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Copyright of Canadian Journal of Dental Hygiene is the property of Canadian Dental Hygienists Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reducing examination table paper (ETP) use may help curb carbon emissions from health care. Six participants applied Glo Germ (DMA International) to their hands before a common physical examination (abdominal, cardiorespiratory, hip and knee) both with and without ETP. After each exam, UV light was shined on the exam table and photographs were taken. The number of hand touches on ETP-covered areas and uncovered areas were tallied and compared using t tests. Despite covering more surface area, participants touched areas without ETP significantly more than ETP-covered areas (P <.05). Despite its continued use, patients do not have much hand contact with ETP during common clinical examinations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil Sciences & Agricultural Engineering is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article reports on the first record of wilt and gummosis disease in subabul trees caused by Fusarium equiseti in India. Subabul trees are economically important for the paper and pulp industry in India. The affected trees exhibited symptoms of yellowing, wilting, and gummosis. Isolates of Fusarium equiseti were obtained from the diseased tissue and were found to be identical in their genetic sequences. Pathogenicity tests confirmed that the Fusarium isolates caused similar symptoms in healthy subabul plants. The rapid spread of this disease poses a threat to subabul plantations, and the development of management strategies is necessary. [Extracted from the article]

Under the dual pressure of energy consumption and environmental pollution, mankind hopes to develop clean and renewable alternative energy, and the rapid development of fuel cells meets people's demand for energy-efficient power systems. The emergence of portable micro energy systems represented by microfluidic fuel cells, such as paper-based microfluidic fuel cells, has greatly enriched the means of medical detection to better cope with the threat of disease transmission. In this work, the numerical simulation method is innovatively introduced to study the paper-based microfluidic fuel cells. Both transient and steady-state modes are employed to demonstrate the whole operation process of the paper-based microfluidic fuel cell. In addition, the different structural parameters, including electrode spacing, the distance between electrode and inlet, channel thickness, and electrode length, are also investigated their influence mechanisms on cell performance. Results show that the increase of most structural parameters decreases cell output power in different degrees. Even on the premise that increasing channel thickness has a positive impact on the output power, the fuel utilization still shows a downward trend. These conclusions provide theoretical support and reference for future optimization work and accelerate the development of microfluidic fuel cells. • Numerical simulation is employed to study paper-based microfluidic fuel cell. • The effects of structure parameters on cell performance are discussed. • Through fuel concentration distribution to reveal cell performance mechanism. • Fuel utilization is used to evaluate energy efficiency. [ABSTRACT FROM AUTHOR]

Background: Access to correct information can influence the formation of the right attitude towards mitigating the spread of the COVID-19. Many individuals in Nigeria have taken up non-scientifically approved practices in a bid to protect themselves from the virus. The aim of this study was to assess the source of information, and knowledge of the cause and mode of transmission of the COIVD-19 among local traders in Ibadan, Oyo State, Nigeria. Methods: A cross-sectional study of local traders from two densely populated markets in Ibadan was conducted using an interviewer-administered semi-structured questionnaire. Data were analyzed using SPSS software v. 25 and Microsoft Excel 2016 and were presented in frequency tables and charts. The Chi-square test was used to test the associations. The level of significance was set at P<0.05. Results: A total of 321 traders (27.4% were males and 72.6% were females) were recruited with a Mean±SD age of 39.4±11.8 years. Radio (93.5%) and television (75.7%) were the most commonly used sources of information, 65.8% believed that COVID-19 was a result of divine punishment for sins and the majority (95.3%) reported that COVID-19 was spread through personal contact with infected persons. There was a statistically significant association between knowledge of cause and age (P=0.004), as well as between knowledge of cause and marital status (P=0.001). Additionally, a statistically significant association was observed between knowledge of transmission and level of education (P=0.012). Conclusion: Even though they have access to adequate information, people may not take proper actions to protect themselves and others from contracting the COVID-19. Reinforced health promotion strategies that pay attention to the local contexts and perspectives of community members should be taken across the board. Background: Access to correct information can influence the formation of the right attitude towards mitigating the spread of the COVID-19. Many individuals in Nigeria have taken up non-scientifically approved practices in a bid to protect themselves from the virus. The aim of this study was to assess the source of information, and knowledge of the cause and mode of transmission of the COIVD-19 among local traders in Ibadan, Oyo State, Nigeria. Methods: A cross-sectional study of local traders from two densely populated markets in Ibadan was conducted using an interviewer-administered semi-structured questionnaire. Data were analyzed using SPSS software v. 25 and Microsoft Excel 2016 and were presented in frequency tables and charts. The Chi-square test was used to test the associations. The level of significance was set at P<0.05. Results: A total of 321 traders (27.4% were males and 72.6% were females) were recruited with a Mean±SD age of 39.4±11.8 years. Radio (93.5%) and television (75.7%) were the most commonly used sources of information, 65.8% believed that COVID-19 was a result of divine punishment for sins and the majority (95.3%) reported that COVID-19 was spread through personal contact with infected persons. There was a statistically significant association between knowledge of cause and age (P=0.004), as well as between knowledge of cause and marital status (P=0.001). Additionally, a statistically significant association was observed between knowledge of transmission and level of education (P=0.012). Conclusion: Even though they have access to adequate information, people may not take proper actions to protect themselves and others from contracting the COVID-19. Reinforced health promotion strategies that pay attention to the local contexts and perspectives of community members should be taken across the board. [ABSTRACT FROM AUTHOR]

Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. An aviation network is a typical complex network, and accurately identifying the key city nodes in the aviation network can help us better prevent network attacks and control the spread of diseases. In this paper, a method for identifying key nodes in undirected weighted networks, called weighted Laplacian energy centrality, was proposed and applied to an aviation network constructed from real flight data. Based on the analysis of the topological structure of the network, the paper recognized critical cities in this network, then simulation experiments were conducted on key city nodes from the perspectives of network dynamics and robustness. The results indicated that, compared with other methods, weighted Laplacian energy centrality can identify the city nodes with the most spreading influence in the network. From the perspective of network robustness, the identified key nodes also have the characteristics of accurately and quickly destroying network robustness. [ABSTRACT FROM AUTHOR]

