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Mental health is deteriorating far and fast globally post-COVID. Though there were already over one billion people living with mental disorders pre-pandemic, in the first year of COVID-19 alone, the prevalence of anxiety and depression soared by 25% worldwide. In light of the chronic shortages of mental health resources and talents, along with disruptions of available health services caused by pandemic-related restrictions, technology is widely believed to hold the key to addressing the rising mental health crises. However, hurdles such as fragmented and oftentimes suboptimal patient protection measures substantially undermine technology's potential to address the global mental health crises reliably and at scale. To shed light on these issues, this paper aims to discuss the post-pandemic mental health challenges and opportunities, and the strategies and solutions the global mental health community could leverage to protect and elevate society's mental health in the long run. [ABSTRACT FROM AUTHOR]

A situation of emergency involving the whole population introduces changes in the dynamics of the health services that are provided. The magnitude of these shifts should be also linked to the medical speciality within which the health benefits are delivered. The aim of the paper was to identify changes in tendencies in inpatient medical service delivery during the COVID-19 pandemic by medical specialties. On the basis of a database extracted for in-patient treatment received from the public payer, a retrospective analysis was carried out. Comparing the values of the dynamics of the services provided in each medical speciality, the period before the COVID-19 pandemic was collated to the years of the pandemic (2020–2021). In the period before COVID-19, positive patient dynamics were observed in more than half of the specialities. Between 2020 and 2021, virtually all specialties reversed the trend and negative dynamics were recorded. The dynamics in 2021 indicate a process of return to the values from 2015 to 2019. Emergency situation has affected the dynamics of healthcare provision in different specialities to various extent. The most resistant to the negative impulses of the state of emergency were the areas that are strictly organisationally and financially defined (e.g. the group of "therapeutic and drug programmes"). [ABSTRACT FROM AUTHOR]

The COVID-19 virus caused a global pandemic leading to a swift policy response. While this response was designed to prevent the spread of the virus and support those with COVID-19, there is growing evidence regarding measurable impacts on non-COVID-19 patients. The paper uses a large dataset from administrative records of the Brazilian public health system (SUS) to estimate pandemic spillover effects in critically ill health care delivery, i.e. the additional mortality risk that COVID-19 ICU hospitalizations generate on non-COVID-19 patients receiving intensive care. The data contain the universe of ICU hospitalizations in SUS from February 26, 2020 to December 31, 2021. Spillover estimates are obtained from high-dimensional fixed effects regression models that control for a number of unobservable confounders. Our findings indicate that, on average, the pandemic increased the mortality risk of non-COVID-19 ICU patients by 1.296 percentage points, 95% CI 1.145–1.448. The spillover mortality risk is larger for non-COVID patients receiving intensive care due to diseases of the respiratory system, diseases of the skin and subcutaneous tissue, and infectious and parasitic diseases. As of July 2023, the WHO reports more than 6.9 million global deaths due to COVID-19 infection. However, our estimates of spillover effects suggest that the pandemic's total death toll is much higher. [ABSTRACT FROM AUTHOR]

Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread. [ABSTRACT FROM AUTHOR]

Recent pandemic outbreaks, including the COVID-19 and SARS, have revealed that supply chains (SCs) are unable to respond to such disasters. To mitigate the destructive impacts and improve the performance of SCs, Operations Research (OR) techniques have been applied to address the issues over the last two decades. The objective of this paper is to develop a network data envelopment analysis (NDEA) model to measure the resilience and sustainability of healthcare SCs in response to the COVID-19 pandemic outbreak. In the proposed NDEA model, for the first time, outputs' weak disposability, chance-constrained programming (CCP), the convexity assumption, and the semi-oriented radial approach are aggregated. Moreover, a modified directional distance function (DDF) measure is developed to measure the overall and divisional efficiency scores. Furthermore, the proposed model can deal with different types of data such as integer-valued data, negative data, stochastic data, ratio data, and undesirable outputs. Also, several useful and interesting properties of the novel efficiency measure are presented. Finally, we measure the performance of 28 healthcare SCs to demonstrate the applicability and capability of our proposed approach. [ABSTRACT FROM AUTHOR]

One of the most important goals of modern medicine is prevention against pandemic and civilization diseases. For such tasks, advanced IT infrastructures and intelligent AI systems are used, which allow supporting patients' diagnosis and treatment. In our research, we also try to define efficient tools for coronavirus classification, especially using mathematical linguistic methods. This paper presents the ways of application of linguistics techniques in supporting effective management of medical data obtained during coronavirus treatments, and possibilities of application of such methods in classification of different variants of the coronaviruses detected for particular patients. Currently, several types of coronavirus are distinguished, which are characterized by differences in their RNA structure, which in turn causes an increase in the rate of mutation and infection with these viruses. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain. [ABSTRACT FROM AUTHOR]

Over the course of this year, more than a billion people have been afflicted by the COVID-19 outbreak. As long as individuals maintain their social distance, they should all be secure at this period. Because of this, there has been a rise in the usage of different online technologies, but at the same time, there has also been a rise in the likelihood of different cyber-attacks. A DDoS assault, the most prevalent and deadly of them all, impairs an online resource for its users. Thus, in this paper, we have proposed a filtering approach that can work efficiently in the COVID-19 scenario and detect the DDoS attack. We base our proposed approach on statistical methods like packet score and entropy variation for the identification of DDoS attack traffic. We have implemented our proposed approach on Omnet++ and for testing its efficiency we have checked it with different test cases. Our proposed approach detects the DDoS attack traffic with 96% accuracy and can also clearly have differentiated the DDoS attack traffic from the flash crowd. [ABSTRACT FROM AUTHOR]

