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

As the proliferation of new variations of COVID-19 (Coronavirus Disease 2019) continues to increase, it is evident that the COVID-19 pandemic is not over. Indeed, we are living in a world of interrelating and overlapping pandemics—a syndemic. A syndemic accelerates the polarization of access to health care, financial support, and education opportunities in marginalized communities, and the polarization breeds social injustice, violence, and ignorance. What, then, is the Gospel the Church proclaims for those who have experienced the pandemic and are now facing a syndemic? As part of a liturgical response, this paper proposes preaching as a praxis of the ecclesiological Gospel. The ecclesiological Gospel is a term I suggest to highlight the contextual, sacramental, and communal aspects of the Gospel. Highlighting God's holistic work for salvation represented in baptism and the eucharist, the ecclesiological Gospel yearns to form a church that baptizes people in diversity, that severs evil interconnections, and that welcomes people to the table of hospitality that forms a new covenantal relationship. This paper examines the concept of a syndemic and its significance for preaching in marginalized communities. It contrasts a holistic Gospel perspective with a narrow view, proposes preaching as praxis of the ecclesiological Gospel, and illustrates its application within a particular context in responding to syndemic conditions. I hope this work offers a chance to reorient the meaning of the Gospel and the identity of the Church for people living in fear, grief, and hopelessness, while encouraging them with the unwavering hope revealed in Christ's suffering, death, and resurrection. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has placed enormous pressure on healthcare systems. In the first line of the "war" against the virus, hospitals tried to maintain their general operations, while supplementing their services with COVID-19 patient care. To tackle the new difficulties, hospitals had to develop strategic response plans, and were in need of finding the most effective management structure for their institution. The focus of this paper is the aforementioned management structures. After overviewing the international literature, we identified three different approaches and we named them the Horizontal, Vertical, and Intermedier Approach. The separation is based on the following features: decision making, information distribution, command forwarding, intra-hospital communication channels, hierarchy, and control. After summarizing the international experiences, we introduce a Hungarian centrum hospital's approach, as the institution was assigned to fulfil COVID-19 centrum hospital duties in the middle of the first wave. The North-Buda Szent János Central hospital was in need of restructuring their management structure, and the leadership decided to transform it into a hierarchical, vertical structure, operating with centralized decision making and personal control. This control–command system idea came from the hospital's Internist Head Coordinator Physician, who is the first author of our article, and had serious military medicine experiences (in Afghanistan). [ABSTRACT FROM AUTHOR]

By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today's environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices' limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model's efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems. [ABSTRACT FROM AUTHOR]

Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building's HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic disrupted public health and economies worldwide. In this paper, we investigate an optimal control problem to simultaneously minimize the epidemic size and control costs associated with intervention strategies based on official data. Considering people with undetected infections, we establish a control system of COVID-19 with time-varying parameters. To estimate these parameters, a parameter identification scheme is adopted and a mixed algorithm is constructed. Moreover, we present an optimal control problem with two objectives that involve the newly increased number of infected individuals and the control costs. A numerical scheme is conducted, simulating the epidemic data pertaining to Shanghai during the period of 2022, caused by the Omicron variant. Coefficient combinations of the objectives are obtained, and the optimal control measures for different infection peaks are indicated. The numerical results suggest that the identification variables obtained by using the constructed mixed algorithm to solve the parameter identification problem are feasible. Optimal control measures for different epidemic peaks can serve as references for decision-makers. [ABSTRACT FROM AUTHOR]

In this paper, an SEAI epidemic model with asymptomatic infection is studied under the background of mass transmission of COVID-19. First, we use the next-generation matrix method to obtain the basic reproductive number R 0 and calculate the equilibrium point. Secondly, when R 0 < 1 , the local asymptotic stability of the disease-free equilibrium is proved by Hurwitz criterion, and the global asymptotic stability of the disease-free equilibrium is proved by constructing the Lyapunov function. When R 0 > 1 , the system has a unique endemic equilibrium point and is locally asymptotically stable, and it is also proved that the system is uniformly persistent. Then, the application of optimal control theory is carried out, and the expression of the optimal control solution is obtained. Finally, in order to verify the correctness of the theory, the stability of the equilibrium point is numerically simulated and the sensitivity of the parameters of R 0 is analyzed. We also simulated the comparison of the number of asymptomatic infected people and symptomatic infected people before and after adopting the optimal control strategy. This shows that the infection of asymptomatic people cannot be underestimated in the spread of COVID-19 virus, and an isolation strategy should be adopted to control the spread speed of the disease. [ABSTRACT FROM AUTHOR]

