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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]

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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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. [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]

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]

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]

This paper discusses and provides some analytical studies for a modified fractional-order SIRD mathematical model of the COVID-19 epidemic in the sense of the Caputo–Katugampola fractional derivative that allows treating of the biological models of infectious diseases and unifies the Hadamard and Caputo fractional derivatives into a single form. By considering the vaccine parameter of the suspected population, we compute and derive several stability results based on some symmetrical parameters that satisfy some conditions that prevent the pandemic. The paper also investigates the problem of the existence and uniqueness of solutions for the modified SIRD model. It does so by applying the properties of Schauder's and Banach's fixed point theorems. [ABSTRACT FROM AUTHOR]

Complex systems are inherently multilevel and multiscale systems. The infectious disease system is considered a complex system resulting from the interaction between three sub-systems (host, pathogen, and environment) organized into a hierarchical structure, ranging from the cellular to the macro-ecosystem level, with multiscales. Therefore, to describe infectious disease phenomena that change through time and space and at different scales, we built a model framework where infectious disease must be considered the set of biological responses of human hosts to pathogens, with biological pathways shared with other pathologies in an ecological interaction context. In this paper, we aimed to design a framework for building a disease model for COVID-19 based on current literature evidence. The model was set up by identifying the molecular pathophysiology related to the COVID-19 phenotypes, collecting the mechanistic knowledge scattered across scientific literature and bioinformatic databases, and integrating it using a logical/conceptual model systems biology. The model framework building process began from the results of a domain-based literature review regarding a multiomics approach to COVID-19. This evidence allowed us to define a framework of COVID-19 conceptual model and to report all concepts in a multilevel and multiscale structure. The same interdisciplinary working groups that carried out the scoping review were involved. The conclusive result is a conceptual method to design multiscale models of infectious diseases. The methodology, applied in this paper, is a set of partially ordered research and development activities that result in a COVID-19 multiscale model. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 pandemic has highlighted the close link between economics and health in the context of emergency management. A widespread vaccination campaign is considered the main tool to contain the economic consequences. This paper will focus, at the level of wealth distribution modeling, on the economic improvements induced by the vaccination campaign in terms of its effectiveness rate. The economic trend during the pandemic is evaluated, resorting to a mathematical model joining a classical compartmental model including vaccinated individuals with a kinetic model of wealth distribution based on binary wealth exchanges. The interplay between wealth exchanges and the progress of the infectious disease is realized by assuming, on the one hand, that individuals in different compartments act differently in the economic process and, on the other hand, that the epidemic affects risk in economic transactions. Using the mathematical tools of kinetic theory, it is possible to identify the equilibrium states of the system and the formation of inequalities due to the pandemic in the wealth distribution of the population. Numerical experiments highlight the importance of the vaccination campaign and its positive effects in reducing economic inequalities in the multi-agent society. [ABSTRACT FROM AUTHOR]

COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a very contagious disease that has killed many people worldwide. According to data from the World Health Organization (WHO), the spread of the disease appears to be slower in Africa. Although several studies have been published on the relationship between meteorological parameters and COVID-19 transmission, the effects of climate conditions on COVID-19 remain largely unexplored and without consensus. However, the transmission of COVID-19 and sensitivity to climate conditions are also not fully understood in Africa. Here, using available epidemiological data over 275 days (i.e., from 1 March to 30 November 2020) taken from the European Center for Disease Prevention and Control of the European Union database and daily data of surface air temperature specific humidity and water vapor from the National Center for Environmental Prediction (NCEP), this paper investigates the potential contribution of climate conditions on COVID-19 transmission over 16 selected countries throughout three climatic regions of Africa (i.e., Sahel, Maghreb, and Gulf of Guinea). The results highlight statistically significant inverse correlations between COVID-19 cases and temperature over the Maghreb and the Gulf of Guinea regions. In contrast, positive correlations are found over the Sahel area, especially in the central part, including Niger and Mali. Correlations with specific humidity and water vapor parameters display significant and positive values over the Sahelian and the Gulf of Guinea countries and negative values over the Maghreb countries. Then, the COVID-19 pandemic transmission is influenced differently across the three climatic regions: (i) cold and dry environmental conditions over the Maghreb; (ii) warm and humid conditions over the Sahel; and (iii) cold and humid conditions over the Gulf of Guinea. In addition, for all three climatic regions, even though the climate impact has been found to be significant, its effect appears to display a secondary role based on the explanatory power variance compared to non-climatic factors assumed to be dominated by socio-economic factors and early strong public health measures. [ABSTRACT FROM AUTHOR]

At the end of 2019, an outbreak of the novel coronavirus (COVID-19) made a profound impact on the country's production and people's daily lives. Up until now, COVID-19 has not been fully controlled all over the world. Based on the clinical research progress of infectious diseases, combined with epidemiological theories and possible disease control measures, this paper establishes a Susceptible Infected Recovered (SIR) model that meets the characteristics of the transmission of the new coronavirus, using the least square estimation (LSE) method to estimate the model parameters. The simulation results show that quarantine and containment measures as well as vaccine and drug development measures can control the spread of the epidemic effectively. As can be seen from the prediction results of the model, the simulation results of the epidemic development of the whole country and Nanjing are in agreement with the real situation of the epidemic, and the number of confirmed cases is close to the real value. At the same time, the model's prediction of the prevention effect and control measures have shed new light on epidemic prevention and control. [ABSTRACT FROM AUTHOR]