Objective: To end the AIDS epidemic by 2030, despite the increasing poverty and inequalities, policies should be designed to deal with population heterogeneity and environmental changes. Bottom-up designs, such as the Agent-Based Model (ABM), can model these features, dealing with such complexity. HIV/AIDS has a complex dynamic of structural factors, risk behaviors, biomedical characteristics and interventions. All embedded in unequal, stigmatized and heterogeneous social structure. To understand how ABMs can model this complexity, we performed a scoping review of HIV applications, highlighting their potentialities. Methods: We searched on PubMed, Web of Science, and Scopus repositories following the PRISMA extension for scoping reviews. Our inclusion criteria were HIV/AIDS studies with an ABM application. We identified the main articles using a local co-citation analysis and categorized the overall literature aims, (sub)populations, regions, and if the papers declared the use of ODD protocol and limitations. Results: We found 154 articles. We identified eleven main papers, and discussed them using the overall category results. Most studies model Transmission Dynamics (37/154), about Men who have sex with Men (MSM) (41/154), or individuals living in the US or South Africa (84/154). Recent studies applied ABM to model PrEP interventions (17/154) and Racial Disparities (12/154). Only six papers declared the use of ODD Protocol (6/154), and 34/154 didn't mention the study limitations. Conclusions: While ABM is among the most sophisticated techniques available to model HIV/AIDS complexity. Their applications are still restricted to some realities. However, researchers are challenged to think about social structure due model characteristics, the inclusion of these features is still restricted to case-specific. Data and computational power availability can enhance this feature providing insightful results. [ABSTRACT FROM AUTHOR]

This paper examines Russia's propaganda discourse on Twitter during the 2022 invasion of Ukraine. The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to identify key communities and narratives associated with the prevalent and damaging narrative of "fascism/Nazism" in discussions related to the invasion. The paper implements a methodological pipeline to identify the main topics, and influential actors, as well as to examine the most impactful messages in spreading this disinformation narrative. Overall, this research contributes to the understanding of propaganda dissemination on social media platforms and provides insights into the narratives and communities involved in spreading disinformation during the invasion. [ABSTRACT FROM AUTHOR]

Harvesting usable and meaningful disaster-related, spatio-temporal data at a highly granular level poses major challenges in its cleaning and aggregation. This paper presents a strategy related to those challenges with respect to individual behavior near COVID-19 laden healthcare facilities. This is done to enable the visualizing of egress behavior data as interactive, three-dimensional (3D) scenes to investigate human behavior patterns regarding touch-based, disease transmission. Therefore, the aim is to demonstrate how this concept of 3D epidemiology may provide new mechanisms to understand the relative risk and exposure prevalence for data analysis. This paper demonstrates 3D enablement of disaster-related field data through use of first-hand observations of 1,936 individuals egressing New York City healthcare facilities during the onset of COVID-19 in the Spring of 2020. The observations capture egress behavior in terms of where people go (e.g. coffee shop, Subway) and how they physically interact with the surroundings (i.e. what they touch and how long they remain). This paper introduces a mechanism for automated extraction and 3D visualization of such data in Potree, an open-source Web Graphics Library (WebGL) point cloud viewer. Distinctive vertex shaders are used to distinguish specific destination selection and behavioral patterns (e.g. personal protective equipment usage). Two-dimensional heatmaps are paired with 3D scenes to demonstrate the potential of using 3D visualization of spatio-temporal patterns for visualizing disease transmission potential. [ABSTRACT FROM AUTHOR]

Background: A larger percentage of social housing tenants have poorer physical and mental health outcomes compared to private renters and homeowners. They are also at a greater risk of respiratory conditions, cardiovascular disease, communicable disease transmission and mortality. One approach that aims to reduce health inequalities is to create research partnerships with underserved local communities. Our primary aim was to develop a research partnership with social housing tenants in Nottingham and our secondary aim was to explore the health priorities of these social housing tenants to inform future research applications. We also hope to provide a descriptive process of PPI within a social housing context for other researchers to learn from. Methods: We used Public and Patient Involvement (PPI) as the foundation of this work, as we believed that people with lived experience of social housing, also end-users of the research, were best placed to inform us of the areas with the greatest research need. Through online and in-person focus groups, we discussed with tenants, collectively named a Social Advisory Group (SAG), their health concerns and priorities. Together they raised 26 health issues, which were combined with 22 funding opportunity themes being offered by the NIHR (National Institute for Health and Care Research). This was with the purpose of investigating whether there was alignment between the health needs of Nottingham's social housing tenants and the NIHR's research priorities. A prioritisation technique (Diamond Nine) was used to sort in total, 48 areas of health and wellbeing, into three top priorities. Tenants were provided the opportunity to be involved in public health research in other ways too, such as reviewing this paper and also an NIHR Programme Development Grant application to expand and continue this work. One was also offered the opportunity to be a public co-applicant. Results: The group prioritised improvements in the quality of social housing, mental health and healthcare services. There was only some alignment between these and the NIHR funding themes. Other factors, such as age and race, also determined individual health priorities.. The diversity and reach of the current project were limited, however this is something we hope to improve in the future with more funding. We learned that tenants have varying degrees of mobility and technological abilities, requiring both online and in-person meetings. Plain English summary: Social housing is offered to people who cannot afford to buy or rent in the open market, and a larger percentage of social housing tenants have poorer physical and mental health outcomes compared to the general population. One approach that aims to reduce health inequalities is to create sustainable research partnerships with underserved local communities. Our primary aim was to involve social housing tenants in public health research, as they are best placed to tell us the type of research they would benefit from. The secondary aim was to explore the health priorities of social housing tenants to inform future research applications. We also hope to describe the process of PPI within a social housing context for other researchers to learn from. To achieve these aims, we established a research partnership with a group of social housing tenants in Nottingham and spoke to them about the areas of their health they wanted to improve (i.e., their priorities). The topics that were discussed the most were the need for improved mental health, quality of social housing and healthcare services, however this varied between individuals according to race and age. We learned several things throughout this process. Firstly, the combination of mobility and technological abilities amongst tenants meant that meetings must be held both in-person and online. This ensured they remained accessible and convenient. Secondly, we learnt that in-person meetings should be held in a neutral space to encourage different members of the group to attend. Finally, in general, people were very enthusiastic about this partnership and were committed to seeing improvements in public health. We therefore provided more opportunities for the group to be involved in research. For example, they were offered the opportunity to write and edit a lay summary for a future research application, which was based on the priorities identified in this paper. One member of the group was nominated to be the public co-applicant , which would allow us to increase the reach of this housing work across the East Midlands. It would also allow us to increase the diversity of the group, as currently it is made up of mostly retired females of British origin. Involving the public in health research has been central to this process and continues to be important in the production of accessible and relevant research. [ABSTRACT FROM AUTHOR]

