases for comparative analyses and highlights challenges and solutions that the authors of this Commentary have faced in their own experiences. We focus on four major tips: (1) carefully strategizing the literature search; (2) structuring databases for multiple use; (3) establishing version control within (and beyond) your study; and (4) the importance of making databases accessible. We highlight how one's approach to these tasks often depends on the goal of the study and the nature of the data. Finally, we assert that the curation of single-question databases has several disadvantages: it limits the possibility of using databases for multiple purposes and decreases efficiency due to independent researchers repeatedly sifting through large volumes of raw information. We argue that curating databases that are broader than one research question can provide a large return on investment, and that research fields could increase efficiency if community curation of databases was established., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2022. Published by The Company of Biologists Ltd.)

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31-4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Metabolic risks including high body mass index, high fasting plasma glucose, high low-density lipoprotein cholesterol, high systolic blood pressure, kidney dysfunction and low bone mineral density, contribute heavy burden to the US health systems. We aimed to investigate the burden attributable to metabolic risks in the US from 1990 to 2019., Methods: Using methodology of Global Burden of Disease Study, the deaths and DALYs attributable to metabolic risks were analyzed by age, gender, states, Socio-demographic Index (SDI) and diseases from 1990 to 2019 in the US., Results: In 2019, the age-standardized death and DALY rates attributable to metabolic risks were 174.9 and 4738.7 per 100,000 people, accounting for 33.1% and 18.2% of death and DALY rates from all causes in the US, and there was a decrease by -32.5% and -21.2% in age-standardized death and DALY rates since 1990. The burden attributable to metabolic risks increased with age, and was higher in males than females. In addition, the burden varied widely across the states, generally in inverse proportion to the SDI levels, and the heaviest burden was observed in East and West South-Central of the US. Cardiovascular diseases carried heavy burden attributable to metabolic risks., Conclusion: The burden attributable to metabolic risks remained major public health concerns in the US. Prevention of metabolic risks should be a high priority in the US., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Safiri, Behzadifar, Kong, Zguira, Bragazzi, Zhong and Zhang.)

After the start of the COVID-19 pandemic and its spread across the world, countries have adopted containment measures to stop its transmission, limit fatalities, and relieve hospitals from straining and overwhelming conditions imposed by the virus. Many countries implemented social distancing and lockdown strategies that negatively impacted their economies and the psychological wellbeing of their citizens, even though they contributed to saving lives. Recently approved and available, COVID-19 vaccines can provide a really viable and sustainable option for controlling the pandemic. However, their uptake represents a global challenge due to vaccine hesitancy and logistic-organizational hurdles that have made its distribution stagnant in several developed countries despite several appeals by the media, policy- and decision-makers, and community leaders. Vaccine distribution is also a concern in developing countries, where there is a scarcity of doses. The objective of the present study was to set up a metric to assess vaccination uptake and identify national socio-economic factors influencing this indicator. We conducted a cross-country study. We first estimated the vaccination uptake rate across countries by fitting a logistic model to reported daily case numbers. Using the uptake rate, we estimated the vaccine roll-out index. Next, we used Random Forest, an "off-the-shelf" machine learning algorithm, to study the association between vaccination uptake rate and socio-economic factors. We found that the mean vaccine roll-out index is 0.016 (standard deviation 0.016), with a range between 0.0001 (Haiti) and 0.0829 (Mongolia). The top four factors associated with the vaccine roll-out index are the median per capita income, human development index, percentage of individuals who have used the internet in the last three months, and health expenditure per capita. The still-ongoing COVID-19 pandemic has shed light on the disparity in vaccine adoption across low- and high-income countries, which represents a global public health challenge. We must pave the way for universal access to vaccines and other approved treatments, regardless of demographic structures and underlying health conditions. Income disparity remains, instead, an important cause of vaccine inequity, which restricts the functioning of the global vaccine allocation framework and, thus, the ending of the pandemic. Stronger mechanisms are needed to foster countries' political willingness to promote vaccine and drug access equity in a globalized society where future pandemics and other global health crises can be anticipated.

In the present paper, we aimed to determine the influence of various non-pharmaceutical interventions (NPIs) enforced during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave. For this purpose, we took into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. We estimated the growth of the first and second wave across countries by fitting a logistic model to daily-reported case numbers, up to the first and second epidemic peaks. We estimated the basic and effective (second wave) reproduction numbers across countries. Next, we used a random forest algorithm to study the association between the growth rate of the second wave and NPIs as well as pre-existing country-specific characteristics. Lastly, we compared the growth rate of the first and second waves of COVID-19. The top three factors associated with the growth of the second wave were body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times, and restrictions on gatherings of 10 people or less. Artificial intelligence techniques can help scholars as well as decision and policy-makers estimate the effectiveness of public health policies, and implement "smart" interventions, which are as efficacious as stringent ones., (© 2022. The Author(s).)

Self-medication is an important initial response to illness in Africa. This mode of medication is often done with the help of African traditional medicines. Because of the misconception that African traditional medicines can cure/prevent all diseases, some Africans may opt for COVID-19 prevention and management by self-medicating. Thus to efficiently predict the dynamics of COVID-19 in Africa, the role of the self-medicated population needs to be taken into account. In this paper, we formulate and analyse a mathematical model for the dynamics of COVID-19 in Cameroon. The model is represented by a system of compartmental age-structured ODEs that takes into account the self-medicated population and subdivides the human population into two age classes relative to their current immune system strength. We use our model to propose policy measures that could be implemented in the course of an epidemic in order to better handle cases of self-medication.