Solidarity between more and less vulnerable groups is fundamental to an effective public health response to a global pandemic. Yet in the case of COVID-19, a focus on deciding who can and who cannot be protected from harm has shaped the pandemic experience and continues to determine the post-pandemic trajectory of life with SARS-CoV-2. In this paper I discuss how this has affected our understanding and acceptance of solidarity. [ABSTRACT FROM AUTHOR]

In this paper we analyze the impact of vaccinations on spread of the COVID-19 virus for different age groups. More specifically, we examine the deployment of vaccines in the Nordic countries in a comparative analysis where we focus on factors such as healthcare stress level and severity of disease through new infections, hospitalizations, intensive care unit (ICU) occupancy and deaths. Moreover, we analyze the impact of the various vaccine types, vaccination rate on the spread of the virus in each age group for Denmark, Finland, Iceland, Norway and Sweden from the start of the vaccination period in December 2020 until the end of September 2021. We perform a threefold analysis: (i) frequency analysis of infections and vaccine rates by age groups; (ii) rolling correlations between vaccination strategies, severity of COVID-19 and healthcare stress level and; (iii) we also employ the epidemic Renormalization Group (eRG) framework. The eRG is used to mathematically model wave structures, as well as the impact of vaccinations on wave dynamics. We further compare the Nordic countries with England. Our main results outline the quantification of the impact of the vaccination campaigns on age groups epidemiological data, across countries with high vaccine uptake. The data clearly shows that vaccines markedly reduce the number of new cases and the risk of serious illness. [ABSTRACT FROM AUTHOR]

This paper dwells upon COVID-19-related efforts of the Center for Sports Medicine, Federal Medical and Biological Agency of Russia. The Agency has the following precautions in place: regular polymerase chain reaction (PCR) testing of athletes and staff; double PCR testing before going to training camps or medical examinations; isolating athletes and their traceable contacts when COVID is suspected; observation and isolation wards set up at training camp venues. Athlete vaccination has begun. Athletes are provided online advice on health, diet, and exercising plus special care for chronically ill athletes and remote psychological counseling. Athletes recovering from COVID-19 are offered rehabilitation programs and doctor-supervised return to training. Specialists of the Research Department at FMBA's Center for Sports Medicine carried out a research dedicated to the prevalence of COVID-19 and different variants of its course in Russian athletes. The study period lasted from March to December 2020. A total of 27,438 records were analyzed. In May, June, July and August 2020, the percentage of positive PCR tests for athletes was significantly lower than the nationwide percentage at p < 0.05, Pearson's chi-squared test. However, the differences were nullified by September–October. The disease was mild or asymptomatic in most patients. Athletes of summer sports were found to be most likely to contract COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 (SARS-CoV-2 virus) pandemic has led to a substantial loss of human life worldwide by providing an unparalleled challenge to the public health system. The economic, psychological, and social disarray generated by the COVID-19 pandemic is devastating. Public health experts and epidemiologists worldwide are struggling to formulate policies on how to control this pandemic as there is no effective vaccine or treatment available which provide long-term immunity against different variants of COVID-19 and to eradicate this virus completely. As the new cases and fatalities are recorded daily or weekly, the responses are likely to be repeated or longitudinally correlated. Thus, studying the impact of available covariates and new cases on deaths from COVID-19 repeatedly would provide significant insights into this pandemic's dynamics. For a better understanding of the dynamics of spread, in this paper, we study the impact of various risk factors on the new cases and deaths over time. To do that, we propose a marginal-conditional based joint modelling approach to predict trajectories, which is crucial to the health policy planners for taking necessary measures. The conditional model is a natural choice to study the underlying property of dependence in consecutive new cases and deaths. Using this model, one can examine the relationship between outcomes and predictors, and it is possible to calculate risks of the sequence of events repeatedly. The advantage of repeated measures is that one can see how individual responses change over time. The predictive accuracy of the proposed model is also compared with various machine learning techniques. The machine learning algorithms used in this paper are extended to accommodate repeated responses. The performance of the proposed model is illustrated using COVID-19 data collected from the Texas Health and Human Services. [ABSTRACT FROM AUTHOR]

The proposed model in "Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare", improves the medical image classification and optimization for better disease diagnosis. The manuscript entitled "Deep learning on digital mammography for expert-level diagnosis accuracy in breast cancer detection", proposes a breast cancer detection system using deep learning. The results obtained within the scope of the study indicate that diversifying X-ray images with tissue analysis methods in the diagnosis of COVID-19 and including CNN input provides significant improvements in the results. Text, radiological pictures, audio notes, video, and other types of multimedia healthcare data are all generated by today's smart healthcare system [[1]]. [Extracted from the article]

Public health officials promoted COVID-19 vaccines to limit burdens placed on the U.S. healthcare system and end the pandemic. People in some closed religious communities refused to vaccinate and likely acquired temporary immunity through infection. This paper compares the death rates in Amish, Old Order Mennonites, and conservative Mennonite groups to a rate estimated for the U.S. population. Approximately two-thirds of the U.S. population was immunized against COVID-19, while few in the Amish/Mennonite community were. We find divergent patterns. Once vaccines became available, excess deaths declined in the general population and remained elevated among Amish and Mennonites. Vaccination campaigns must consider and value the cultural beliefs of closed religious communities to be effective. [ABSTRACT FROM AUTHOR]