Cholera, as an endemic disease around the world, has imposed great harmful effects on human health. In addition, from a microscopic viewpoint, the interference of random factors exists in the process of virus replication. However, there are few theoretical studies of viral infection models with biologically reasonable stochastic effects. This paper studied a stochastic cholera model used to describe transmission dynamics in China. In this paper, we adopted a special method to simulate the effect of environmental perturbations to the system instead of using linear functions of white noise, i.e., the transmission rate of environment to human was satisfied Ornstein-Uhlenbeck processes, which is a more practical and interesting. First, it was theoretically proved that the solution to the stochastic model is unique and global, with an ergodic stationary distribution. Moreover, by solving the corresponding Fokker-Planck equation and using our developed algebraic equation theory, we obtain the exact expression of probability density function around the quasi-equilibrium of the stochastic model. Finally, several numerical simulations are provided to confirm our analytical results. [ABSTRACT FROM AUTHOR]

Background: As the global community begins recovering from the COVID-19 pandemic, the challenges due to its aftermath remain. This health crisis has highlighted challenges associated with airborne pathogens and their capacity for rapid transmission. While many solutions have emerged to tackle this challenge, very few devices exist that are inexpensive, easy to manufacture, and versatile enough for various settings. Methods: This paper presents a novel suction device designed to counteract the spread of aerosols and droplets and be cost-effective and adaptable to diverse environments. We also conducted an experimental study to evaluate the device's effectiveness using an artificial cough generator, a particle counter, and a mannequin in an isolated system. We measured droplet removal rates with simulated single and repeated cough incidents. Also, measurements were taken at four distinct areas to compare its effectiveness on direct plume versus indirect particle removal. Results: The device reduced airborne disease transmission risk, as evidenced by its capacity to decrease the half-life of aerosol volume from 23.6 minutes to 15.6 minutes, effectively capturing aerosol-sized droplets known for their extended airborne persistence. The suction device lessened the peak total droplet volume from peak counts. At 22 minutes post peak droplet count, the count had dropped 24% without the suction device and 43% with the suction device. Conclusions: The experiment's findings confirm the suction device's capability to effectively remove droplets from the environment, making it a vital tool in enhancing indoor air quality. Given the sustained performance of the suction device irrespective of single or multiple cough events, this demonstrates its potential utility in reducing the risk of airborne disease transmission. 3D printing for fabrication opens the possibility of a rapid iterative design process, flexibility for different configurations, and rapid global deployment for future pandemics. [ABSTRACT FROM AUTHOR]

The occurrence and spread of infectious diseases pose considerable challenges to public health. While the relationship between the built environment and the spread of infectious diseases is well-documented, there is a dearth of urban planning tools specifically designed for conducting Health Impact Assessments (HIAs) targeted at infectious diseases. To bridge this gap, this paper develops a comprehensive framework of an HIA for Urban Planning and Epidemic (HIA4UPE), formulated by considering the progression of public health incidents and the distinct transmission patterns of infectious diseases. This framework is designed to provide a comprehensive assessment by including a health risk-overlay assessment, health resource-quality assessment, health resource-equality assessment, and health outcome-impact prediction, enabling a multidimensional evaluation of the potential impacts of current environmental conditions or planning proposals on the incidence of infectious diseases. Furthermore, this paper advances the application of spatial analysis and computation, comprehensive assessment methodologies, and predictive analytics to conduct specific assessments. The theoretical framework and analytical tools presented in this paper contribute to the academic discourse and offer practical utility in urban planning and policymaking on epidemic prevention and control. [ABSTRACT FROM AUTHOR]

This paper focuses on the discovery of unusual spatiotemporal associations across multiple phenomena from distinct application domains in a spatial neighborhood where each phenomenon is represented by anomalies from the domain. Such an approach can facilitate the discovery of interesting links between distinct domains, such as links between traffic accidents and environmental factors or road conditions, environmental impacts and human factors, disease spread, and hydrological trajectory, to name a few. This paper proposes techniques to discover spatiotemporal associations across distinct phenomena using a series of anomalous windows from each domain that represent a phenomenon. We propose a novel metric called influence score to quantify the associated influence between the phenomena. In addition, we also propose spatiotemporal confidence, support, and lift measures to quantify these associations. Two novel algorithms for finding multi-domain spatiotemporal associations across phenomena are proposed. We present experimental results across real-world phenomena that are linked and discuss the efficacy of our approach. [ABSTRACT FROM AUTHOR]

This paper estimates the state transition of COVID-19 positive cases by analyzing the data about confirmed positive cases in Tokyo, Japan. The prediction of the number of newly infected persons is one of the active research topics for the COVID-19 pandemic. Although such a prediction is important for recognizing the future risk of spreading infectious diseases, understanding the state transition after they are confirmed to be positive is also important for estimating the number of required ICUs, hotel rooms for isolation, etc. This paper classifies the state after being positive into "in hotel/home for isolation," "in hospital with a mild state," "in hospital with a severe state," "recovered," and "dead" and estimates the transition probabilities among those states from the data about confirmed positive cases in Tokyo, Japan. This paper shows the parameters estimated from different periods and discusses the difference considering the pandemic situation. An agent simulation using the estimated transition probabilities as its parameters is also proposed. The result of the simulation from August to November 2020 shows the predicted number of agents is close to the actual data. As one of the possible applications to the proposed agent simulation, this paper shows the simulation result from December 2020 to January 2021 under a hypothetical situation. [ABSTRACT FROM AUTHOR]