Due to immunosuppression, transplant recipients are at higher risk of infections with SARS-CoV-2 and worse clinical outcomes than immunocompetent hosts. Furthermore, lung transplant patients represent a special group among solid organ recipients, since pneumonia is the main manifestation of COVID-19. However, data on the course of disease and the changes in morbidity and mortality during the course of the pandemic are limited. In our pulmonary rehabilitation clinic, we treat patients shortly after lung transplant as well as long-term transplant patients. Over the last almost 4 years of pandemic, we witnessed several COVID-19 infections in lung transplant patients in our clinic as well as patients who acquired an infection beforehand. In this paper, we aim at retrospectively describing a series of recent COVID-19 cases in our clinic, looking at the clinical course of disease and outcomes in lung transplant patients. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in millions of fatalities worldwide. The case of pediatric cancer patients stands out since, despite being considered a population at risk, few studies have been carried out concerning symptom detection or the description of the mechanisms capable of modifying the course of the COVID-19 disease, such as the interaction and response between the virus and the treatment given to cancer patients. By synthesizing existing studies, this paper aims to expose the treatment challenges for pediatric patients with COVID-19 in an oncology context. Additionally, this updated review includes studies that utilized the antiviral agents Remdesivir and PaxlovidTM in pediatric cancer patients. There is no specific treatment designed exclusively for pediatric cancer patients dealing with COVID-19, and it is advisable to avoid self-medication to prevent potential side effects. Managing COVID-19 in pediatric cancer patients is indeed a substantial challenge. New strategies, such as chemotherapy application rooms, have been implemented for children with cancer who were positive for COVID-19 but asymptomatic since the risk of disease progression is greater than the risk of complications from SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Several research papers have attempted to describe the dynamics of COVID-19 based on systems of differential equations. These systems have taken into account quarantined or isolated cases, vaccinations, control measures, and demographic parameters, presenting propositions regarding theoretical results that often investigate the asymptotic behavior of the system. In this paper, we discuss issues that concern the theoretical results proposed in the paper "An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter". We propose detailed explanations regarding the resolution of these issues. Additionally, this paper focuses on extending the local stability analysis of the disease-free equilibrium, as presented in the aforementioned paper, while emphasizing the derivation of theorems that validate the global stability of both epidemic equilibria. Emphasis is placed on the basic reproduction number R 0 , which determines the asymptotic behavior of the system. This index represents the expected number of secondary infections that are generated from an already infected case in a population where almost all individuals are susceptible. The derived propositions can inform health authorities about the long-term behavior of the phenomenon, potentially leading to more precise and efficient public measures. Finally, it is worth noting that the examined paper still presents an interesting epidemiological scheme, and the utilization of the Kalman filtering approach remains one of the state-of-the-art methods for modeling epidemic phenomena. [ABSTRACT FROM AUTHOR]

