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This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, Re and R0, for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible-Infectious-Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters.

COVID-19, which broke out globally in 2019, is an infectious disease caused by a novel strain of coronavirus, and its spread is highly contagious and concealed. Environmental vectors play an important role in viral infection and transmission, which brings new difficulties and challenges to disease prevention and control. In this paper, a type of differential equation model is constructed according to the spreading functions and characteristics of exposed individuals and environmental vectors during the virus infection process. In the proposed model, five compartments were considered, namely, susceptible individuals, exposed individuals, infected individuals, recovered individuals, and environmental vectors (contaminated with free virus particles). In particular, the re-positive factor was taken into account (i.e., recovered individuals who have lost sufficient immune protection may still return to the exposed class). With the basic reproduction number R0 of the model, the global stability of the disease-free equilibrium and uniform persistence of the model were completely analyzed. Furthermore, sufficient conditions for the global stability of the endemic equilibrium of the model were also given. Finally, the effective predictability of the model was tested by fitting COVID-19 data from Japan and Italy.

With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.

The development of COVID-19 in China has gradually become normalized; thus, the prevention and control of the pandemic has encountered new problems: the amount of infectious medical waste (IMW) has increased sharply; the location of outbreaks are highly unpredictable; and the pandemic occurs everywhere. Thus, it is vital to design an effective IMW reverse logistics network to cope with these problems. This paper firstly introduces mobile processing centers (MPCs) into an IMW reverse logistics network for resource-saving, quick response, and the sufficient capacity of processing centers. Then, a multi-participant-based (public central hospitals, disposal institutions, the logistics providers, and the government) collaborative location and a routing optimization model for IMW reverse logistics are built from an economic, environmental perspective. An augmented ε-constraint method is developed to solve this proposed model. Through a case study in Chongqing, it is found that for uncertain outbreak situations, fixed processing centers (FPCs) and MPCs can form better disposal strategies. MPC can expand the processing capacity flexibly in response to the sudden increase in IMW. The results demonstrate good performance in reduction in cost and infection risk, which could greatly support the decision making of IMW management for the government in the pandemic prevention and control.

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.

Older people living in care homes are at high risk of poor health outcomes and mortality if they contract COVID-19 or other infectious diseases. Measures used to protect residents include social distancing and isolation, although implementation is challenging. This review aimed to assess the social distancing and isolation strategies used by care homes to prevent and control the transmission of COVID-19 and other infectious diseases. Seven electronic databases were searched: Medline, CINAHL, Embase, PsycINFO, HMIC, Social Care Online, and Web of Science Core Collection. Grey literature was searched using MedRxiv, PDQ-Evidence, NICE Evidence Search, LTCCovid19.org and TRIP. Extracted data were synthesised using narrative synthesis and tabulation. 103 papers were included (10 empirical studies, seven literature reviews, and 86 policy documents). Strategies used to prevent and control the transmission of COVID-19 and other infectious diseases included social distancing and isolation of residents and staff, zoning and cohorting of residents, restriction of resident movement/activities, restriction of visitors and restriction of staff working patterns. This review demonstrates a lack of empirical evidence and the limited nature of policy documentation around social distancing and isolation measures in care homes. Evaluative research on these interventions is needed urgently, focusing on the well-being of all residents, particularly those with hearing, vision or cognitive impairments.

Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa's infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme-the prediction of infectious diseases in Africa-by capturing the current research hotspots and trends.

With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.

The COVID-19 pandemic affected nearly all strata of life, including scientific activities. Implementation of nationwide lockdowns, closures of universities, and other measures significantly limiting social mobility precluded field and laboratory research from being pursued at a pre-pandemic pace. On the other hand, working from home could translate into higher productivity of researchers in terms of publication output, ultimately affecting the journals' metrics. This effect has been well evidenced in the biomedical field, but whether it also occurred in aquatic sciences has not been explored. Therefore, this study aimed to analyze and compare various metrics of journals classified in the Scopus evaluations in 2019 (encompassing 2016–2019, the pre-pandemic period) and 2022 (covering 2019–2022, the pandemic period) within three subjects: aquatic science, oceanography, and water science and technology. In general, the number of journals classified into the considered categories increased in 2022 by 12%, and the majority (78%) experienced an increase in the CiteScore metric. The total published articles and received citations also significantly increased in 2022 by 17.5% and 63.5%, respectively, and were even higher in the case of open access journals—69% and 145%, respectively. The overall share of cited articles in the considered period was higher by 5% than in the 2019 Scopus evaluation and by 7% in the case of open access periodicals. However, source normalized impact per paper (SNIP) and the SCImago journal rank (SJR) values decreased by 7% and 8%, respectively. The highest share of citations was generated by Water Research, Water, and the Journal of Hydrology, and the highest number of articles was published by Water, Frontiers in Marine Science, and Desalination and Water Treatment. Only five of the COVID-19-related papers published in journals with the highest CiteScore and total number of citations received more than 200 citations, but their contribution to citations received by the journals was not high. In summary, the present study's findings indicate the high potential of the field dedicated to water sciences to adapt in a period challenged by an infectious disease pandemic. However, they do not exclude the adverse effect that this period could have on the activities of individual scientists and research teams. [ABSTRACT FROM AUTHOR]