One of the driving concerns during any epidemic is the strain on the healthcare system. As we have seen many times over the globe with the COVID-19 pandemic, hospitals and ICUs can quickly become overwhelmed by cases. While strict periods of public health mitigation have certainly helped decrease incidence and thus healthcare demand, vaccination is the only clear long-term solution. In this paper, we develop a two-module model to forecast the effects of relaxation of non-pharmaceutical intervention and vaccine uptake on daily incidence, and the cascade effects on healthcare demand. The first module is a simple epidemiological model which incorporates non-pharmaceutical intervention, the relaxation of such measures and vaccination campaigns to predict caseloads into the Fall of 2021. This module is then fed into a healthcare module which can forecast the number of doctor visits, the number of occupied hospital beds, number of occupied ICU beds and any excess demand of these. From this module, we can also estimate the length of stay of individuals in ICU. For model verification and forecasting, we use the four most populous Canadian provinces as a case study. [ABSTRACT FROM AUTHOR]

This paper aims to explore how the Hellenic Open University managed to adapt to the new normal of the pandemic and particularly what methods it deployed in its postgraduate Healthcare Management program. The first part introduces the key features of the Hellenic Open University and analyzes the strategic policy deployed by the University during the pandemic through the pillars of academic integrity, openness and excellence. The second part presents the research method and findings regarding both instructors and students of the Healthcare Management postgraduate program. The third part concludes that the policy introduced by University had a positive effect overall both to academic staff and healthcare practitioners. The importance and innovation of this study lies in the fact that it emphasizes on adult students who are in their crashing majority healthcare practitioners and thus have already a saying regarding the pandemic's management. The scope of the research was to demonstrate that this student audience had a significant input in the teaching procedure, since they were at the forefront of the pandemic crisis. In this regard, the instructor-student relationship was more than ever reciprocal based on the knowledge of the first and the experience of the latter. [ABSTRACT FROM AUTHOR]

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare. [ABSTRACT FROM AUTHOR]

Since it emerged in December of 2019, COVID-19 has placed a huge burden on medical care in countries throughout the world, as it led to a huge number of hospitalizations and mortalities. Many medical centers were overloaded, as their intensive care units and auxiliary protection resources proved insufficient, which made the effective allocation of medical resources an urgent matter. This study describes learned survival prediction models that could help medical professionals make effective decisions regarding patient triage and resource allocation. We created multiple data subsets from a publicly available COVID-19 epidemiological dataset to evaluate the effectiveness of various combinations of covariates—age, sex, geographic location, and chronic disease status—in learning survival models (here, "Individual Survival Distributions"; ISDs) for hospital discharge and also for death events. We then supplemented our datasets with demographic and economic information to obtain potentially more accurate survival models. Our extensive experiments compared several ISD models, using various measures. These results show that the "gradient boosting Cox machine" algorithm outperformed the competing techniques, in terms of these performance evaluation metrics, for predicting both an individual's likelihood of hospital discharge and COVID-19 mortality. Our curated datasets and code base are available at our Github repository for reproducing the results reported in this paper and for supporting future research. [ABSTRACT FROM AUTHOR]

Health service psychology internship directors confronted a myriad of training challenges in response to the COVID-19 pandemic. Specifically, internship training directors were tasked with ensuring that interns received appropriate training in each of the nine profession-wide competency (PWCs) areas while also ensuring the physical and emotional well-being of staff and trainees. The purpose of this paper is to describe one internship's approach to adapting the nine PWCs during COVID-19 in the context of an academic medical setting. Successes and challenges associated with training adaptations in the context of each of the nine PWCs will be shared along with considerations for improving academic medical training programs' ability to support interns' educational goals and training requirements throughout and following the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Constraints imposed due to the cameo of the novel corona virus has abruptly changed the operative mode of medical sciences. Most of the hospitals have migrated towards the telemedicine mode of services for the non-invasive and non-emergency patients during the COVID-19 time. The advent of telemedicine services has remotely rendered health services to different types of patients from their isolates. Here, the patients' medical data has to be transmitted to different physicians/doctors in a safe manner. Such data are to be secured with a view to restore its privacy clause. Cardio vascular diseases (CVDs) are a kind of cardiac disease related to blockage of arteries and veins. Cardiac patients are more susceptible to the COVID-19 attacks. They are advised to be treated though cardiac telemedicine services. This paper presents an intelligent and secured transmission of clinical cardiac reports of the patients through recurrence relation based session key. Such reports were made through the following confusion matrix operations. The beauty of this technique is that confusion matrices are transferred to specified number of cardiologists with additional secret shares encapsulation. The case of robustness checking, transparency and cryptographic engineering has been tested under different set of inputs. The total cryptographic time observed here was noted as 469.92 ms, 3 ms 74.45 , 502.88 ms, 361.38 ms, 493.12 ms, and 660.16 ms, which is acceptable when compared with other classical techniques. The estimation of correlation coefficient in proposed variables has been recorded as − 0.362 . Different types of result and its analysis proves the efficiency of the proposed technique. It will provide more security in medical data transmission, especially in the needy hours of COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Purpose: The pandemic SARS-CoV-2 poses new and unprecedented challenges for health care systems on a national and global level. Although the current situation has been going on for more than 1 year, there is limited data on the impact of the pandemic on general hospital and medical practice care. This survey captures the perspective of patients with gynaecological diseases of this impact. Methods: Using a paper-based questionnaire, 327 patients were asked about medical care and their experiences during the pandemic at the University Hospital Bonn and the University Hospital Charité Berlin. The study was performed from the 1st June to 30th September 2020. Results: A total of 327 patients participated in the study: 156 stated to have been tested for coronavirus, and 1 patient reported a positive test. 41.3% of the patients felt insecure about the current situation, 30.4% were concerned about the risk of infection during the hospital stay. The pandemic-specific measures in hospitals and medical practices unsettled 6.8% of patients. 18.1% of patients feared that their gynaecological disease would not be treated adequately due to the pandemic. 55.7% of patients reported that their confidence in their physicians has increased during the pandemic. Conclusion: The results show that patients' confidence in the healthcare system and the physicians acting significantly increased during the COVID-19 crisis. Transparent and comprehensive information policy regarding actions and restrictions within the COVID-19 crisis eases patients concerns and improves patients' confidence in their physicians, which is crucial for a successful treatment's outcome. [ABSTRACT FROM AUTHOR]