Background: During dental procedures, critical parameters, such as cooling condition, speed of the rotary dental turbine (handpiece), and distance and angle from pollution sources, were evaluated for transmission risk of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), simulated by spiking in a plasmid encoding a modified viral spike protein, HexaPro (S6P), in droplets and aerosols. Methods: To simulate routine operation in dental clinics, dental procedures were conducted on a dental manikin within a digital dental unit, incorporating different dental handpiece speeds and cooling conditions. The tooth model was immersed in Coomassie brilliant blue dye and was pre-coated with 100 μL water spiked-in with S6P-encoding plasmid. Furthermore, the manikin was surrounded by filter papers and Petri dishes positioned at different distances and angles. Subsequently, the filter papers and Petri dishes were collected to evaluate the aerosol splash points and the viral load of S6P-encoding plasmid in aerosols and splatters generated during the dental procedure. Results: Aerosol splashing generated a localized pollution area extended up to 60 cm, with heightened contamination risks concentrated within a 30 cm radius. Significant differences in aerosol splash points and viral load by different turbine handpiece speeds under any cooling condition (P < 0.05) were detected. The highest level of aerosol splash points and viral load were observed when the handpiece speed was set at 40,000 rpm. Conversely, the lowest level of aerosol splash point and viral load were found at a handpiece speed of 10,000 rpm. Moreover, the aerosol splash points with higher viral load were more prominent in the positions of the operator and assistant compared to other positions. Additionally, the position of the operator exhibited the highest viral load among all positions. Conclusions: To minimize the spread of aerosol and virus in clinics, dentists are supposed to adopt the minimal viable speed of a dental handpiece with limited cooling water during dental procedures. In addition, comprehensive personal protective equipment is necessary for both dental providers and dental assistants. [ABSTRACT FROM AUTHOR]

Zoonotic diseases represent a significant societal challenge in terms of their health and economic impacts. One Health approaches to managing zoonotic diseases are becoming more prevalent, but require novel thinking, tools and cross-disciplinary collaboration. Bovine tuberculosis (bTB) is one example of a costly One Health challenge with a complex epidemiology involving humans, domestic animals, wildlife and environmental factors, which require sophisticated collaborative approaches. We undertook a scoping review of multi-host bTB epidemiology to identify trends in species publication focus, methodologies, and One Health approaches. We aimed to identify knowledge gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. The review included 532 articles. We found different levels of research attention across episystems, with a significant proportion of the literature focusing on the badger-cattle-TB episystem, with far less attention given to tropical multi-host episystems. We found a limited number of studies focusing on management solutions and their efficacy, with very few studies looking at modelling exit strategies. Only a small number of studies looked at the effect of human disturbances on the spread of bTB involving wildlife hosts. Most of the studies we reviewed focused on the effect of badger vaccination and culling on bTB dynamics with few looking at how roads, human perturbations and habitat change may affect wildlife movement and disease spread. Finally, we observed a lack of studies considering the effect of weather variables on bTB spread, which is particularly relevant when studying zoonoses under climate change scenarios. Significant technological and methodological advances have been applied to bTB episystems, providing explicit insights into its spread and maintenance across populations. We identified a prominent bias towards certain species and locations. Generating more high-quality empirical data on wildlife host distribution and abundance, high-resolution individual behaviours and greater use of mathematical models and simulations are key areas for future research. Integrating data sources across disciplines, and a "virtuous cycle" of well-designed empirical data collection linked with mathematical and simulation modelling could provide additional gains for policy-makers and managers, enabling optimised bTB management with broader insights for other zoonoses. [ABSTRACT FROM AUTHOR]

Background: The prolonged presence of Syrian refugees in Jordan has highlighted the need for sustainable health service delivery models for refugees. In 2012, the Jordanian government adopted a policy that granted Syrian refugees access, free of charge, to the national health system. However since 2012, successive policy revisions have limited refugee access. This paper seeks to understand factors that initially put refugee integration into the health system on the policy agenda, as well as how these same factors later affected commitment to sustain the policy. Methods: This paper draws on data from a document review of 197 peer-reviewed and grey literature publications, a media analysis of newspaper articles retrieved from four officially recognized newspapers in Jordan, and 33 semi-structured key informant interviews. We used Kingdon's Multiple Streams Model – a well-established tool for analyzing policy adoption – to understand how political priority developed for integration of refugees into the health system. Results: We find that several factors helped bring attention to the issue, namely concerns over infectious disease transmission to host communities, high rates of chronic conditions among the refugee population and the increasingly urban and dispersed nature of refugees. At the outset of the conflict, the national mood was receptive to refugees. Politicians and government officials quickly recognized the crisis as an opportunity to secure material and technical support from the international humanitarian community. At the same time, global pressures for integrating refugees into national health systems helped move the integration agenda forward in Jordan and the region more broadly. Since 2012, there were several modifications to the policy that signal profound changes in national views around the continued presence of Syrian refugees in the country, as well as reduced external financial support which has undermined the sustainability of the policy. Conclusion: This case study underscores the dynamic nature of policymaking and the challenge of sustaining government commitment to the right to health among refugees. Our analysis has important implications for advocates seeking to advance and maintain momentum for the integration of refugees into national health systems. [ABSTRACT FROM AUTHOR]

Background: There is an increase in number of Human Papillomavirus related head and neck squamous cell carcinoma (HPV-related HNSCC) High risk HPV(HR-HPV) types can be cleared by an infected person, however, some can persist and develop HN cancer. There is a broad knowledge gap regarding HPV and related cancers. Main text: The aim of this review is to assess existing published knowledge on oral-genital HPV transmission, concordance of HPV genotypes and risk of oral or/and genital lesions among spouses/partners of patients diagnosed with HPV-related HNSCC, identify gaps in the current research and highlight areas that requires further inquiry. Method: Database like Pub med, Google Scholar, Scopus, Puplon, Wiley online library were used for search strategy. Published papers on transmission, concordance of HPV genotypes and genital lesions among spouses/partners of patients diagnosed with HPV-related HNSCC were included. Papers published from January1,2000 to October 31, 2022 were included. The published papers included are 8 Case reports, 2 cross-sectional studies, 3 Cohort studies and 2 systematic reviews. Results: A total of 2125 citations were retrieved from the five sources. 15papers were included. Case reports reported concurrent HPV-related oropharyngeal, tonsillar, unspecified HNSCC, laryngeal and nasopharyngeal carcinoma among couples. The two cross-sectional studies were done. Almost all the tumors taken from patients with HPV-related oropharyngeal carcinoma (HPV-related OPC) and their spouses were positive for identical HPV 16 type. The three cohort studies showed an increase risk of upper aero-digestive tract cancer among male spouses of females with cervical cancer. Two systematic reviews reviewed literature studies which evaluated concurrent cases of HPV-related Oropharyngeal cancers. Examination of these papers showed that the majority of the studies suggested that there is HPV transmission, concordance and risk of HNSCC cancer among spouses with HPV-related oral-genital cancer. No studies evaluated the risk of developing genital cancer in spouses of patients with HNSCC. Conclusion: The findings of this review highlighted big need of further research on oral-genital HPV infection among spouses of patients diagnosed with HPV-related HNSCC. Studies are needed to evaluate the risk of getting genital and upper aero-digestive tract HPV-related cancer among spouses with HPV-related HNC. [ABSTRACT FROM AUTHOR]