Losing a child is a traumatic event, disrupting life's natural cycle, profoundly affecting the family system, and causing enduring grief. Perinatal death, including ectopic pregnancies, miscarriages, stillbirths, and neonatal deaths, exacerbates this distress. Additionally, the COVID-19 pandemic has challenged healthcare systems and supporting services available to individuals in need. Thus, this research explores experiences of parents facing perinatal loss in 2020–2021, further focusing on the pandemic's impact. Using a mixed-methods design with self-reports and qualitative interviews, this paper presents results from the quantitative protocol, involving an update and follow-up of a previous study. It compares measurements across scales: COVID-19: The Impact of Event Scale-Revised; The Prolonged Grief-13; The Parental Assessment of Paternal/Maternal Affectivity; The Dyadic Adjustment Scale (short version); The Daily Spiritual Experiences Scale; and The Inventory of Complicated Spiritual Grief. In the baseline measurement, 45 parents participated (37 mothers and 8 fathers), with 20 (13 mothers and 7 fathers) contributing to the follow-up and 9 engaging in interviews. Baseline results showed higher scores for mothers compared to fathers, with effect sizes ranging from small to medium (ranging from −0.02 to 0.29), though statistical significance was limited due to the small sample size. Multiple regression analysis for distress measures at baseline identified two significant predictors: maternal/paternal affectivity and gestational week. Additionally, positive support from healthcare professionals emerged as a mitigating factor, particularly in relation to Avoidance. A significant reduction in stress measures and parental affectivity was observed at the 6-month follow-up. Qualitative analysis revealed three themes: Shifts in Self-Perception and Post-Loss Growth; Conflicted Relationship with One's Body; and Negative Impact of COVID-19 vs. Unexpectedly Positive Aspects. In conclusion, the findings emphasize the significance of psychological and psychosocial interventions based on meaning-making processes, along with the importance of spiritual care and empowerment for those navigating perinatal loss. [ABSTRACT FROM AUTHOR]

The purpose of this research is to examine whether Buddhist ethics can improve the health and well-being of Buddhist practitioners during pandemics like COVID-19. It is hypothesized that diseases are part of suffering, and Buddhist teachings aim to eliminate the suffering of all beings. Buddhism also offers ethical codes of conduct for its practitioners to improve their health and well-being. So, the Buddha's teaching or Buddhist ethics, when practiced seriously, should be able to improve one's health, physically and mentally, enhancing the well-being of all Buddhist practitioners during the spread of all pandemics including COVID-19. The present study found that Buddhist ethical practices like the chanting of Buddhist suttas and the development of mindfulness, concentration/meditation, and insight (vipassanā) can improve both physical and mental health, which are important for dealing with any pandemic, tremendously. If the cores of Buddhist ethics (morality, meditation, and wisdom) are perfectly practiced, not only will one live with good physical and mental health but one also will be able to eliminate all the mental defilements that are the root causes of all illnesses and thus enter nibbāna, the state of mind that is beyond all sources of suffering including pandemics/epidemics. More specifically, this paper highlights a set of Buddhist practices, called four bhāvanās (types of development), that can be used to improve health and well-being during any pandemic. [ABSTRACT FROM AUTHOR]

The process of SARS-CoV-2 infection, responsible for the COVID-19 pandemic, is carried out through different steps, with the interaction between ACE2 and Spike protein (S) being crucial. Besides of that, the acidic environment of endosomes seems to play a relevant role in the virus uptake into cells and its intracellular replication. Patients affected by two rare genetic tubulopathies, Gitelman's and Bartter's Syndromes, and a rare genetic metabolic disease, Fabry Disease, have shown intrinsic protection from SARS-CoV-2 infection and COVID-19 on account of specific intrinsic features that interfere with the virus uptake into cells and its intracellular replication, which will be reported and discussed in this paper, providing interesting insights for present and future research. [ABSTRACT FROM AUTHOR]

This paper examines the relationships between the consumption of 'bad' or 'good' digital economic news and attitudes toward immigrant and ethnic minorities during the crisis that developed during the COVID-19 pandemic. The study considered attitudes toward two minority groups in Israel: immigrant citizens from English-speaking countries, and Israeli Palestinian citizens, an ethnic minority. The data were collected through an online survey of 866 respondents, who were members of the majority population group. The study found that, during the global crisis, exposure to bad digital news was associated with more positive attitudes toward both disadvantaged and non-disadvantaged minority groups. Moreover, in times of global crisis, people focused mostly on local rather than global digital news. In contrast to the idea of Intergroup Threat Theory, the study revealed that feelings of economic threat during the global crisis engendered higher cohesion between different population groups, and more positive attitudes toward minorities. In times of crisis, bad news for the economy brings good news for social solidarity—people tend to rally around the flag; this phenomenon even occurs between groups engaged in years-long, protracted conflict. [ABSTRACT FROM AUTHOR]

