Emotional risk work during the pandemic: Healthcare professionals' perceptions from a COVID-19 ward

In March 2020, COVID-19 wards were established in hospitals in Denmark. Healthcare professionals from a variety of specialities and wards were transferred to these new wards to care for patients admitted with severe COVID-19 infections. Based on ethnographic fieldwork in a COVID-19 ward at a hospital in Copenhagen, Denmark, including focus group interviews with nursing staff, we intended to explore practices in a COVID-19 ward by seeking insight into the relation between the work carried out and the professionals' ways of talking about it. We used a performative approach of studying how the institutional ways of handling pandemic risk work comes into being and relates to the health professionals' emerging responses. The empirical analysis pointed at emotional responses by the nursing staff providing COVID-19 care as central. To explore these emotional responses we draw on the work of Mary Douglas and Deborah Lupton's concept of the ‘emotion-risk-assemblage'. Our analysis provides insight into how emotions are contextually produced and linked to institutional risk understandings. We show that work in the COVID-19 ward was based on an institutional order that was disrupted during the pandemic, producing significant emotions of insecurity. Although these emotions are structurally produced, they are simultaneously internalised as feelings of incompetence and shame.

Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk: An International Journal of Medical Toxicology and Drug Experience

Introduction Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit-risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking. Objectives In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk. Methods The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included. Results Twenty-seven PRAC subgroup examples representing 1719 subgroup drug-event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected. Conclusions We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

The Influence of Density and Mixed Land Use on The Spread of Covid-19 in Surakarta City

Covid-19 has a significant risk of spreading in urban areas because of the aglomeration of built-up areas and people. It frequently contains a mix of land uses and is accessible to urban amenities. Due to the area's extensive usage of mixed land uses, it is better able to provide internal urban services on its own. Greater use of area lockdown and social separation strategies could result from this situation. The most populous city in the province of Central Java, Surakarta, has a significant risk of contracting COVID-19. The purpose of this study is to evaluate the impact of density and levels of mixed land use on the Covid-19 distribution in Surakarta City.Population density is used to calculate density. The entropy index approach was used to measure the amount of mixed land use. It is a method for calculating the balance between each form of land use. The availability of current land use data being processed by the spatial analysis with the Arc GIS application provided help for the analysis. Additionally, it makes use of information on Covid-19 cases in relation to the general populace that is supplied by the Surakarta Municipality. The relationship between mixed land use and Covid-19 risk was analyzed using a linear regression approach. The study's findings indicated a minor influence between density and the spread of COVID-19. Meanwhile, the level of mixed land use does not influence the spread of the Covid-19 virus in Surakarta City.

Investigation of Risk Factors Influencing the Safety of Maritime Containers Supply Chain: In the Period of the Pandemic

Maritime security is facing many challenges due to war conflicts, geopolitics, sanctions, and pandemics. The supply chain for maritime containers has faced considerable obstacles as a result of the COVID-19 pandemic. Numerous factors, such as port closures, travel restrictions, and a decreased workforce, have impacted the supply chain. The risk of cargo theft, piracy, and other security events has increased as a result of these difficulties. Therefore, it is essential to look at the risk variables that may affect the security of the marine container supply chain during the pandemic. This research paper highlights those risks through the following three indexes: the likelihood index (LI), severity index (SI), and average risk index (ARI) by analyzing 64 risk factors that were prepared and designed by incorporating the Delphi expert survey technique to prepare a systematic questionnaire. The article addresses worries over the COVID-19 pandemic's effects on international supply networks. The causes of the most recent global shipping industry disruptions and their impact on supply chains have been thoroughly examined. In order to reduce the number of disruptions in global supply chains and lower the direct and indirect costs for consumers, the authors have also mentioned the necessary actions that must be implemented. The results concluded after the analysis pointed to "management activities,” such as human resources or the working environment as having the highest possibility of going wrong, whereas "operation activities” were judged to likely be the fatal ones if the security of maritime containers was ever compromised. The main objective of the study is to evaluate how the COVID-19 epidemic may affect international shipping, particularly container shipping, which is currently the most important link in the world's multimodal land–sea supply chains.

From the Chairman

Early on, whispers of a potentially engineered virus grew and fueled speculation that China was behind the pandemic-speculation so pervasive that, in February 2020, a group of 27 public health scientists published a letter in The Lancet disputing the laboratory leak theory, and announcing their support of their counterparts in China-the scientists, public health officials, and medical professionals-combating the pandemic. Robert Malone, MD, the inventor of the mRNA technology, has expressed strong concern over the risk-benefit analysis of vaccinating young adults, and the Centers for Disease Control and Prevention's Advisory Committee on Immunization Practices has met to discuss cases of myocarditis or pericarditis in people aged 30 and younger who have received an mRNA Covid-19 vaccine. What we do know for certain is that the incredible strength and collaboration of the scientific community have allowed us to regain some semblance of normalcy.