The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data. [ABSTRACT FROM AUTHOR]

The development of antimicrobial polymeric materials has evolved into one of the more promising methods for preventing the growth of microbes and mitigating the spread of infectious diseases in several applications including the health and food packaging sectors. The outbreak of global pandemics, and particularly the recent COVID-19 pandemic, further strengthen the importance of developing such solutions. This review paper presents a fundamental understanding of how antimicrobial polymers are developed, describes the possible surface modification approaches to render polymers with antimicrobial properties, highlights the potential mechanism of action against a range of microorganisms (bacterial, viral, and fungal), and details some of the international standard protocols and procedures to evaluate the antimicrobial properties of modified materials (such as plastics and textiles). In addition, this review paper discusses the toxicity of antimicrobial additives when used in healthcare and food packaging applications. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has required countries to implement different containment strategies to limit its spread, like strict or weakened national lockdown rules and the application of age-stratified vaccine prioritization strategies. These interventions have in turn modified the age-dependent patterns of social contacts. In our recent paper, starting from the available age-structured real data at the national level, we identified, for the Italian case, specific virulence parameters for a two-age-structured COVID-19 epidemic compartmental model (under 60, and 60 years and over) in six different diseases transmission scenarios under concurrently adopted feedback interventions. An interpretation of how each external scenario modifies the age-dependent patterns of social contacts and the spread of COVID-19 disease has been accordingly provided. In this paper, which can be viewed as a sequel to the previous one, we mainly apply the same general methodology therein (involving the same dynamic model) to new data covering the three subsequent additional scenarios: (i) a mitigated coordinated intermittent regional action in conjunction with the II vaccination phase; (ii) a super-attenuated coordinated intermittent regional action in conjunction with the II vaccination phase; and (iii) a last step towards normality in conjunction with the start of the III vaccination phase. As a new contribution, we show how meaningful updated information can be drawn out, once the identification of virulence parameters, characterizing the two age groups within the latest three different phases, is successfully carried out. Nevertheless, differently from our previous paper, the global optimization procedure is carried out here with the number of susceptible individuals in each scenario being left free to change, to account for reinfection and immunity due to vaccination. Not only do the slightly different estimates we obtain for the previous scenarios not impact any of the previous considerations (and thus illustrate the robustness of the procedure), but also, and mainly, the new results provide a meaningful picture of the evolution of social behaviors, along with the goodness of strategic interventions. [ABSTRACT FROM AUTHOR]

In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures. [ABSTRACT FROM AUTHOR]

Asymmetry plays a significant role in the transmission dynamics in novel discrete fractional calculus. Few studies have mathematically modeled such asymmetry properties, and none have developed discrete models that incorporate different symmetry developmental stages. This paper introduces a Taylor monomial falling function and presents some properties of this function in a delta fractional model with Green's function kernel. In the deterministic case, Green's function will be non-negative, and this shows that the function has an upper bound for its maximum point. More precisely, in this paper, based on the properties of the Taylor monomial falling function, we investigate Lyapunov-type inequalities for a delta fractional boundary value problem of Riemann–Liouville type. [ABSTRACT FROM AUTHOR]

The automation of hand hygiene monitoring is critical in healthcare for ensuring clean hands and preventing infectious disease spread. While advancements have been made, existing methods have limitations in accurately detecting and classifying handwashing actions. This paper addresses these limitations and introduces the Residual-Based Multilevel Fused Network (ResMFuse-Net) as a novel approach to automate the quality assurance of hand hygiene procedures. Our model integrates advanced techniques, including feature fusion, model compression, a feature fusion block (FFB), and a modified separable residual block (SE-ResB). The proposed model fused two networks into one trainable feature extraction pipeline, and applies model compression to retain the core blocks that are crucial for propagating strong and robust features while conserving a significant fraction of the computing resources. Additionally, we introduce a FFB that includes ConvLSTM and alpha dropout to learn spatial dependencies, establish correlations between frames in a video, and mitigate overfitting. This paper introduces a SE-ResB, which is a customized residual component composed of separable convolutions and LeakyReLU activation. The SE-ResB is incorporated to handle the fused features and generate a more diverse set of features, leading to considerable performance enhancements. This study also includes an ablation analysis that highlights the importance of each component. The proposed ResMFuse-Net is evaluated on two datasets: a newly created handwashing dataset (451 videos) and a publicly available dataset (656 videos). Achieving a recognition accuracy of 97.61% on the handwashing dataset and 98.69% on the other dataset, the ResMFuse-Net outperforms previous methods with fewer parameters and FLOPs, demonstrating its efficiency and cost-effectiveness. [ABSTRACT FROM AUTHOR]