Background: COVID-19 caused a pandemic, due to its ease of transmission and high number of infections. The evolution of the pandemic and its consequences for the mortality and morbidity of populations, especially the elderly, generated several scientific studies and many research projects. Among them, we have the Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO) research project. For such a project with many data records, it is necessary to provide a smooth graphical analysis to extract value from it. Methods: In this paper, we present the development of a full-stack Web application for the PREMO project, consisting of a dashboard providing statistical analysis, data visualization, data import, and data export. The main aspects of the application are described, as well as the diverse types of graphical representations and the possibility to use filters to extract relevant information for clinical practice. Results: The application, accessible through a browser, provides an interactive visualization of data from patients admitted to the intensive care unit (ICU), throughout the six waves of COVID-19 in two hospitals in Lisbon, Portugal. The analysis can be isolated per wave or can be seen in an aggregated view, allowing clinicians to create many views of the data and to study the behavior and consequences of different waves. For instance, the experimental results show clearly the effect of vaccination as well as the changes on the most relevant clinical parameters on each wave. Conclusions: The dashboard allows clinicians to analyze many variables of each of the six waves as well as aggregated data for all the waves. The application allows the user to extract information and scientific knowledge about COVID-19's evolution, yielding insights for this pandemic and for future pandemics. [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]

Background: Personal emotions and affects have been identified and studied in the context of pandemics, as well as coping strategies centered on emotional regulation or the balance between positive and negative emotions. Objectives: The objectives of this paper are to identify an emotion and affect structure in our sample and analyze the relationship of these dimensions with resilient coping in the context of the COVID-19 pandemic. Method: This study employed a cross-sectional design that involved a non-probabilistic sample with 598 participants over the age of 18, with 51.1% being female, and an average age of 40.73 years. First, the emotional structure was identified through principal component analysis (PCA). Secondly, a linear regression analysis was performed to investigate emotional dimensions as predictors of coping. Results: A valid and reliable emotional structure with four dimensions was identified. The regression model revealed that coping is positively associated with the active and positive dimension and negatively correlated with the negative and moral dimensions. Conclusions: Emotional dimensions are predictors of coping, with moral and negative dimensions having a negative effect, while active and positive dimensions have a positive effect. When designing interventions for coping strategies, multiple dimensions of emotions and affective states in people who are in vulnerable situations must be considered. [ABSTRACT FROM AUTHOR]

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for coronavirus disease-19 (COVID-19). This virus has caused a global pandemic, marked by several mutations leading to multiple waves of infection. This paper proposes a comprehensive and integrative mathematical approach to the third wave of COVID-19 (Omicron) in the Kingdom of Saudi Arabia (KSA) for the period between 16 December 2022 and 8 February 2023. It may help to implement a better response in the next waves. For this purpose, in this article, we generate a new mathematical transmission model for coronavirus, particularly during the third wave in the KSA caused by the Omicron variant, factoring in the impact of vaccination. We developed this model using a fractal-fractional derivative approach. It categorizes the total population into six segments: susceptible, vaccinated, exposed, asymptomatic infected, symptomatic infected, and recovered individuals. The conventional least-squares method is used for estimating the model parameters. The Perov fixed point theorem is utilized to demonstrate the solution's uniqueness and existence. Moreover, we investigate the Ulam–Hyers stability of this fractal–fractional model. Our numerical approach involves a two-step Newton polynomial approximation. We present simulation results that vary according to the fractional orders (γ) and fractal dimensions (θ), providing detailed analysis and discussion. Our graphical analysis shows that the fractal-fractional derivative model offers more biologically realistic results than traditional integer-order and other fractional models. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) was a global pandemic with high mortality and morbidity that led to an increased health burden all over the world. Although the virus mostly affects the pulmonary tract, cardiovascular implications are often observed among COVID-19 patients and are predictive of poor outcomes. Increased values of myocardial biomarkers such as troponin I or NT-proBNP were proven to be risk factors for respiratory failure. Although the risk of acute coronary syndromes (ACSs) was greater in the acute phase of COVID-19, there were lower rates of hospitalization for ACSs, due to patients' hesitation in presenting at the hospital. Hospitalized ACSs patients with COVID-19 infection had a prolonged symptom-to-first-medical-contact time, and longer door-to-balloon time. The mechanisms of myocardial injury in COVID-19 patients are still not entirely clear; however, the most frequently implicated factors include the downregulation of ACE2 receptors, endothelial dysfunction, pro-coagulant status, and increased levels of pro-inflammatory cytokines. The aim of this paper is to evaluate the long-term outcomes and prognosis of COVID-19 survivors that presented an acute myocardial infarction, by reviewing existing data. The importance of the association between this infectious disease and myocardial infarction arises from the increased mortality of patients with SARS-CoV-2 infection and AMI (10–76%, compared with 4.6% for NSTEMI patients and 7% for STEMI patients without COVID-19). The literature review showed an increased risk of cardiovascular events in COVID-19 survivors compared with the general population, even after the acute phase of the disease, with poorer long-term outcomes. [ABSTRACT FROM AUTHOR]