Purpose: In March 2020, many state, local, and national governments declared various states of emergencies in response to the COVID-19 pandemic. In Massachusetts, where our multidisciplinary pediatric feeding clinic is located, the governor declared of a state of emergency encouraging social distancing, and simultaneously signed an order establishing reimbursement parity for telehealth visits to in-office traditional visits by both commercial and state health insurers. This presented a challenge and an opportunity for our multidisciplinary program for children with pediatric feeding disorders embedded in a large academic children's hospital. In this paper we aim to provide a roadmap for rapid implementation of telehealth practice without a reliance on in-person care in a multidisciplinary pediatric feeding clinic. Description: Within a week, the program pivoted from solely in-person care to 100% telehealth services for both new and established patients. Through this transition, the program encountered several challenges with technology, scheduling, licensing, and concerns for reinforcing pre-existing healthcare disparities. Assessment: The program quickly overcame many of these challenges and found telehealth to offer benefits to patients such as improved coordination of care with other agencies, reduced appointment times, and reduced travel time and travel cost. Even with a reduction in the number of patients seen per clinic due to the manner in which telehealth was implemented, there was an increase in the number of visits completed with a slight reduction in the no-show rate. Additionally, providers in the program are better able to evaluate feeding practices in the home and understand many of the barriers families may face in implementing interventions. While telehealth does have some challenges, it can help to improve access, communication, and may increase patient satisfaction for children who require multidisciplinary care for their pediatric feeding disorder. Conclusion: Our hope is that billing parity for telehealth will continue to be supported by insurance companies and state governments throughout the remainder of this pandemic, and far beyond. [ABSTRACT FROM AUTHOR]

This research aims to look into the effect of COVID-19 on emerging stock markets in seven of the Association of Southeast Asian Nations' (ASEAN-7) member countries from March 21, 2020 to April 31, 2020. This paper uses a ST-HAR-type Bayesian posterior model and it highlights the stock market of this ongoing crisis, such as, COVID-19 outbreak in all countries and related industries. The empirical results shown a clear evidence of a transition during COVID-19 crisis regime, also crisis intensity and timing differences. The most negatively impacted industries were health care and consumer services due to the Covid-19 drug-race and international travel restrictions. More so, study results estimated that only a small number of sectors are affected by COVID-19 fear including health care, consumer services, utilities, and technology, significance at the 1%, 5%, and 10%, that measure current volatility's reliance on weekly and monthly variables. Secondly, it is found that there is almost no chance that the COVID-19 pandemic would positively affect the stock market performance in all the countries, mainly Indonesia and Singapore were the countries most affected. Thirdly, results shown that Thailand's stock market output has dropped by 15%. Results shows that COVID-19 fear causes an eventual reason of public attention towards stock market volatility. The study presented comprehensive way forwards to stabilize movement of ASEAN equity market's volatility index and guided the policy implications to key stakeholders that can better help to mitigate drastic impacts of COVID-19 fear on the performance of equity markets. [ABSTRACT FROM AUTHOR]

In March 2020, the Portuguese Government issued a remarkable regulation by which irregular migrants who had previously started the regularization procedure were temporarily regularized and thus allowed full access to all social benefits, including healthcare. The Portuguese constitutional and legal framework is particularly generous regarding the right to healthcare to irregular migrants. Nevertheless, until now, several practical barriers prevented full access to healthcare services provided by the national health service, even in situations in which it was legally granted. This decision is not only remarkable in light of the fulfilling of migrants' rights to health, as imposed by international commitments assumed by, but also in view of the fight against COVID-19. The decision is grounded both on human rights and in public health reasons. The paper is divided in two main parts. In the first one, it analyzes national State obligations with regard to healthcare provision to migrants in irregular situation. In the second part, it analyzes the Portuguese solution, using this case study to discuss the possible mechanisms to comply with such obligations. [ABSTRACT FROM AUTHOR]