This paper studies a delayed rumor propagation model with logistic growth and saturation incidence. The next generation matrix method, some inequality techniques, the Lyapunov-LaSalle invariance principle, and the Lyapunov method are used in this paper. Our results indicate that if the basic regeneration number (which is analogous to the basic reproduction number in disease transmission models) is less than 1, the rumor-free equilibrium point (which is analogous to the disease-free equilibrium point in disease transmission models) is globally stable. If the basic regeneration number is greater than 1, then the rumor is permanent, and some sufficient conditions are obtained for local and global asymptotic stability of the rumor prevailing equilibrium point (which is analogous to the endemic equilibrium point in disease transmission models). Finally, three examples with numerical simulations are presented to illustrate the obtained theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents the study of the effectiveness of horizontal transfer of local isolates of the pathogenic fungus Beauveria bassiana (Balsamo) on adults of olive fruit fly Bactrocera oleae (Rossi) at a concentration of 106 spores/ml in laboratory conditions (this work was carried out in specialized scientific laboratories). In addition, it is not possible to reach the desired results in such experiments effectively when the data and observations of the study are not clear and accurate. For this reason, in this paper, experimental data will be presented with inaccurate or uncertain observations using neutrosophic statistics. The purpose is to know the success of males contaminated with pathogenic isolates in the transmission of infection to females. In laboratory conditions through a neutrosophic reading of the study data. This proposed presentation provides greater accuracy, flexibility, and applicability than the classic experimental design in the case of uncertainty. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a new type of planetary traction drive bearing-type reducer. In this design, the transmission outer ring is replaced with an elastic ring. The design constructs a circular arc at the axial end of the rolling body's contour line. This ensures that the contact point of this arc with the reducer's outer ring and the inner ring's axial end face is maintained on the radial traction contact line. As a result, it can replace the thrust bearing and provide an axial support function. It has the advantages of simple structure, easy processing, smooth transmission, and low noise. This paper first introduces the design and development process of this bearing-type reducer and presents systematic research on its transmission principle and dynamics. Subsequently, in response to the edge effect phenomenon of the outer ring contact line, the contour line of the outer ring is refined by adopting the shaping method used for bearing rollers, establishing a full circular arc profile shaping method, which significantly improves its edge effect. Finally, in our investigations, combined with experimental tests, a prototype of the bearing-type reducer was fabricated, and the speed ratio, torque, and transmission efficiency of the reducer were studied. The results demonstrate that the bearing-type reducer can achieve high transmission accuracy and efficiency. The transmission performance varies significantly under different lubrication conditions, with the peak efficiency reaching as high as 99.97% when using Santotrac 50 traction oil. The results verify the feasibility of the proposed design method and have the potential to be applied in wheel hub motors and robot joints. [ABSTRACT FROM AUTHOR]

In the first part of this paper, we review old and new results about the influence of host population heterogeneity on (various characteristics of) epidemic outbreaks. In the second part we highlight a modelling issue that so far has received little attention: how do contact patterns, and hence transmission opportunities, depend on the size and the composition of the host population? Without any claim on completeness, we offer a range of potential (quasi-mechanistic) submodels. The overall aim of the paper is to describe the state-of-the-art and to catalyse new work. [ABSTRACT FROM AUTHOR]

Due to the pandemic era, many courses in higher education were conducted online to minimize the risk of disease transmission. For several engineering courses that contain practical material, an appropriate method is required to deliver practicum learning materials online to maintain the quality of these courses. Therefore, in this paper, the implementation of an economical IoT-based (Internet of Things) liquid tank control system was introduced to support the online practicum class. The tank system design has a height of 120 cm and a diameter of 50 cm. The tank features a valve, flowmeter, and an 18 LPM water pump. The control systems, consisting of Arduino Uno, an ultrasonic sensor, and a stepper motor, was run on the MATLAB Simulink application. IoT systems can be deployed using the TeamViewer app. After running the simulation, it was found that the lower setpoint change did not make significant changes in the process and produced the same curve shape as the steady state. The curves at steady state and during processing are not much different for the disturbance change. The error can be calculated using IAE and ITAE. The performances of the control system with IAE and ITAE values are 1.460 and 915.122 for setpoint change, respectively. For disturbance change, the IAE and ITAE values are 0.877 and 490.446, respectively. This system can help students better understand the industry 4.0 concept and allow them do practical work effectively from home at a low cost. [ABSTRACT FROM AUTHOR]

This paper is concerned with a reaction-diffusion SIS (susceptible-infected-susceptible) epidemic model governed by a saturated incidence function and linear birth-death growth in advective environments. The related model without birth and death case has been studied in our previous work [8]. In this paper, our main purpose is to investigate the combined effects of varying total population, saturation infection mechanism and spatial heterogeneity on the transmission dynamics and spatial distribution of disease. The extinction and persistence of the infectious disease in terms of the basic reproduction number are established. We discuss the global attractivity of the equilibria in two special cases and explore the asymptotic profiles of the endemic equilibrium with respect to the dispersal and advection rates. Compared with the results for the model without birth and death in [8] , our findings indicate that the linear birth-death growth can enhance the persistence of an infectious disease and may provide some prospective applications in disease prevention and control. [ABSTRACT FROM AUTHOR]

Background: Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in selfprotective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known. Methods: This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19. Results: Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% compared to the counterfactual scenario. Conclusion: Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives. [ABSTRACT FROM AUTHOR]

Background: This paper investigates the consequences of the COVID-19 pandemic on mortality and hospitalization among nursing home residents in Norway. While existing evidence shows that nursing home residents were overrepresented among COVID-19-related deaths, suggesting inadequate protection measures, this study argues that the observed overrepresentation in mortality and hospitalization may partly stem from the inherent frailty of this demographic. Using nationwide administrative data, we assessed excess deaths and hospitalization by comparing pandemic-era rates to those of a pre-pandemic cohort. Methods: We compared mortality and hospitalization rates between a pandemic cohort of nursing home residents as of September 2019 (N = 30,052), and a pre-pandemic cohort as of September 2017 (N = 30,429). Both cohorts were followed monthly for two years, beginning in September 2019 and 2017, respectively. This analysis was conducted at the national level and separately for nursing home residents in areas with low, medium, and high SARS-CoV-2 community transmission. Event studies and difference-in-difference models allowed us to separate the impact of the pandemic on mortality and hospitalization from secular and seasonal changes. Results: The pandemic cohort experienced a non-significant 0.07 percentage points (95% confidence interval (CI): − 0.081 to 0.221) increase in all-cause mortality during the 18 months following pandemic onset, compared to the pre-pandemic cohort. Moreover, our findings indicate a substantial reduction in hospitalizations of 0.27 percentage points (95% CI: − 0.464 to − 0.135) and a non-significant decrease of 0.80 percentage points (95% CI: − 2.529 to 0.929) in the proportion of nursing home residents hospitalized before death. The effect on mortality remained consistent across regions with both high and low levels of SARS-CoV-2 community transmission. Conclusions: Our findings indicate no clear evidence of excess all-cause mortality in Norway during the pandemic, neither nationally nor in areas with high infection rates. This suggests that early implementation of nationwide and nursing home-specific infection control measures during the pandemic effectively protected nursing home residents. Furthermore, our results revealed a decrease in hospitalizations, both overall and prior to death, suggesting that nursing homes adhered to national guidelines promoting on-site treatment for residents. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model's application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases. [ABSTRACT FROM AUTHOR]