In the midst of global efforts to curb the spread of infectious diseases, researchers worldwide are striving to unravel the intricate spatial and temporal patterns of disease transmission dynamics. Mathematical models are indispensable tools for understanding the dissemination of emerging pathogens and elucidating the evolution of epidemics. This paper introduces a novel approach by investigating city transmission networks as a framework for analyzing disease spread. In this network, major cities are depicted as nodes interconnected by edges representing disease transmission pathways. Subsequent network analysis employs various epidemiological and structural metrics to delineate the distinct roles played by cities in disease transmission. The primary objective is to identify superspreader cities. Illustratively, we apply this methodology to study COVID-19 transmission in Saudi Arabian cities, shedding light on the specific dynamics within this context. These insights offer valuable guidance for decision-making processes and the formulation of effective intervention strategies, carrying significant implications for managing public health crises. [ABSTRACT FROM AUTHOR]

The potential role of dromedaries in the spread on Influenza A virus was investigated in a paper by Adamu et al. [[3]], exploring the seroprevalence of this virus. One Health is a well-known strategy for promoting and developing interdisciplinary collaboration across all aspects of health in human, animal, and environmental domains. The risk of close contact between humans and wild animals is relatively low, but when pet animals are considered, this risk is much higher. [Extracted from the article]

The emergence of the highly contagious coronavirus disease has led to multiple pandemic waves, resulting in a significant number of hospitalizations and fatalities. Even outside of hospitals, general practitioners have faced serious challenges, stretching their resources and putting themselves at risk of infection. Telemonitoring systems based on Internet of things technology have emerged as valuable tools for remotely monitoring disease progression, facilitating rapid intervention, and reducing the risk of hospitalization and mortality. They allow for personalized monitoring strategies and tailored treatment plans, which are crucial for improving health outcomes. However, determining the appropriate monitoring intensity remains the responsibility of physicians, which poses challenges and impacts their workload, and thus, can hinder timely responses. To address these challenges, this paper proposes an expert system designed to recommend and adjust the monitoring intensity for COVID-19 patients receiving home treatment based on their medical history, vital signs, and reported symptoms. The system underwent initial validation using real-world cases, demonstrating a favorable performance (F1-score of 0.85). Subsequently, once integrated with an Internet of Things telemonitoring system, a clinical trial will assess the system's reliability in creating telemonitoring plans comparable with those of medics, evaluate its effectiveness in reducing medic–patient interactions or hospitalizations, and gauge patient satisfaction and safety. [ABSTRACT FROM AUTHOR]

Fast and accurate point-of-care testing (POCT) of infectious diseases is crucial for diminishing the pandemic miseries. To fight the pandemic coronavirus disease 2019 (COVID-19), numerous interesting electrochemical point-of-care (POC) tests have been evolved to rapidly identify the causal organism SARS-CoV-2 virus, its nucleic acid and antigens, and antibodies of the patients. Many of those electrochemical biosensors are impressive in terms of miniaturization, mass production, ease of use, and speed of test, and they could be recommended for future applications in pandemic-like circumstances. On the other hand, self-diagnosis, sensitivity, specificity, surface chemistry, electrochemical components, device configuration, portability, small analyzers, and other features of the tests can yet be improved. Therefore, this report reviews the developmental trend of electrochemical POC tests (i.e., test platforms and features) reported for the rapid diagnosis of COVID-19 and correlates any significant advancements with relevant references. POCTs incorporating microfluidic/plastic chips, paper devices, nanomaterial-aided platforms, smartphone integration, self-diagnosis, and epidemiological reporting attributes are also surfed to help with future pandemic preparedness. This review especially screens the low-cost and easily affordable setups so that management of pandemic disease becomes faster and easier. Overall, the review is a wide-ranging package for finding appropriate strategies of electrochemical POCT targeting pandemic infectious disease detection. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious disease that poses a serious risk to public health worldwide. To reduce its spread, people need to adopt preventive behaviours such as wearing masks, maintaining physical distance, and isolating themselves if they are infected. However, the effectiveness of these measures may depend on various factors that differ across countries. This paper investigates how some factors, namely outsiders' effect, life expectancy, population density, smoker percentage, and temperature, influence the transmission and death rate of COVID-19 in ninety-five top-affected countries. We collect and analyse the data of COVID-19 cases and deaths using statistical tests. We also use fuzzy logic to model the chances of COVID-19 based on the results of the statistical tests. Unlike the conventional uniform weighting of the rule base in fuzzy logic, we propose a novel method to calculate the weights of the rule base according to the significance of the factors. This study aims to provide a comprehensive and comparative analysis of the factors of COVID-19 transmission and death rates among different countries. [ABSTRACT FROM AUTHOR]