This paper uses secondary analysis to understand how COVID-19 shaped people's experiences with psychosocial support services in Australia. Data are drawn from questionnaires (n = 66) and semi-structured interviews (n = 62), conducted for a national service evaluation, with 121 people living with enduring mental health conditions and using psychosocial support services. Data relating to COVID-19 were inductively coded and analysed using constant comparative analysis. Most people's experiences included tele-support. While some people described minimal disruption to their support, many reported reduced engagement. People's wellbeing and engagement were influenced by: their location, living situation and pre-COVID lifestyles; physical health conditions; access to, comfort with, and support worker facilitation of technology; pre-COVID relationships with support workers; and communication from the organisation. The findings can help services prepare for future pandemics, adjust their services for a 'COVID-normal' world, and consider how learnings from COVID-19 could be incorporated into a flexible suite of service delivery options. [ABSTRACT FROM AUTHOR]

Background and objective: The COVID-19 pandemic started in Wuhan, China, in December 2019. Although there are some doubts about the reporting of cases and deaths in China, it seems that this country was able to control the epidemic more effectively than many other countries. In this paper, we would like to analyze the measures taken in China and compare them with other countries in order to find out what they can learn from China. Methods: We develop a system dynamics model of the COVID-19 pandemic in Wuhan. Based on a number of simulations we analyze the impact of changing parameters, such as contact rates, on the development of a second wave. Results: Although China's health care system seems to be poorly financed and inefficient, the epidemic was brought under control in a comparably short period of time and no second wave was experienced in Wuhan until today. The measures to contain the epidemic do not differ from what was implemented in other countries, but China applied them very early and rigorously. For instance, the consequent implementation of health codes and contact-tracking technology contributed to contain the disease and effectively prevented the second and third waves. Conclusions: China's success in fighting COVID-19 is based on a very strict implementation of a set of measures, including digital management. While other countries discuss relaxing the lock-down at a rate of 50 per 100,000 inhabitants, China started local lock-downs at a rate of 3 per 100,000. We call for a public debate whether this policy would be feasible for more liberal countries as well. [ABSTRACT FROM AUTHOR]

The current paper focuses on the circumstances that have led to the high COVID-19 infection rates amongst the ultra-Orthodox population in Israel. The current study utilizes a qualitative design and is based on in-depth interviews, email correspondence and online records of 25 ultra-Orthodox individuals who either tested positive for COVID-19 or had contact with a verified COVID-19 patient. The data were analyzed through identification of main themes and an interpretation of their meanings. The findings showed that a wide range of causes led to the high infection rate, including aspects that derive from a structural element, a religious element and a social-ideological element—all of which are directly or indirectly connected to religion. These findings demonstrate the central role of religion in health outcomes among the ultra-Orthodox community in general and during pandemics in particular, and they shed light on the central role of religion in health outcomes among closed-religious communities. The findings further reveal the importance of cooperation between the state authorities and the religious ones, and of providing culturally adapted health service solutions in the fight against COVID-19 and promoting health more generally. Study limitations are discussed and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

This paper presents a qualitative content analysis of survey data collected from behavioral health care providers from throughout New York regarding the challenges faced as a result of COVID-19. Survey responses from 295 agency and program administrators and staff, representing 238 organizations, were analyzed. Ten themes were identified: business operations, service provision, telehealth, safety, client concerns, staff concerns, supplies, technology, illness/grief/loss, and communication. These themes represent concerns that arose from the rapid transition to widespread use of telehealth, limited technology accessibility for both staff and clients, reduced revenue and billing changes, impact of COVID-19 infection itself and subsequent deaths of clients and staff, and necessary modifications for organizational communication both internally and externally. The implications of these challenges and the need for further research to identify how to best address them are discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques. [ABSTRACT FROM AUTHOR]

Background: In March, 2020, the World Health Organization declared COVID-19 a pandemic. The absence of previous knowledge of COVID-19 has made decision-making difficult for all in health care, including surgical departments. We reviewed the management recommendations for surgical activity and changes to surgical practice, identifying concordances and discrepancies, based on the literature published in the early phase of the pandemic. Method: We searched the electronic datasets, PubMed Database, Google, and Google Scholar, using the keywords "SARS-CoV-2", "COVID-19", "surgery", "recommendations", "guideline", and "triage". The search was limited to the first 2 months after the pandemic began and was closed on May 6, 2020. Results: Twenty papers were included in the analysis and their recommendations are divided into the following categories: 1. general aspects, such as maintaining the safety of health personnel and indications for surgery. 2. The preoperative phase, with recommendations about activating different care pathways for COVID-19 positive patients. 3. The operative phase, with recommendations about activating safety measures for aerosol-generating procedures. 4. The postoperative phase, with recommendations for managing operating theatres and patient transfers. Conclusion: The recommendations proposed in the revised documents are considered good practices aimed at keeping patients and healthcare professionals safe. However, these recommendations must be contextualized in each individual hospital. [ABSTRACT FROM AUTHOR]