In this paper, we have delved into the intricate dynamics of a discrete-time Hepatitis B virus (HBV) model, shedding light on its local dynamics, topological classifications at equilibrium states, and pivotal epidemiological parameters such as the basic reproduction number. Our analysis extended to exploring convergence rates, control strategies, and bifurcation phenomena crucial for understanding the behavior of the HBV system. Employing linear stability theory, we meticulously examined the local dynamics of the HBV model, discerning various equilibrium states and their topological classifications. Subsequently, we identified bifurcation sets at these equilibrium points, providing insights into the system’s stability and potential transitions. Further, through the lens of bifurcation theory, we conducted a comprehensive bifurcation analysis, unraveling the intricate interplay of parameters that govern the HBV model’s behavior. Our investigation extended beyond traditional stability analysis to explore chaos and convergence rates, enriching our understanding of the dynamics of the understudied HBV model. Finally, we validated our theoretical findings through numerical simulations, confirming the robustness and applicability of our analysis in real-world scenarios. By integrating biological and epidemiological insights into our mathematical framework, we offered a holistic understanding of the dynamics of HBV transmission dynamics, with implications for public health interventions and disease control strategies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to disrupt the teaching and learning in international higher education. Those of us in the global South have particularly been hard hit, struggling to balance working/ functioning economies, a struggling healthcare system, education, commerce, trade, transport, and the community spread of what was later found to be a very infectious disease (Porter et al., 2021; Rogerson & Rogerson, 2020; Shamasunder et al., 2020). In this paper, we explored and theorised the experiences of academics who taught large classes at a research-intensive university in South Africa. We purposely recruited and interviewed eight academics for this case study. We drew on Chela Sandoval’s (2013) philosophical notion of “decolonial love” to theorise what an inclusive, democratic and ubuntu-orientated teaching of large classes could look like for us in the global South, beyond the pandemic. The findings revealed that academics continue to be frustrated/challenged/made anxious with teaching large classes due to inadequate infrastructure (digital), lack of resources, and general unpreparedness with the virtual/online teaching and learning. The findings also revealed that large classes were problematic as academics struggled to provide critical engagements and discussions during the hard Covid-19 lockdown, and with some lamenting the frustrations of “teaching to themselves” due to the lack of student engagement. We conclude this paper by proposing a decolonial love approach to the online teaching and learning of large classes, underpinned by the ethics of care, compassion and understanding in curriculum imaginations. [ABSTRACT FROM AUTHOR]

Accurate prediction of infectious disease cases plays a crucial role in achieving effective infection prevention and control. However, the inherent variability of incubation periods and progression dynamics of infectious diseases pose significant challenges to the accuracy of predicting multiple diseases. Multiple representation fusion (MRF) methods would improve the performance of prediction models, due to their capability to capture diverse temporal dependencies that reflect potential disease transmission patterns. But the traditional fusion approach for infectious disease prediction still faces many challenges, including the requirement of auxiliary data, vulnerability to disease evolution, and lack of intuitive explanation. To address these challenges, this paper proposes an oriented transformer (ORIT) for infectious diseases case prediction. Contrary to traditional MRF structures that integrate representations from multiple data sources, the MRF in the proposed ORIT combines multi-orientation context vectors solely by capturing multi-dimensional temporal relationships within disease case data. Furthermore, this paper considers the heterogeneity of the incubation period in the prediction of different infectious disease cases. Lastly, this paper conducts comprehensive experiments to evaluate the proposed method using two real datasets of infectious diseases, and compares it with 21 well-known prediction methods. The experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As COVID-19 spread the whole way across the world, a significant number of us got mindful of how significant face covers are. Medical services authorities and nearby foundations from one side of the planet to the other are encouraging individuals to wear masks, as it is the best way to forestall the transmission of the infection. Masks have without a doubt frustrated the facial-acknowledgment industry; the innovation has likewise adjusted. It might sound odd yet wearing a cover does not really prevent a PC from recognizing somebody. We are intending to prepare our model to recognize whether the pictures are genuine or fake one even though individuals are wearing face cover. In this paper, we intend to make a liveness detector equipped for spotting counterfeit faces. To make a liveness detector, we will prepare a deep learning neural network fit for recognizing genuine versus counterfeit appearances. It deals with two correlative spaces: RGB space and multi-scale Retinex (MSR) space. The RGB space contains the point-by-point facial surfaces, yet it is sensitive to illumination whereas the MSR pictures can adequately catch the high recurrence data, which is discriminative for face recognition. [ABSTRACT FROM AUTHOR]

The availability of high-fidelity energy consumption estimates and the ability to evaluate drivetrain efficiency are crucial for effectively planning a large-scale transition to electric vehicles. For both new and retrofitted electric vehicles, a key question is the transmission type—single-speed or multi-speed—and the resulting impact on the vehicle's overall efficiency. This paper presents a comprehensive simulation-based methodology for evaluating the impact of transmission selection on vehicle efficiency using high-fidelity driving cycle data. The method can be used for new vehicles and retrofit applications where a transmission is already present. The efficiency of a single-speed reduction gearbox was compared to that of a five-speed multi-speed transmission in a retrofitted vehicle, of which the impact of the manual transmission on the vehicle dynamics and efficiency was examined. The manual transmission proved to be more efficient for a perfect gear-shifting strategy. [ABSTRACT FROM AUTHOR]