The first cases of COVID-19 were reported in Wuhan Province, in China, in December 2019, spreading rapidly around the world. The World Health Organization (WHO) declared this pandemic at the beginning of March 2020 and, at the same time, the first patient in Galați County was confirmed. Both the global and the regional epidemiological evolutions have taken place with variations in incidence, which have been graphically recorded in several "waves". We conducted a retrospective study on cases of COVID-19 infection, hospitalized between March and June 2020 in an infectious diseases clinic from Galati, in the south-east of Romania. The present paper describes the "first-wave" regional epidemiological and clinical-biological features and the evolution of the COVID-19 pandemic. A poor outcome was related to late presentation to hospital, old age, and over six comorbid conditions including Alzheimer's disease. The high death rate among people from long-term care institutions is the consequence of the cumulative risk factors associated with immune senescence and inflammation, while COVID-19 is more likely a contributing factor to lower life expectancy. [ABSTRACT FROM AUTHOR]

COVID-19 is a highly contagious disease which has spread across the world. A deterministic model that considers an important component of individuals with vertically transmitted underlying diseases (high-risk susceptible individuals), rather than the general public, is formulated in this paper. We also consider key parameters that are concerned with the disease. An epidemiological threshold, R 0 , is computed using next-generation matrix approach. This is used to establish the existence and global stability of equilibria. We identify the most sensitive parameters which effectively contribute to change the disease dynamics with the help of sensitivity analysis. Our results reveal that increasing contact tracing of the exposed individuals who are tested for COVID-19 and hospitalizing them, largely has a negative impact on R 0 . Results further reveal that transmission rate between low-risk/high-risk susceptible individuals and symptomatic infectious individuals β and incubation rate of the exposed individuals σ have positive impact on R 0 . Numerical simulations show that there are fewer high-risk susceptible individuals than the general public when R 0 < 1 . This may be due to the fact that high-risk susceptible individuals may prove a bit more difficult to control than the low-risk susceptible individuals as a result of inherited underlying diseases present in them. We thus conclude that high level of tracing and hospitalizing the exposed individuals, as well as adherence to standard precautions and wearing appropriate Personal Protective Equipment (PPE) while handling emergency cases, are needed to flatten the epidemic curve. [ABSTRACT FROM AUTHOR]

Simple Summary: Infectious diseases have been part of human history. Countless epidemics have produced high mortality rates in vulnerable populations. With the understanding of the spread of these types of diseases, population groups have been able to adapt and better cope with infections. Given the COVID-19 pandemic, one of the strategies used is the modeling of infectious diseases with the aim of establishing protection measures for people and stopping the spread of the epidemic. Our study evaluates protection strategies through infectious disease modeling with COVID-19 data in a commune in Chile. The results of the simulations indicate that the model generates important protection for the population by recognizing the super-propagating people (bridge nodes). This type of protection can be key in the fight against COVID-19. Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this paper, we evaluate the effectiveness of the DIL-W α ranking in immunizing nodes in an edge-weighted network with 3866 nodes and 6,841,470 edges. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-W α , considering different protection budgets. Furthermore, we consider three different values for α ; in this way, we compare how the protection performs according to the value of α. [ABSTRACT FROM AUTHOR]

Simple Summary: Intranasal vaccination is one of the methods used to stimulate mucosal immunity. It has been widely practised to control many human and animal respiratory diseases. Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which resulted in a global pandemic. COVID-19 has reminded some veterinarians of various contagious veterinary diseases, including coronavirus infections in animals. This article discusses the control of highly contagious diseases of veterinary importance with emphasis on an intranasal vaccination approach, and the potential of implementing similar strategies in human medicine to control the ongoing COVID-19 pandemic. The world is currently facing an ongoing coronavirus disease 2019 (COVID-19) pandemic. The disease is a highly contagious respiratory disease which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Current control measures used by many countries include social distancing, wearing face masks, frequent hand washing, self-isolation, and vaccination. The current commercially available vaccines are injectable vaccines, although a few intranasal vaccines are in trial stages. The reported side effects of COVID-19 vaccines, perceptions towards the safety of the vaccines, and frequent mutation of the virus may lead to poor herd immunity. In veterinary medicine, attaining herd immunity is one of the main considerations in disease control, and herd immunity depends on the use of efficacious vaccines and the vaccination coverage in a population. Hence, many aerosol or intranasal vaccines have been developed to control veterinary respiratory diseases such as Newcastle disease, rinderpest, infectious bronchitis, and haemorrhagic septicaemia. Different vaccine technologies could be employed to improve vaccination coverage, including the usage of an intranasal live recombinant vaccine or live mutant vaccine. This paper discusses the potential use of intranasal vaccination strategies against human COVID-19, based on a veterinary intranasal vaccine strategy. [ABSTRACT FROM AUTHOR]