At the beginning of 2023, the Italian former prime minister, the former health minister and 17 others including the current president of the Lombardy region were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government's response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse the pandemic data of Italy and the Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use of early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is still ongoing, even if the emergency is over, and we now have enough data to analyze the outbreak over a long timeline. There is evidence that the outbreak alternates periods of high and low infections. Retrospectively, this can help in understanding the nature of an appropriate mathematical model for this dramatic infection. The periodic behavior may be the consequence of time-dependent coefficients related to seasonal effects and specific political actions, or an intrinsic feature of the model. The present paper relies on the assumption that the periodic spikes are an intrinsic feature of the disease, and, as such, it should be properly reflected in the mathematical model. Based on the concept of waning immunity proposed for other pathologies, we introduce a new model with (i) a compartment for weakly immune people subject to immunity booster, represented by a non-linear term; (ii) discrimination between individuals infected/vaccinated for the first time, and individuals already infected/vaccinated, undergoing to new infections/doses. We analyze some preliminary properties of our model, called SIRW2, and provide a proof-of-concept that it is capable of reproducing qualitatively the long-term oscillatory behavior of COVID-19 infection. [ABSTRACT FROM AUTHOR]

Infectious disease epidemics are challenging for medical and public health practitioners. They require prompt treatment, but it is challenging to recognize and define epidemics in real time. Knowing the prediction of an infectious disease epidemic can evaluate and prevent the disease's impact. Mathematical models of epidemics that work in real time are important tools for preventing disease, and data-driven deep learning enables practical algorithms for identifying parameters in mathematical models. In this paper, the SIR model was reduced to a logistic differential equation involving a constant parameter and a time-dependent function. The time-dependent function leads to constant, rational, and birational models. These models use several constant parameters from the available data to predict the time and number of people reported to be infected with the COVID-19 Omicron variant. Two out of these three models, rational and birational, provide accurate predictions for countries that practice strict mitigation measures, but fail to provide accurate predictions for countries that practice partial mitigation measures. Therefore, we introduce a time-series model based on neural networks to predict the time and number of people reported to be infected with the COVID-19 Omicron variant in a given country that practices both partial and strict mitigation measures. A logistics-informed neural network algorithm was also introduced. This algorithm takes as input the daily and cumulative number of people who are reported to be infected with the COVID-19 Omicron variant in the given country. The algorithm helps determine the analytical solution involving several constant parameters for each model from the available data. The accuracy of these models is demonstrated using error metrics on Omicron variant data for Portugal, Italy, and China. Our findings demonstrate that the constant model could not accurately predict the daily or cumulative infections of the COVID-19 Omicron variant in the observed country because of the long series of existing data of the epidemics. However, the rational and birational models accurately predicted cumulative infections in countries adopting strict mitigation measures, but they fell short in predicting the daily infections. Furthermore, both models performed poorly in countries with partial mitigation measures. Notably, the time-series model stood out for its versatility, effectively predicting both daily and cumulative infections in countries irrespective of the stringency of their mitigation measures. [ABSTRACT FROM AUTHOR]

Significant and composite indices for infectious disease can have implications for developing interventions and public health. This paper presents an investment for developing access to further analysis of the incidence of individual and multiple diseases. This research mainly comprises two steps: first, an automatic and reproducible procedure based on functional data analysis techniques was proposed for analyzing the dynamic properties of each disease; second, orthogonal transformation was adopted for the development of composite indices. Between 2000 and 2019, nineteen class B notifiable diseases in China were collected for this study from the National Bureau of Statistics of China. The study facilitates the probing of underlying information about the dynamics from discrete incidence rates of each disease through the procedure, and it is also possible to obtain similarities and differences about diseases in detail by combining the derivative features. There has been great success in intervening in the majority of notifiable diseases in China, like bacterial or amebic dysentery and epidemic cerebrospinal meningitis, while more efforts are required for some diseases, like AIDS and virus hepatitis. The composite indices were able to reflect a more complex concept by combining individual incidences into a single value, providing a simultaneous reflection for multiple objects, and facilitating disease comparisons accordingly. For the notifiable diseases included in this study, there was superior management of gastro-intestinal infectious diseases and respiratory infectious diseases from the perspective of composite indices. This study developed a methodology for exploring the prevalent properties of infectious diseases. The development of effective and reliable analytical methods provides special insight into infectious diseases' common dynamics and properties and has implications for the effective intervention of infectious diseases. [ABSTRACT FROM AUTHOR]

Vaccination strategies remain one of the most effective and feasible preventive measures in combating infectious diseases, particularly during the COVID-19 pandemic. With the passage of time, continuous long-term lockdowns became impractical, and the effectiveness of contact-tracing procedures significantly declined as the number of cases increased. This paper presents a mathematical assessment of the dynamics and prevention of COVID-19, taking into account the constant and time-varying optimal COVID-19 vaccine with multiple doses. We attempt to develop a mathematical model by incorporating compartments with individuals receiving primary, secondary, and booster shots of the COVID-19 vaccine in a basic epidemic model. Initially, the model is rigorously studied in terms of qualitative analysis. The stability analysis and mathematical results are presented to demonstrate that the model is asymptotically stable both locally and globally at the COVID-19-free equilibrium state. We also investigate the impact of multiple vaccinations on the COVID-19 model's results, revealing that the infection risk can be reduced by administrating the booster vaccine dose to those individuals who already received their first vaccine doses. The existence of backward bifurcation phenomena is studied. A sensitivity analysis is carried out to determine the most sensitive parameter on the disease incidence. Furthermore, we developed a control model by introducing time-varying controls to suggest the optimal strategy for disease minimization. These controls are isolation, multiple vaccine efficacy, and reduction in the probability that different vaccine doses do not develop antibodies against the original virus. The existence and numerical solution to the COVID-19 control problem are presented. A detailed simulation is illustrated demonstrating the population-level impact of the constant and time-varying optimal controls on disease eradication. Using the novel concept of human awareness and several vaccination doses, the elimination of COVID-19 infections could be significantly enhanced. [ABSTRACT FROM AUTHOR]

Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients. [ABSTRACT FROM AUTHOR]

From the moment a patient is admitted to the hospital, monitoring begins, and specific information is collected. The continuous flow of parameters, including clinical and analytical data, serves as a significant source of information. However, there are situations in which not all values from medical tests can be obtained. This paper aims to predict the medical test values of COVID-19 patients in the intensive care unit (ICU). By retrieving the missing medical test values, the model provides healthcare professionals with an additional tool and more information with which to combat COVID-19. The proposed approach utilizes a customizable deep learning model. Three types of neural networks, namely Multilayer Perceptron (MLP), Long/Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), are employed. The parameters of these neural networks are configured to determine the model that delivers the optimal performance. Evaluation of the model's performance is conducted using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The application of the proposed model achieves predictions of the retrieved medical test values, resulting in RMSE = 7.237, MAPE = 5.572, and MAE = 4.791. Moreover, the article explores various scenarios in which the model exhibits higher accuracy. This model can be adapted and utilized in the diagnosis of future infectious diseases that share characteristics with Coronavirus Disease 2019 (COVID-19). [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) continues to spread throughout the world, and it is necessary for us to implement effective methods to prevent and control the spread of the epidemic. In this paper, we propose a new model called Spatial–Temporal Attention Graph Convolutional Networks (STAGCN) that can analyze the long-term trend of the COVID-19 epidemic with high accuracy. The STAGCN employs a spatial graph attention network layer and a temporal gated attention convolutional network layer to capture the spatial and temporal features of infectious disease data, respectively. While the new model inherits the symmetric "space-time space" structure of Spatial–Temporal Graph Convolutional Networks (STGCN), it enhances its ability to identify infectious diseases using spatial–temporal correlation features by replacing the graph convolutional network layer with a graph attention network layer that can pay more attention to important features based on adaptively adjusted feature weights at different time points. The experimental results show that our model has the lowest error rate compared to other models. The paper also analyzes the prediction results of the model using interpretable analysis methods to provide a more reliable guide for the decision-making process during epidemic prevention and control. [ABSTRACT FROM AUTHOR]

Featured Application: Intelligent prevention and control of infectious diseases. Infectious diseases, such as COVID-19, may have a significant impact on human daily life and social activities. One effective method to prevent and control the spread of such diseases is to accurately locate the sources of infection and limit the possible exposure to the virus. This paper presents a method, system, big data storage and analysis to control the infection based on Bluetooth technology. GPS and Bluetooth positioning are combined to track the movement trajectory of each person with a smart device, locally store the location information and close contacts, as well as periodically update it on the cloud platform. Based on the related algorithms of big data, this method can provide personal and regional risk levels, providing an alarm function which can be triggered by being close enough to a high-risk area or if the infected person's risk level is greater than the set threshold within the Bluetooth interconnection range. The system can provide a wealth of data on the location of infection sources and close contacts, offering valuable technical support for rapid and efficient epidemic prevention and control. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (SARS-CoV-2) has caused many infections and deaths throughout the world; the spread of the coronavirus pandemic is still ongoing and continues to affect healthcare systems and economies of countries worldwide. Mathematical models are used in many applications for infectious diseases, including forecasting outbreaks and designing containment strategies. In this paper, we study two types of SIR and SEIR models for the coronavirus. This study focuses on the discrete-time and fractional-order of these models; we study the stability of the fixed points and orbits using the Jacobian matrix and the eigenvalues and eigenvectors of each case; moreover, we estimate the parameters of the two systems in fractional order. We present a statistical study of the coronavirus model in two countries: Saudi Arabia, which has successfully recovered from the SARS-CoV-2 pandemic, and China, where the number of infections remains significantly high. [ABSTRACT FROM AUTHOR]

Vaccination reduces the risks related to infectious disease, especially among more vulnerable groups, such as older adults. The vaccines available to older adults in the UK through the government-funded programme currently include influenza, pneumococcal, shingles and COVID-19 vaccines. The purpose of the programme is disease prevention and improving wellbeing among the ageing population. Yet, the target population's views of the programme remain unknown. This paper aims to increase the understanding of older adults' perceptions of the vaccination programme available in the UK. A total of 13 online focus groups (56 informants) were carried out for this qualitative study. The findings indicate that getting vaccinated involves personal decision-making processes, which are influenced by previous experiences and interpersonal interactions. Factors related to the wider community and culture are less prominent in explaining vaccination decisions. However, opportunistic vaccination offers, a lack of information and a lack of opportunities to discuss vaccines, especially with healthcare professionals, are prominent factors. The study provides in-depth data about the rationale behind older adults' vaccination decisions in the UK. We recommend that the provision of information and opportunities to discuss vaccines and infectious disease be improved to enable older adults' to make better informed decisions regarding the vaccines available to them. [ABSTRACT FROM AUTHOR]