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With the advancement of technology and the arrival of the era of integrated media, big data, artificial intelligence, cloud computing, and virtual reality are gradually changing the ecology of public opinion. This will bring new challenges to the technical discovery, data analysis, and response of network disease dissemination to public opinion. Internet data barriers are still widespread. The specific requirements are to explore the application mode of blockchain technology to bring a better service experience to the people. Therefore, this paper focuses on the dissemination of disease public opinion information and risk management research in the blockchain environment. Furthermore, the application of advanced blockchain technology is used to seek breakthroughs in the field of risk management of network disease transmission and public opinion in terms of technology and data. This paper systematically sorts out the research results of domestic and foreign disease transmission network public opinion, public opinion risk management, and blockchain through comprehensive use of literature analysis, grounded research, empirical research, simulation research, and other research methods. Based on theories of the disease transmission network, public opinion, risk management, blockchain, and system dynamics, we conduct research on network public opinion and risk management of disease transmission in the block chain environment. At the theoretical level, the research in this paper can promote the risk management of disease transmission network public opinion in the blockchain environment and carry out the construction of the theoretical system to deepen the application of blockchain technology in the field of disease public opinion risk management. At the practical level, the research in this paper can guide the improvement of the effect of public opinion risk management. Then, blockchain technology is applied to manage the risk of disease public opinion. In future research, the theory of public opinion risk management will be further enriched. We will apply blockchain technology to develop the evolution of the risk of disease public opinion transmission, carry out research on public opinion management and other aspects, and at the same time, try the design and development of a blockchain public opinion system. In this way, the innovation of blockchain in the development and application of disease network public opinion research will be promoted. [ABSTRACT FROM AUTHOR]

A dramatic increase in the number of outbreaks of dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate dengue infections via COVID-19 data in countries that lack sufficient dengue data. [ABSTRACT FROM AUTHOR]

In this paper, we examine the epidemiological model B-SIR, focusing on the dynamic law that governs the transmission rate B. We define this dynamic law by the differential equation B ′ / B = F ⊕ − F ⊖ , where F ⊖ represents a reaction factor reflecting the stress proportional to the active group's percentage variation. Conversely, F ⊕ is a factor proportional to the deviation of B from its intrinsic value. We introduce the notion of contagion impulse f and explore its role within the model. Specifically, for the case where F ⊕ = 0 , we derive an autonomous differential system linking the effective reproductive number with f and subsequently analyze its dynamics. This analysis provides new insights into the model's behavior and its implications for understanding disease transmission. [ABSTRACT FROM AUTHOR]

This paper studies the dynamics of interacting Tilapia fish and Pelican bird population in the Salton Sea. We assume that the diseases spread in Tilapia fish follows the Holling type II response function, and the interaction between Tilapia and Pelican follows the Beddington–DeAngelis response function. The dynamics of diffusive and delayed system are discussed separately. Analytically, all the feasible equilibria and their stability are discussed. The criterion for Turing instability is derived. Based on the normal form theory and center manifold arguments, the existence of stability criterion and the direction of Hopf bifurcation are obtained. Numerical simulation shows the occurrence Hopf bifurcation, double Hopf bifurcation and transcritical bifurcation scenarios. The snap shot shows the spot, spot-strip mix patterns in the whole domain. Further, the stability switching phenomena is observed in the delayed system. Our comprehensive study highlights the effect of different parameters, multiple time delay and extinction in Pelican populations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution. The severity of SARS-CoV-2 infection varied over the course of the pandemic due to factors such as changes in variant characteristics and population immunity from previous infection or vaccination. Here, the authors estimate infection hospitalisation and infection fatality rates in England over time from the start of the pandemic until March 2023. [ABSTRACT FROM AUTHOR]

The burden of hepatitis E in Southeast Asia is substantial, influenced by its distinct socio-economic and environmental factors, as well as variations in healthcare systems. The aim of this study was to assess the pooled seroprevalence of hepatitis E across countries within the Southeast Asian region by the UN division. The study analyzed 66 papers across PubMed, Web of Science, and Scopus databases, encompassing data from of 44,850 individuals focusing on anti-HEV seroprevalence. The investigation spanned nine countries, excluding Brunei and East Timor due to lack of data. The pooled prevalence of anti-HEV IgG was determined to be 21.03%, with the highest prevalence observed in Myanmar (33.46%) and the lowest in Malaysia (5.93%). IgM prevalence was highest in Indonesia (12.43%) and lowest in Malaysia (0.91%). The study stratified populations into high-risk (farm workers, chronic patients) and low-risk groups (general population, blood donors, pregnant women, hospital patients). It revealed a higher IgG—28.9%, IgM—4.42% prevalence in the former group, while the latter group exhibited figures of 17.86% and 3.15%, respectively, indicating occupational and health-related vulnerabilities to HEV. A temporal analysis (1987–2023), indicated an upward trend in both IgG and IgM prevalence, suggesting an escalating HEV burden. These findings contribute to a better understanding of HEV seroprevalence in Southeast Asia, shedding light on important public health implications and suggesting directions for further research and intervention strategies. Key points Research Question Investigate the seroprevalence of hepatitis E virus (HEV) in Southeast Asian countries focusing on different patterns, timelines, and population cohorts. Findings Sporadic Transmission of IgG and IgM Prevalence: • Pooled anti-HEV IgG prevalence: 21.03% • Pooled anti-HEV IgM prevalence: 3.49% Seroprevalence among specific groups: High-risk group (farm workers and chronic patients): • anti-HEV IgG: 28.9% • anti-HEV IgM: 4.42% Low-risk group (general population, blood donors, pregnant women, hospital patients): • anti-HEV IgG: 17.86% • anti-HEV IgM: 3.15% Temporal Seroprevalence of HEV: Anti-HEV IgG prevalence increased over decades (1987–1999; 2000–2010; 2011–2023): 12.47%, 18.43%, 29.17% as an anti-HEV IgM prevalence: 1.92%, 2.44%, 5.27% Importance Provides a comprehensive overview of HEV seroprevalence in Southeast Asia. Highlights variation in seroprevalence among different population groups. Reveals increasing trend in HEV seroprevalence over the years. Distinguishes between sporadic and epidemic cases for a better understanding of transmission dynamics. [ABSTRACT FROM AUTHOR]