Given the present pandemic caused by the severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 virus, the authors tried fitting existing models for the daily loss of lives. Based on data reported by Worldometers on the initial stages (first wave) of the pandemic for countries acquiring the disease, the authors observed that the logarithmic rendering of their data hinted the response of a first-order process to a step function input, which may be modeled by a three-parameters function, as described in this paper. This model was compared against other similar, log(N)-class of models that are non-compartmental type (such as the susceptible, infected, and removed, or SIR models), obtaining good fit and statistical comparison results, where N denotes the cumulative number of daily presumed deaths. This simple first-order response model can also be applied to bacterial and other biological growth phenomena. Here we describe the model, the numerical methods utilized for its application to actual pandemic data, and the statistical comparisons with other models which shows that our simple model is comparatively outstanding, given its simplicity. While researching the models available, the authors found other functions that can also be applied, with extra parameters, to be described in follow-on articles. [ABSTRACT FROM AUTHOR]

The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that govern the outbreak of a contagious disease. The understanding of COVID-19 will be enriched by the analysis of data related to the phenomena, and this data can be collected using IoT sensors. In this paper, we show an integrated solution based on IoT technologies that can serve as opportunistic health data acquisition agents for combating the pandemic of COVID-19, named CIoTVID. The platform is composed of four layers—data acquisition, data aggregation, machine intelligence and services, within the solution. To demonstrate its validity, the solution has been tested with a use case based on creating a classifier of medical conditions using real data of voice, performing successfully. The layer of data aggregation is particularly relevant in this kind of solution as the data coming from medical devices has a very different nature to that coming from electronic sensors. Due to the adaptability of the platform to heterogeneous data and volumes of data; individuals, policymakers, and clinics could benefit from it to fight the propagation of the pandemic. [ABSTRACT FROM AUTHOR]

Accurate diagnosis at an early stage of infection is essential for the successful management of any contagious disease. The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is a pandemic that has affected 214 countries affecting more than 37.4 million people causing 1.07 million deaths as of the second week of October 2020. The primary diagnosis of the infection is done either by the molecular technique of RT-qPCR by detecting portions of the RNA of the viral genome or through immunodiagnostic tests by detecting the viral proteins or the antibodies produced by the host. As the demand for the test increased rapidly many naive manufacturers entered the market with novel kits and more and more laboratories also entered the diagnostic arena making the test result more error-prone. There are serious debates globally and regionally on the sensitivity and specificity of these tests and about the overall accuracy and reliability of the tests for decision making on control strategies. The significance of the test is also complexed by the presence of asymptomatic carriers, re-occurrence of infection in cured patients as well as by the varied incubation periods of the infection and shifting of the viral location in the host tissues. In this paper, we review the techniques available for SARS-CoV-2 diagnosis and probable factors that can reduce the sensitivity and specificity of the different test methods currently in vogue. We also provide a checklist of factors to be considered to avoid fallacious practices to reduce false positive and false negative results by the clinical laboratories. [ABSTRACT FROM AUTHOR]

Understanding of the pathogenesis of the coronavirus disease-2019 (COVID-19) remains incomplete, particularly in respect to the multi-organ dysfunction it may cause. We were the first to report the analogous biological and physiological features of COVID-19 pathogenesis and the harmful amplification loop between inflammation and tissue damage induced by the dysregulation of neutrophil extracellular traps (NETs) formation. Given the rapid evolution of this disease, the nature of its symptoms, and its potential lethality, we hypothesize that COVID-19 progresses under just such an amplifier loop, leading to a massive, uncontrolled inflammation process. Here, we describe in-depth the correlations of COVID-19 symptoms and biological features with those where uncontrolled NET formation is implicated in various sterile or infectious diseases. General clinical conditions, as well as numerous pathological and biological features, are analogous with NETs deleterious effects. Among NETs by-products implicated in COVID-19 pathogenesis, one of the most significant appears to be elastase, in accelerating virus entry and inducing hypertension, thrombosis and vasculitis. We postulate that severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) may evade innate immune response, causing uncontrolled NETs formation and multi-organ failure. In addition, we point to indicators that NETS-associated diseases are COVID-19 risk factors. Acknowledging that neutrophils are the principal origin of extracellular and circulating DNA release, we nonetheless, explain why targeting NETs rather than neutrophils themselves may in practice be a better strategy. This paper also offers an in-depth review of NET formation, function and pathogenic dysregulation, as well as of current and prospective future therapies to control NETopathies. As such, it enables us also to suggest new therapeutic strategies to fight COVID-19. In combination with or independent of the latest tested approaches, we propose the evaluation, in the short term, of treatments with DNase-1, with the anti-diabetic Metformin, or with drugs targeting elastase (i.e., Silvelestat). With a longer perspective, we also advocate a significant increase in research on the development of toll-like receptors (TLR) and C-type lectin-like receptors (CLEC) inhibitors, NET-inhibitory peptides, and on anti-IL-26 therapies. [ABSTRACT FROM AUTHOR]