Purpose: SARS-CoV-2's new scenario has forced health systems to work under extreme stress urging to perform a complete reorganization of the way our means and activities were organized. The orthopaedic and trauma units have rescheduled their activities to help SARS-CoV-2 units, but trauma patients require also treatment, and no standardized protocols have been established. Methods: A single-centre cross-sectional study was performed in a tertiary hospital. Two different periods of time were analyzed: a two week period of time in March 2019 (pre-SARS-CoV-2) and the same period in March 2020 (SARS-CoV-2 pandemic time). Outpatient's data, emergency activity, surgical procedures, and admissions were evaluated. Surgeons' and patient's opinion was also evaluated using a survey. Results: A total of ~ 16k (15,953) patients were evaluated. Scheduled clinical appointments decreased by ~ 22%. Urgent consultations and discharge from clinics also descended (~ 37% and ~ 20% respectively). Telemedicine was used in 90% of outpatient clinical evaluations. No elective surgical procedures during SARS-CoV-2 time were scheduled, and subtracting the effect of elective surgeries, there was a reduction of inpatient surgeries, from ~ 85% to ~ 59%. Patients delayed trauma assistance more than 48 hours in 13 cases (35%). Pre-operative admission for hip fractures decreased in ten hours on average. Finally, surveys stated that patients were more in favour than surgeons were to this new way to evaluate orthopaedic and trauma patients based strongly on telemedicine. Conclusion: Detailed protocols should be standardized for surgical departments during the pandemic. This paper offers a general view in how this virus affects an orthopaedic unit and could serve as a protocol and example for orthopaedic and trauma units. Even in the worst scenario, an orthopaedic and trauma unit could offer an effective, efficient, and quality service. SARS-CoV-2 will set up a new paradigm for health care in orthopaedics and trauma. [ABSTRACT FROM AUTHOR]

Purpose: This study examines immigrant Latinas' (ILs') help-seeking behaviors, types of support systems, and access to intimate partner violence (IPV) services during a global health crisis (COVID-19) at a community-based agency in a Northeastern state. Method: Nineteen immigrant Latinas who had prior IPV-related services such as legal aid, advocacy, and support within 1–3 years were recruited for the study. Spanish-speaking telephone interviews averaging between 30 and 45 min were conducted with each participant. Content analysis was the method employed to review the data and generate themes of the participants' experiences. Results: Participants' qualitative responses included an increase of intimate partner violence during the pandemic. Types of support systems included reaching out to police departments, hospitals and health-care settings, and community-based agencies. Findings indicated a 47% positive response rate when working with police officers (e.g., bilingual Spanish-English speaking police officers), and the participants reported being supported by the agency staff where they received services. Conclusion: Recommendations are provided to the community-based agency and other service providers regarding ongoing delivery of services and best practices for ILs throughout the pandemic transitions and beyond. [ABSTRACT FROM AUTHOR]

The Stability Community of the American Association of Pharmaceutical Scientists (AAPS) held a virtual workshop on "Vaccine Stability Considerations to Enable Rapid Development and Deployment", on March 24-25, 2021. The workshop included distinguished speakers and panelists from across the industry, academia, regulatory agencies, as well as health care leaders. This paper presents a review of the topics covered. Specifically the challenges in accelerating vaccine development and analytical characterization techniques to establish shelf-life were covered. Additionally, vaccine stability modeling using prior knowledge stability models and advanced kinetic analysis played a key in the EUA approaches discussed during the workshop. Finally, the role of stability studies in addressing the challenges of vaccine distribution and deployment during the pandemic were a focus of presentations and panel discussions. Although the workshop did not have any presentation topics directly dedicated to the mRNA vaccines, the techniques discussed are generally applicable. The mRNA vaccine developers were represented in the panel discussions, where experts involved in the EUA approval/deployment stages for this vaccine type could discuss the challenges as applied to their vaccines. [ABSTRACT FROM AUTHOR]

This study examines the level of preparedness exhibited through strategy, planning and organization to deal with the COVID-19 pandemic in Italy. A comparative analysis of four regions revealed that the implementation of pandemic plans was affected by multiple factors. For instance, some planning was outdated and insufficient to cope with the new threat posed by the pandemic; due to a decentralized health care system, there was confusion about whether regional or national decision-making was the coordinating actor; shortages in supplies and equipment such as masks, in some regions, were due to lack of implementation of existing pandemic plans. The study emphasizes the importance of a coordinated response to crises. [ABSTRACT FROM AUTHOR]

The ever-debated question of triage and allocating the life-saving ventilator during the COVID-19 pandemic has been repeatedly raised and challenged within the ethical community after shortages propelled doctors before life and death decisions (Anderson-Shaw and Zar 2020; Huxtable 2020; Jongepier 2020; Peterson, Largent, and Karlawish 2020). The British Medical Association's ethical guidance highlighted the possibility of an initial surge of patients that would outstrip the health system's ability to deliver care "to existing standards," where utilitarian measures have to be applied, and triage decisions need to maximize "overall benefit" (British Medical Association 2020, 3) In these emergency circumstances, triage that "grades according to their needs and the probable outcomes of intervention" will prioritize or eliminate patients for treatment, and health professionals may be faced with obligations to withhold or withdraw treatments to some patients in favour of others (British Medical Association 2020, 4). This piece is a response and extension to articles published on the manner of involvement for ethics and ethicists in pandemic triage decisions, particularly examining the ability and necessity of establishing triage committees to ameliorate scarce allocation decisions for physicians. [ABSTRACT FROM AUTHOR]

However, the challenge becomes how to take Big Data and translate it into information that healthcare professionals can use for decision making to improve healthcare outcomes and improve the quality of care. Rapid growth in IoT networks has revolutionized the healthcare industry by bringing major improvements in terms of e-health/medical records (EHR/EMR), prescription drug data, and insurance information. The fuzzy memberships are adopted to remove the impact of outliers, and Universum data are utilized to give prior information about data distribution. [Extracted from the article]