The syphilis and COVID-19 pandemics have marked a turning point in the history of mankind. The aim of this review is to analyze what two pandemics caused by different diseases have in common. It is a scoping review made up of papers covering everything related to syphilis and COVID-19. The dialectical structural model of care (DSMC) is applied, focusing on three thematic plots that explain the historical and current context of the topic addressed. To this end, we compiled information from books, journals, and databases such as Cochrane, National Library of Spain, PubMed/Medline, Scielo, and Google Scholar. Syphilis is a bacterial disease transmitted sexually. COVID-19 is a viral infection transmitted by droplets. Despite their similarities and differences, both have triggered pandemics that have claimed the lives of thousands of people. Both still exist as active diseases. The origin of both remains a scientific enigma; many human and material resources have been devoted to tackling these two infections, and a wide range of drugs have been developed to combat them. [ABSTRACT FROM AUTHOR]

Vaccination against any infectious disease is considered to be an effective way of controlling it. This paper studies a fractional order model with vaccine efficacy and waning immunity. We present the model's dynamics under vaccine efficacy, the impact of immunization, and the waning of the vaccine on coronavirus infection disease. We analyze the model under their equilibrium points. The model under the equilibrium points is discussed and proven that it is locally asymptotically stable if R v is lesser than unity. We present the backward bifurcation hypothesis of the model and show that there is a parameter β 2 that causes the backward bifurcation in the imperfect vaccine model. We show certain assumptions when ψ = 1 for the imperfect vaccine case; the model is both stable globally asymptotically at the disease-free ( R 0 ≤ 1 ) and endemic cases ( R 0 > 1 ). By using infected cases from the recent wave throughout Pakistan, we shall estimate the model parameters and calculate the numerical value of the basic reproductive number R 0 ≈ 1.2591 . We present the comprehensive graphical results for the realistic parameter values and show many useful suggestions regarding the elimination of the infection from society. The vaccination efficacy that provides an important role in disease elimination is discussed in detail. [ABSTRACT FROM AUTHOR]

Nowadays, a lot of research papers are concentrating on the diffusion dynamics of infectious diseases, especially the most recent one: COVID-19. The primary goal of this work is to explore the stability analysis of a new version of the S E I R model formulated with incommensurate fractional-order derivatives. In particular, several existence and uniqueness results of the solution of the proposed model are derived by means of the Picard–Lindelöf method. Several stability analysis results related to the disease-free equilibrium of the model are reported in light of computing the so-called basic reproduction number, as well as in view of utilising a certain Lyapunov function. In conclusion, various numerical simulations are performed to confirm the theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, we explore two modeling approaches to understanding the dynamics of infectious diseases in the population: equation-based modeling (EBM) and agent-based modeling (ABM). To achieve this, a comparative study of these approaches was conducted and we highlighted their advantages and disadvantages. Two case studies on the spread of the COVID-19 pandemic were carried out using both approaches. The results obtained show that differential equation-based models are faster but still simplistic, while agent-based models require more machine capabilities but are more realistic and very close to biology. Based on these outputs, it seems that the coupling of both approaches could be an interesting compromise. [ABSTRACT FROM AUTHOR]

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet. [ABSTRACT FROM AUTHOR]

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece. [ABSTRACT FROM AUTHOR]

The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is transmitted by contacting; however, the virus is so active that it can attach to objects and be transmitted from objects to humans via such contacting. The virus, which spreads through some living or inanimate-mediated processes, is more dangerous. On the basis of the routine contact transmission of infectious diseases, this paper further discusses the scope and efficiency of infectious diseases with indirect transmission. Through the study of two different transmission routes, the dynamic models of infectious diseases were constructed. The propagation of these two processes is theoretically studied using a differential equation model and stability analysis theory, and some actual virus propagation processes are simulated by numerical solutions. The prevention and control methods of infectious diseases are given, which lay the theoretical foundation for the discussion of related problems in practical application. [ABSTRACT FROM AUTHOR]

The consideration of infectious diseases from a mathematical point of view can reveal possible options for epidemic control and fighting the spread of infection. However, predicting and modeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemic of a new type of virus based on the example of COVID-19. The important result of the study is the description of the principle of dynamic balance of epidemiological processes, which has not been previously used by other researchers for epidemic modeling. The new approach is also based on solving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered and dead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the future trajectories of stochastic parameters, which are called the percentage increase in the total number of confirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the considered indicators of the COVID-19 epidemic, in Russia and European countries, during the first wave of the epidemic. [ABSTRACT FROM AUTHOR]

Propolis has gained wide popularity over the last decades in several parts of the world. In parallel, the literature about propolis composition and biological properties increased markedly. A great number of papers have demonstrated that propolis from different parts of the world is composed mainly of phenolic substances, frequently flavonoids, derived from plant resins. Propolis has a relevant role in increasing the social immunity of bee hives. Experimental evidence indicates that propolis and its components have activity against bacteria, fungi, and viruses. Mechanisms of action on bacteria, fungi, and viruses are known for several propolis components. Experiments have shown that propolis may act synergistically with antibiotics, antifungals, and antivirus drugs, permitting the administration of lower doses of drugs and higher antimicrobial effects. The current trend of growing resistance of microbial pathogens to the available drugs has encouraged the introduction of propolis in therapy against infectious diseases. Because propolis composition is widely variable, standardized propolis extracts have been produced. Successful clinical trials have included propolis extracts as medicine in dentistry and as an adjuvant in the treatment of patients against COVID-19. Present world health conditions encourage initiatives toward the spread of the niche of propolis, not only as traditional and alternative medicine but also as a relevant protagonist in anti-infectious therapy. Production of propolis and other apiary products is environmentally friendly and may contribute to alleviating the current crisis of the decline of bee populations. Propolis production has had social-economic relevance in Brazil, providing benefits to underprivileged people. [ABSTRACT FROM AUTHOR]