An opinion paper on requirement for a practitioner's handbook designed for business continuity and resilience in government: a South African context

PurposeThis paper aims to find a suitable structure for a practitioner's handbook that addresses the structural elements of the business continuity (BC) practice.Design/methodology/approachThe case study using the mixed method, quantitative with a questionnaire and conceptual research approach was what has been chosen. The four steps to the research process are outlined: one, choosing the topic, two, collecting relevant literature, three, identifying specific variables and four, generating a structure. The design brought on by years of experience, should be put into an organised system and handbook that can be reused, without having to reinvent the wheel.FindingsA BC handbook should be as relevant to the executives and management as to their employees. By adopting a BC practice in a government department, state-owned entity, agency or municipality. Assurance will be ascertained for reliable, improved service delivery and reputation with much less interruption. Therefore a handbook with a "cradle to the grave” BC approach should outline, with examples of standards, awareness, policy, BC programme plan, BC structures, business impact and risk analysis, strategy, budgets, scorecards, monitoring and evaluation, recovery and BC plans, together with the audit and an International Standards Organization (ISO) 22301 certification process.Research limitations/implicationsThe research was limited to literature, questionnaires and identified variables pertaining to BC management (BCM) in the South African Government.Practical implicationsThe implications of the case study is that out of the variables identified and the relevant literature and standards, a structure for a relevant post-COVID-19 government practitioner's handbook could be made available.Social implicationsThe use of a BCM handbook for government would assist in the continuation of services through manmade and natural disasters. The service to the citizen, including but not limited to water, electricity, sanitation, medical and health services, and the food supply chain are just a few areas that can be positively impacted upon by good BCM. By implication the reliance of government structure are treated most in time of disasters as experienced through the two year period of the COVID-19 pandemic.Originality/valueThe government departments in South Africa do not have or have not implemented BCM due to the lack of clear guidelines. The COVID-19 pandemic however had accelerated the requirement for a top down BCM approach. To ensure that the scope of BCM is not limited, the possibility of having a set handbook for the government practitioner will ensure that service quality remains intact. Such a handbook related to government BCM practice is long outstanding.

The Effect of Provincial Policies on the COVID-19 Mortality Rate: Risk Analysis Study in Hamadan Province

Background: Governments adopt different policies and strategies to control and reduce the mortality rate of COVID-19. In order to investigate the effect of the adopted policies on the reduction of mortality caused by this disease, the policies implemented by the Regional Headquarter for the Control of COVID-19 Epidemic in Hamedan Province were evaluated. Materials and Methods: The required information was obtained from the Vice-Chancellor of Health of Hamadan University of Medical Sciences and the minutes of the meetings of the Headquarter for the Control of COVID-19 Epidemic in Hamadan Governorate. All the information obtained dates to the period from April to August 2021. A Bayesian network model was used in GeNIe software version 2.2 for the analysis of the information. Results: In this study, seven models were used to evaluate the impact of the adopted strategies. The first model included social distancing, including travel restriction and limiting gatherings, and the mortality rate was estimated to reach 4.72% by implementing the model. The second model includes observing personal hygiene, wearing masks, and vaccination, and the mortality rate was estimated to reach 4.92% by its implementation. The third model encompassed both travel restrictions and business closures, and the mortality rate reached 6.41% after its implementation. Models 4, 5, and 6, which are a combination of the first, second, and third models, have estimated the mortality rate to reach 1.95%, 2.77%, and 2.26%, respectively. In addition, model 7, which combines the above conditions, made the mortality rate reach 2.35%. In the present study, model 6 was selected as the most suitable model with five policies and RMES=0.03005. Conclusion: According to the results obtained in this study, the simultaneous implementation of five policies, including travel restrictions, business closures, personal hygiene, wearing masks and vaccination, can greatly reduce the risk of mortality. © 2022, Bushehr University of Medical Sciences. All rights reserved.

COVID-19-associated 2020 lockdown: a study on atmospheric black carbon fall impact on human health.