The COVID-19 lockdown has had serious consequences, including rethinking higher education. The study aims to enhance the knowledge base of online education and academic integrity through a case study of the Ludovika University of Public Service (LUPS), Budapest, Hungary. The research aimed to assess the teachers' experience with distance learning and examinations, including the change in workload, digital competencies, Moodle, Turnitin, and other software used during and after the lockdown. This paper summarizes the university-level policy changes induced during the lockdown, covering the introduction of emergency distance teaching and online examinations in academic integrity at the university. Two years after the first lockdown, the researchers made a survey (n = 145) about the continuation of the introduced solutions. The results show that a remarkable reordering started while the technical and technological backgrounds were available for the changes. The teachers could feel a significant increase in workload with distance education and have low trust in maintaining the standards of academic integrity. However, the research shows moderate and low levels of digital competencies in the majority of teachers, which clearly defines the most crucial task leading to success. Maintaining the monitoring system with objective indicators of the development and the opinions of the interested parties is essential for successful strategies in the field. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has brought to the forefront the importance of predicting and controlling an epidemic outbreak with policies such as vaccination or reducing social contacts. This paper studies an SIHR epidemic model characterized by susceptible (S), non-seriously infected (I), hospitalized (H), and recovered (R) subpopulations, and dynamic vaccination; vaccination itself and H are fed back, and its dynamics are also determined by a free-design time-dependent function and parameters. From a theoretical analysis, the well-posedness of the model is demonstrated; positivity and the disease-free ( P d f ) and endemic ( P e e ) equilibrium points are analyzed. The controlled reproduction number ( R c ) is proved to be a threshold for the local asymptotic stability of  P d f  and the existence  P e e ; when  R c < 1  ( R c > 1 ), then  P d f  is (not) locally asymptotically stable and  P e e  does not (does) exist. Simulations have been carried out with data concerning COVID-19 where the importance of keeping  R c < 1  to prevent the disease spreading and future deaths is highlighted. We design the control input, since it can be easily adapted to match the user specification, to obtain impulsive and regular vaccination and fulfill the condition  R c < 1 . [ABSTRACT FROM AUTHOR]

The lockdown and de-escalation process following the COVID-19 pandemic led to a period of new normality. This study aimed to assess the confinement impact on the mental health of peripartum women, as their psychological well-being may be particularly vulnerable and thus affect their offspring's development. A cross-sectional epidemiological study was conducted among women who gave birth during strict confinement (G0) and the new normality period (G1), in which a self-administered paper-based questionnaire assessed 15 contextual factors and the General Health Questionnaire-12 (GHQ-12). For each item, it was verified whether the positive screening rate differed in each confinement phase, and a risk factor study was conducted. For G0, significantly higher positive screening and preterm birth rates were observed in the positive screening group. In the case of G1, maternal age (>35 years), decreased physical activity, and normal weight were found to be protective factors against distress. This study underscores the heightened mental health risk for postpartum women during major psychosocial upheavals (war, economic crisis, natural disasters, or pandemics), along with their resilience as the positive screening rate decreases with the new normality. Findings encourage adopting strategies to identify high-risk women and promote effective measures, such as promoting physical activity. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method's effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations. [ABSTRACT FROM AUTHOR]