We study the effectiveness of non-pharmaceutical interventions (NPIs) against COVID-19 on the allocation of scarce resources in the hospital sector in Scandinavia. Denmark and Norway imposed strict NPIs, but Sweden followed an extraordinarily lenient approach. We use an event study to compare COVID-19 hospitalizations, intensive-care (ICU) patients, and deaths in Sweden with Denmark and Norway. The outcome variables initially follow a common trend, but diverge 2–3 weeks after lockdown. Both the timing of the effect and the similarity in the trend between Denmark and Norway are highly consistent with a causal effect of the lockdown. We use our event study to build a counterfactual model that predicts the outcome variables for Denmark and Norway if they had followed Sweden's approach. In the absence of strict NPIs, the peak number of hospitalizations would have been 2.5 (3.5) times as large in Denmark (Norway). Overall, Denmark (Norway) would have had 334 (671) percent more hospital-patient days, 277 (379) percent more ICU-patient days, and 402 (1015) percent more deaths. The benefit of lockdown in terms of healthcare and mortality costs amounts to between 1 and 4 (0.9 and 3.5) percent of GDP in Denmark (Norway). [ABSTRACT FROM AUTHOR]

The Rotterdam Study is an ongoing prospective, population-based cohort study that started in 1989 in the city of Rotterdam, the Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. It focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. In response to the COVID-19 pandemic, a substudy was designed and embedded within the Rotterdam Study. On the 20th of April, 2020, all living non-institutionalized participants of the Rotterdam Study (n = 8732) were invited to participate in this sub-study by filling out a series of questionnaires administered over a period of 8 months. These questionnaires included questions on COVID-19 related symptoms and risk factors, characterization of lifestyle and mental health changes, and determination of health care seeking and health care avoiding behavior during the pandemic. As of May 2021, the questionnaire had been sent out repeatedly for a total of six times with an overall response rate of 76%. This article provides an overview of the rationale, design, and implementation of this sub-study nested within the Rotterdam Study. Finally, initial results on participant characteristics and prevalence of COVID-19 in this community-dwelling population are shown. [ABSTRACT FROM AUTHOR]

The global outbreak of COVID-19 forced EU governments to implement drastic confinement measures to contain the spread of the Coronavirus. These measures, however, come at a high economic cost. In this work, we analyze the resilience/preparedness of public health systems, the confinement measures introduced by governments, and their socio-economic effects. We also investigate the relationships between these elements by focusing on the EU Member States. We conduct an after-action review (AAR) study based on three indices. The first index indicates the preparedness of the countries' health systems to deal with a potential health shock resulting from the COVID-19 outbreak. The second index shows the strictness of confinement measures taken per Member State in spring 2020. Finally, the third index captures the expected socio-economic effects of such measures on each country for the year 2020. Our findings show that on average, countries with less prepared health systems implemented stricter confinement measures and that higher levels of stringency in the confinement measures are associated with stronger, negative, socio-economic impacts. However, the results differ across countries in the case of each index. Overall, the results call for health systems to be better prepared to handle public health crises and for a more coordinated EU approach to overcome divergences across countries. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has increased the vulnerability of cancer patients to psychological symptoms, in addition to treatment modality issues. The present study has aimed to determine the psychological state of Arab cancer patients during the COVID-19 pandemic and to explore the contributing factors. A cross-sectional, descriptive, correlational design with the Hospital Anxiety and Depression Scale (HADS), Insomnia Severity Index (ISI), Functional Assessment of Cancer Therapy–Fatigue subscale (FACT-F), and the Functional Assessment of Cancer Therapy–General (FACT-G) were used to collect the data. Out of 291 participants, 60.5% were female; 22.7% were 51–60 years; 31.6% were diagnosed with breast cancer; 44.3% with stage 2 cancer; 23% had contracted COVID-19; and 43.3% had received their first dose of the vaccine. The main study findings are: 74.9% had anxiety, 64.3% experienced depression, 68% suffered from insomnia, and 69.8% had fatigue. Quality of life (QoL) was low among cancer patients who had marked anxiety, depression, insomnia, or fatigue, and these findings were statistically significant (p < 0.05). Predictive factors showed males were 5.09 times more likely to report depression; gynecological cancer patients were 40.4 times more likely to experience insomnia, and patients who had COVID-19 were 5.13 times more likely to report fatigue. The study findings reveal that the COVID-19 pandemic affected the psychological state of cancer patients and had a significant effect on their quality of life. Hence, there is a need to develop a holistic cancer health care approach, which is often limited in Arab countries, to help patients manage these symptoms effectively and to decrease the prevalence of mental health morbidity. [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze temporal changes in social needs (SN), comparing those who received routine annual in-person care to those receiving SN screenings through a combination of tele-social care and in-person care biannually. Our prospective cohort study used a convenience sample of patients from primary care practices. Baseline data were collected from April 2019 to March 2020. The intervention group (n = 336) received SN screening and referral telephone outreach from June 2020 to August 2021. The control group (n = 2890) was screened, in person, during routine visits at baseline and summer 2021. We used a repeated-measures logistic regression with general estimating equations to assess incremental change in individual SN for the intervention group. Food, housing, legal and benefit needs increased and peaked at the beginning of the pandemic and decreased after interventions (P < 0.001). There was a 32% decrease in the odds of food insecurity for those in the intervention group compared to the control group (adjusted OR 0.668, 95% confidence interval 0.444–1.004, P = 0.052), and a 75% decrease in the odds of housing insecurity (adjusted OR 0.247, 95% confidence interval 0.150–0.505, P < 0.001). During COVID-19, there was an increase in SN followed by a decrease after interventions were offered. Those who completed tele-social care showed greater improvements in social needs than those in routine care, with the greatest improvements in food and housing needs. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, public health authorities have encouraged the use of face masks to minimize transmission within the community. To assess mask wear during a COVID-19 surge and guide public health response efforts, including public messaging on mask recommendations, we compared observed mask use in the largest city in each of Idaho's 2 most populous counties, both without a current mask mandate. We recorded mask usage by every third person exiting stores of 5 retail chains in Boise and Nampa during November 8–December 5, 2021. Observations were conducted during three time periods (morning, afternoon, and evening) on weekday and weekend days. A multivariable model with city, retail chain, and city-chain interaction was used to assess mask wear differences by city for each chain. Of 3021 observed persons, 22.0% wore masks. In Boise, 31.3% (430/1376) of observed persons wore masks; in Nampa, 14.3% (236/1645) wore masks. Among all persons wearing masks, > 94% wore masks correctly; cloth and surgical masks were most common. By retail chain, observed individuals at Boise locations were 2.3–5.7 times as likely to wear masks than persons at respective Nampa locations. This study provided a rapid, nonconfrontational assessment of public use of mitigation measures in 2 Idaho cities during a COVID-19 surge. [ABSTRACT FROM AUTHOR]