Occupancy density is a dynamic measurement that reveals the relationship between the floor area and occupant count, usually in a room or building. The research presented in this paper probes further into the relationship between the physical properties of space and occupants' activity, to expand the understanding of occupancy density. The presented outcome is an evidence-based technique for determining room and activity-specific occupancy density limits that can support the design and be integrated into the design process. In this study, occupant information, namely, positioning, is simulated in the spatial context, including room dimensions and furniture layout. Controllable distancing variables, such as those globally introduced in response to the COVID-19 pandemic to prevent the spread of infectious diseases in indoor environments, are used to assess occupancy density thresholds. [ABSTRACT FROM AUTHOR]

The end of the acute phase of the COVID-19 pandemic is near in some countries as declared by World Health Organization (WHO) in January 2022 based on some studies in Europe and South Africa despite unequal distribution of vaccines to combat the disease spread globally. The heterogeneity in individual age and the reaction to biological and environmental changes that has been observed in COVID-19 dynamics in terms of different reaction to vaccination by age group, severity of infection per age group, hospitalization and Intensive Care Unit (ICU) records show different patterns, and hence, it is important to improve mathematical models for COVID-19 pandemic prediction to account for different proportions of ages in the population, which is a major factor in epidemic history. We aim in this paper to estimate, using the Usher model, the lifespan loss due to viral infection and ageing which could result in pathological events such as infectious diseases. Exploiting epidemiology and demographic data firstly from Cameroon and then from some other countries, we described the ageing in the COVID-19 outbreak in human populations and performed a graphical representation of the proportion of sensitivity of some of the model parameters which we varied. The result shows a coherence between the orders of magnitude of the calculated and observed incidence numbers during the epidemic wave, which constitutes a semi-quantitative validation of the mathematical modelling approach at the population level. To conclude, the age heterogeneity of the populations involved in the COVID-19 outbreak needs the consideration of models in age groups with specific susceptibilities to infection. [ABSTRACT FROM AUTHOR]

Since November 2019, each country in the world has been affected by COVID-19, which has claimed more than four million lives. As an infectious disease, COVID-19 has a stronger transmission power and faster propagation speed. In fact, environmental noise is an inevitable important factor in the real world. This paper mainly gives a new random infectious disease system under infection rate environmental noise. We give the existence and uniqueness of the solution of the system and discuss the ergodic stationary distribution and the extinction conditions of the system. The probability density function of the stochastic system is studied. Some digital simulations are used to demonstrate the probability density function and the extinction of the system. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in December 2019 in Wuhan, the capital of China's Hubei province, and has since spread globally, resulting in the ongoing coronavirus pandemic. The most important type of COVID-19 confrontation is the use of polymerase chain reaction testing (PCR) to detect if a person is healthy or infected with COVID-19. Many countries released different numbers about the total cases of infected persons with the pandemic based on the number of total PCRs and other statistical numbers related to this disease. The total case numbers for some countries were very promising, such that they implied that these countries were to recover soon from these diseases. At other times, some countries overestimated the total number of cases or deaths to get financial aid. Therefore, there is a need to verify and evaluate these numbers by using machine-learning algorithms that proved to be efficient in solving many problems. The convolutional neural network (CNN) is an advanced machine-learning algorithm that was deployed to detect COVID-19 from different medical images such as X-ray images. However, CNN was not used to validate the published COVID-19 statistical data. In this paper, we adapted the self-organizing UNet (SO-UNet) in the cooperative convolutional neural network (CCNN) model to detect the accuracy of the reported COVID-19 statistics. The detection is based on using COVID-19 statistical variables that are reported by reliable and credible databases provided by known global organizations. These variables are used to create multi-dimension images to train and test the CCNN model. The results showed that reported important statistics for COVID-19 could be verified using the CCNN model. It also showed that the verified data coincided with the verification reported by the most important and trusted organizations in the world. Finally, the comparison of the new model to Benford's law outcome proved the model's credibility and efficiency in validating COVID-19 reported data. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) has exposed the unpreparedness of governments in their capacities to prevent, detect, and respond to emerging infectious diseases. Many healthcare systems have been overburdened and the coordinated efforts in different countries have focused on containment and mitigation, with varying degrees of success. A delay in the detection of and response to infectious diseases can lead to the overburdening of already challenged health systems. Containment strategies, such as social distancing, contact tracing, quarantining of exposed individuals and lockdowns, can help control the spread of the infection in communities. Still, long-term solutions should be sought to counter future outbreaks. In this paper, we focus on Zimbabwe to identify and discuss public health strategies that can result in an effective response to future infectious disease outbreaks. We consider potential solutions to facilitate early detection, control, and mitigation of any similar emerging infectious disease. We argue that sustained financial support in public health infrastructure, both locally and nationally, integrated surveillance response systems, and improved communication and research within and across public and private sectors can be instrumental in limiting the damage caused by future outbreaks. [ABSTRACT FROM AUTHOR]