This paper seeks to establish how COVID-19 is impacting migrant fish workers through focusing on two global fish hubs, Thailand and Taiwan. Through a careful review of the news reports, social media, and NGO reports and press releases, three significant themes emerged: employment disruptions due to seafood system instabilities; travel or mobility restrictions; and poor access to services such as health care or social programs. We unpack each theme in turn to spotlight the impacts COVID-19 is having on yet another vulnerable worker population, fish workers. We further reflect on what this pandemic reveals about unacceptable work in industrial fisheries and consider if the pandemic may be producing opportunities to advocate for better working conditions. [ABSTRACT FROM AUTHOR]

The current coronavirus disease 2019 (COVID-19) pandemic has caused significant strain on medical centers resources. Thus, concerns about the reducing and management of COVID-19 are on the rise, as there is need to provide diagnosis, treatment, monitoring, and follow-ups during the pandemic. Therefore, the COVID-19 pandemic has radically and quickly altered how medical practitioners provide care to patients. Medical centers are now responding to COVID-19 through rapid adoption of digital tools and technologies such as telemedicine and virtual care which refer to the delivery of healthcare services digital or at a distance using Information and Communications Technology (ICT) for treatment of patients. Telemedicine is expected to deliver timely care while minimizing exposure to protect medical practitioners and patients. Accordingly, a rapid literature review was conducted, and 35 research studies published from 2019 to May 2020 were employed to provide theoretical and practical evidence on the significance of using telemedicine and virtual care for remote treatment of patients during the COVID-19 pandemic. This article provides practical guide based on how to use telemedicine and virtual care during the COVID-19 pandemic. This study provides implication on the potentials of consolidating virtual care solutions in the near future towards contributing to integrate digital technologies into healthcare. [ABSTRACT FROM AUTHOR]

We used qualitative data from the Partners PrEP Program (PPP) to address the question: How did Central Ugandan HIV clinics adapt to COVID-19 lockdown restrictions to promote continuous access to HIV care? PPP was a stepped-wedge cluster randomized trial of integrated PrEP and ART delivery for HIV serodifferent couples at Central Ugandan HIV clinics (NCT03586128). Individual interviews with purposefully selected PPP couples (N = 42) and clinicians, coordinators, and counselors providing HIV care (N = 36) were carried out. Sixty-four interviews were completed after lockdown and included questions about accessing and providing ART/PrEP refills during lockdown restrictions. We used an inductive, content-focused approach to analyze these interview data. Barriers to continuous access identified by interviewees included loss of income with increased cost of transport, reduced staff at clinics, and physical distancing at clinics. Interviewees pointed to multi-month refills, visits to clinics "close to home," transport to clinics for providers, and delivery of refills in neighborhoods as factors promoting continuous access to antiretroviral medications. Access barriers appeared somewhat different for ART and PrEP. Fewer resources for community delivery and pre-refill HIV testing requirements were identified as PrEP-specific access challenges. Participants emphasized their success in continuing ART/PrEP adherence during the lockdown, while providers emphasized missed refill visits. These results highlight the contributions of providers and ART/PrEP users to adaptation of HIV services during COVID-19 lockdown restrictions in Uganda. The roles of direct care providers and service users as drivers of adaptation should be recognized in future efforts to conceptualize and investigate health system resiliency. [ABSTRACT FROM AUTHOR]

The article focuses on COVID-19 reinforces the need to improve sepsis care resources in Africa. Topics include the lower recorded burden across Africa is yet to be fully elucidated, the risk of disease and death associated with increased age or comorbidities, the lower testing capacity in some countries may result in under-diagnosis, and the not these factors or others, such as genetic factors or immune priming from exposure to other infections.

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based integer programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a general variable neighborhood search algorithm. We evaluate the IP model and the metaheuristic on various small-, medium- and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small- and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses. [ABSTRACT FROM AUTHOR]

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate. [ABSTRACT FROM AUTHOR]