To address the COVID-19 pandemic, as with other infectious diseases, a key intervention is vaccination. Health communications are thus of vital importance for informing the public on the benefits and risks of vaccines. This in turn makes the readability of media content fundamental. Previous studies of COVID-19-related information have found the readability of online information considerably more difficult than recommended. However, studies on the readability of information related to COVID-19 vaccination in Asian contexts have yet to be carried out. Furthermore, especially in the case of the current pandemic, health information is communicated by a variety of information channels, including the internet and mass media. This paper investigates the readability of textual information on COVID-19 vaccination found online and in newspaper articles in parts of Asia where English is one of the main languages, namely Singapore, Hong Kong, and the Philippines. Readability was assessed using a set of readability tests (Flesch–Kincaid Reading Ease, Flesch–Kincaid Grade Level, Gunning Fog Index, Coleman–Liau Index, and Simple Measure of Gobbledygook Grade level). It was found that a low proportion of URLs scored within recommended readability thresholds, and did so consistently across locations and types of sources. Furthermore, a relatively low proportion of web searches returned information from local sources; most URLs linked to sources outside of Singapore, Hong Kong, or the Philippines. Further, local online and newspaper sources scored similarly poorly on readability on average compared to non-local sources. Understanding of fast-evolving health communications concerning COVID-19 vaccination encompasses information about vaccine development and deployment from other locations, as well as locally. Nevertheless, these findings indicated a fairly low proportion of local sources among the top search results, and relatively low (i.e., difficult-to-read) readability scores for top search results and for local newspapers. An important issue for health communications strategies addressing COVID-19 vaccination will therefore be to consider different types of media sources in order to achieve the right mix of local and non-local sources while also ensuring appropriate readability. [ABSTRACT FROM AUTHOR]

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases. [ABSTRACT FROM AUTHOR]

The world has been severely challenged by the Coronavirus Disease (COVID-19) outbreak since the early 2020s. Worldwide, there have been more than 66 million cases of infection and over 3,880,450 deaths caused by this highly contagious disease. All sections of the population including those who are affected, those who are not affected and those who have recovered from this disease, are suffering physiologically, psychologically or psychophysiologically. In this paper we briefly discuss the consequences of COVID-19 on physiological, psychological and psychophysiological vulnerability. We also attempt to provide evidence in support of exercise management as a prevention strategy for improving and minimizing the physiological, psychological and psychophysiological effects of COVID-19. Moderate exercise including walking, yoga and tai-chi to name but a few exercise regimes are critical in preventing COVID-19 and its complications. Governments, public health authorities and the general population should maintain physical activity during the COVID-19 pandemic to prevent additional physical and mental distress. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic highlighted the crucial role of diagnostic testing in managing infectious diseases, particularly through the use of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tests. RT-qPCR has been pivotal in detecting and quantifying viral RNA, enabling the identification and management of SARS-CoV-2 infections. However, despite its widespread use, there remains a notable gap in understanding fundamental diagnostic metrics such as sensitivity and specificity among many scientists and healthcare practitioners. This gap is not merely academic; it has profound implications for interpreting test results, making public health decisions, and affecting patient outcomes. This review aims to clarify the distinctions between laboratory- and field-based metrics in the context of RT-qPCR testing for SARS-CoV-2 and summarise the global efforts that led to the development and optimisation of these tests during the pandemic. It is intended to enhance the understanding of these fundamental concepts among scientists and healthcare professionals who may not be familiar with the nuances of diagnostic test evaluation. Such knowledge is crucial for accurately interpreting test results, making informed public health decisions, and ultimately managing infectious disease outbreaks more effectively. [ABSTRACT FROM AUTHOR]

We aimed to use the digital platform maintained by the local health service providers in Southeast Sweden for integrated monitoring of disparities in vaccination and morbidity during the COVID-19 pandemic. The monitoring was performed in the adult population of two counties (n = 657,926) between 1 February 2020 and 15 February 2022. The disparities monitored were relocated (internationally displaced), substance users, and suffering from a psychotic disorder. The outcomes monitored were COVID-19 vaccination, SARS-CoV-2 test results, and hospitalization with COVID-19. Relocated residents displayed an increased likelihood of remaining unvaccinated and a decreased likelihood of testing as well as increased risks of primary SARS-CoV-2 infection and hospitalization compared with the general population. Suffering from a major psychiatric disease was associated with an increased risk of remaining unvaccinated and an increased risk of hospitalization but a decreased risk of SARS-CoV-2 infection. From the digital monitoring, we concluded that the relocated minority received insufficient protection during the pandemic, suggesting the necessity for comprehensive promotion of overall social integration. Persons with major psychiatric diseases underused vaccination, while they benefitted from proactively provided testing, implying a need for active encouragement of vaccination. Further research is warranted on legal and ethical frameworks for digital monitoring in vaccination programs. [ABSTRACT FROM AUTHOR]

Simple Summary: ASF remains a significant concern for the Philippines as it has impacted the swine industry and food security. It causes high mortality rates, leading to decreased pork production, high prices of pork and pork products, and financial losses for farmers and stakeholders, especially for the livelihoods of many small-scale pig farmers in the Philippines. Since its first appearance in 2019, the government has implemented strict measures to control the spread of the disease that include enhanced surveillance, strict biosecurity and quarantine protocols, restricted movement of live pigs and pork products, and culling of infected and at-risk pigs. Ongoing surveillance and cooperation between industry stakeholders, government agencies, and research institutions and collaboration with international organizations and experts to develop enhanced strategies for ASF control and prevention are necessary to mitigate ASF's impacts on the swine industry. Moreover, public awareness campaigns and support for affected farmers are continuously in place to combat ASF in the Philippines. African swine fever (ASF), a highly contagious disease of swine, has posed a significant global threat to the swine industry. As an archipelago, the Philippines has a geographic advantage when it comes to the risk of ASF transmission. However, since its introduction to the Philippines in 2019, it has proliferated not only in backyard and commercial farms but also in wild pig populations. While certain parts of the country were more affected than others, the epidemiologic features of ASF necessitate that all affected areas must be closely monitored and that confirmed cases be treated with the utmost care. With the very limited data on ASF epidemiology and surveillance in the Philippines, future efforts to combat ASF must place even greater emphasis on improved prevention and control strategies. It is worth mentioning that the government's efforts toward comprehensive ASF surveillance and epidemiological investigation into the possible ASFV sources or transmission pathways are the most important measures in the prevention and control of ASF outbreaks. This review article provides a comprehensive overview of the current swine industry and ASF situation in the Philippines, which includes its epidemiology, surveillance, prevention, and control strategies. [ABSTRACT FROM AUTHOR]

In this study, we aimed to characterize a cohort of children hospitalized due to varicella before and after the outbreak of the COVID-19 pandemic. Medical charts of all children hospitalized in the Regional Hospital of Infectious Diseases in Warsaw due to varicella in the years 2019 and 2022 were retrospectively analyzed and compared. In total, 221 children were included in the analysis; 59 of them were hospitalized in 2019, whereas 162 were hospitalized in 2022. Children hospitalized in 2022 were older than those reported in 2019 (median 4.0 vs. 3.0 years, p = 0.02). None of the hospitalized children received complete varicella vaccination. The most common complication in both years was bacterial superinfection of skin lesions, found in 156/221 (70.6%) of patients. This complication rate was higher in 2022 (50.8% in 2019 vs. 77.8% in 2022, p = 0.0001), OR = 3.38, 95% CI: 1.80–6.35. Moreover, skin infections in 2022 more often manifested with cellulitis (in 2022 13.6% vs. 3.4% in 2019, p = 0.03), OR = 4.40, 95% CI: 1.00–19.33. Sepsis as a complication of varicella was almost five-fold more prevalent in 2022 than in 2019 (p = 0.009), OR = 5.70, 95% CI: 1.31–24.77. Antibiotic use increased between 2019 and 2022 (71.2% vs. 85.2%, p = 0.01). Furthermore, patients were treated more frequently with the combination of two different antibiotics simultaneously (only 3.4% of patients in 2019 compared to 15.4% in 2022, p = 0.01). Primary infections with varicella zoster virus in 2022 led to a more severe course of the disease. [ABSTRACT FROM AUTHOR]

This study aims to provide a meta-synthesis of qualitative studies examining the perceptions and experiences of nurses who cared for patients in dedicated COVID-19 hospitals in South Korea. We searched key health databases (RISS, KISS, KMbase, NDSL, KoreaMed, DBpia, PubMed, CINAHL, and Cochrane) from September to November 2023. We reviewed and analyzed articles using a thematic synthesis approach. The quality of the studies was ascertained using the Critical Appraisal Skills Program checklist for qualitative research. Ultimately, 13 studies involving 219 nurses were included in the final review. Six major themes and thirteen subthemes emerged. During the unexpected COVID-19 pandemic, nurses were able to overcome difficult situations through their interactions with patients, sense of a professional mission, and commitment to nursing. Most importantly, they persevered through their collaboration and closeness with fellow nurses, despite confusion about their professional identity, the ethical dilemmas they faced in patient care, and the conflicting attitudes of their social support system. To prepare for future infectious disease outbreaks, a multifaceted support system should be established to enable nurses to have positive interactions with their families, colleagues, and patients, which have become central to their resilience. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to impact the world even three years after its outbreak. International border closures and health alerts severely affected the air transport industry, resulting in substantial financial losses. This study analyzes the global data on infected individuals alongside aircraft types, flight durations, and passenger flows. Using a Bayesian framework, we forecast the risk of infection during commercial flights and its potential spread across an air transport network. Our model allows us to explore the effect of mitigation measures, such as closing individual routes or airports, reducing aircraft occupancy, or restricting access for infected passengers, on disease propagation, while allowing the air industry to operate at near-normal levels. Our novel approach combines dynamic network modeling with discrete event simulation. A real-case study at major European hubs illustrates our methodology. [ABSTRACT FROM AUTHOR]