The mean mass concentrations of black carbon (BC), biomass burning (BC)bb, and fossil fuel combustion (BC)ff have been estimated during March-May 2020 (during the COVID-19 outbreak) and March-May 2019 at a semiarid region of Agra over the Indo-Gangetic basin region. The daily mean mass concentration of BC in 2020 and 2019 was 3.9 and 6.9 µg m-3, respectively. The high monthly mean mass concentration of BC was found to be 4.7, 3.4 and 3.3 µg m-3 in Mar-2020, Apr-2020, and May-2020, respectively, whereas in Mar-2019, Apr-2019, and May-2019 was 7.7, 7.5 and 5.4 µg m-3, respectively. The absorption coefficient (babs) and absorption angstrom exponent (AAE) of black carbon were calculated. The highest mean AAE was 1.6 in the year 2020 (Mar-May 2020) indicating the dominance of biomass burning. The mean mass concentration of fossil fuel (BC)ff and biomass burning (BC)bb is 3.4 and 0.51 µg m-3, respectively, in 2020 whereas 6.4 and 0.73 µg m-3, respectively, in 2019. The mean fraction contribution of BC with fossil fuel (BC)ff was 82.1 ± 13.5% and biomass burning (BC)bb was 17.9 ± 4.3% in 2020, while in 2019, fossil fuel (BC)ff was 86.7 ± 13.5% and biomass burning (BC)bb was 13.3 ± 6.7%. The population-weighted mean concentration of BC, fossil fuel (BC)ff, and biomass burning (BC)bb has been calculated. The health risk assessment of BC has been analyzed in the form of attributable relative risk factors and attributed relative risk during the COVID-19 outbreak using AirQ + v.2.0 model. The attributable relative risk factors of BC were 20.6% in 2020 and 29.4% in 2019. The mean attributed relative risk per 10,000,000 populations at 95% confidence interval (CI) due to BC was 184.06 (142.6-225.2) in 2020 and 609.06 (418.3-714.6) in 2019. The low attributed factor and attributed relative risk in 2020 may be attributed to improvements in air quality and a fall in the emission of BC. In 2020, due to the COVID-19 pandemic, the whole country faced the biggest lockdown, ban of the transportation of private vehicles, trains, aircraft, and construction activities, and shut down of the industry leading to a fall in the impact of BC on human health. Overall, this was like a blessing in disguise. This study will help in future planning of mitigation and emission control of air pollutants in large and BC in particular. It only needs a multipronged approach. This study may be like torch bearing to set path for mitigation of impacts of air pollution and improvement of air quality.

A cyber warfare perspective on risks related to health IoT devices and contact tracing.

The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence.

COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.

Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy-sadness, trust-disgust, fear-anger, surprise-anticipation, and negative-positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.

Benefit-risk assessment of Covid-19 vaccine, MRNA (MRNA-1273) for males age 18-64 years.

Since the authorization of the Moderna mRNA COVID-19 vaccine, real-world evidence has indicated its effectiveness in preventing COVID-19 cases. However, increased cases of mRNA vaccine-associated myocarditis/pericarditis have been reported, predominantly in young adults and adolescents. The Food and Drug Administration conducted a benefit-risk assessment to inform the review of the Biologics License Application for use of the Moderna vaccine among individuals ages 18 and older. We modeled the benefit-risk per million individuals who receive two complete doses of the vaccine. Benefit endpoints were vaccine-preventable COVID-19 cases, hospitalizations, intensive care unit (ICU) admissions, and deaths. The risk endpoints were vaccine-related myocarditis/pericarditis cases, hospitalizations, ICU admissions, and deaths. The analysis was conducted on the age-stratified male population due to data signals and previous work showing males to be the main risk group. We constructed six scenarios to evaluate the impact of uncertainty associated with pandemic dynamics, vaccine effectiveness (VE) against novel variants, and rates of vaccine-associated myocarditis/pericarditis cases on the model results. For our most likely scenario, we assumed the US COVID-19 incidence was for the week of December 25, 2021, with a VE of 30% against cases and 72% against hospitalization with the Omicron-dominant strain. Our source for estimating vaccine-attributable myocarditis/pericarditis rates was FDA's CBER Biologics Effectiveness and Safety (BEST) System databases. Overall, our results supported the conclusion that the benefits of the vaccine outweigh its risks. Remarkably, we predicted vaccinating one million 18-25 year-old males would prevent 82,484 cases, 4,766 hospitalizations, 1,144 ICU admissions, and 51 deaths due to COVID-19, comparing to 128 vaccine-attributable myocarditis/pericarditis cases, 110 hospitalizations, zero ICU admissions, and zero deaths. Uncertainties in the pandemic trajectory, effectiveness of vaccine against novel variants, and vaccine-attributable myocarditis/pericarditis rate are important limitations of our analysis. Also, the model does not evaluate potential long-term adverse effects due to either COVID-19 or vaccine-attributable myocarditis/pericarditis.

Americans' COVID-19 risk perceptions and risk perception predictors changed over time

Identifying and understanding risk perceptions—"how bad are the harms” to humans or to what they value that people see as potentially or actually arising from entities or events—has been critical for risk analysis, both for its own sake, and for expected associations between risk perceptions and subsequent outcomes, such as risky or protective behavior, or support for hazard management policies. Cross-sectional surveys have been the dominant method for identifying and understanding risk perceptions, yielding valuable data. However, cross-sectional surveys are unable to probe the dynamics of risk perceptions over time, which is critical to do while living in a dynamically hazardous world and to build causal understandings. Building upon earlier longitudinal panel studies of Americans' Ebola and Zika risk perceptions using multi-level modeling to assess temporal changes in these views and inter-individual factors affecting them, we examined patterns in Americans' COVID-19 risk perceptions in six waves across 14 months. The findings suggest that, in general, risk perceptions increased from February 2020 to April 2021, but with varying trends across different risk perception measures (personal, collective, affective, affect, severity, and duration). Factors in baseline risk perceptions (Wave 1) and inter-individual differences across waves differed even more: baseline ratings were associated with how immediate the threat is (temporal distance) and how likely the threat would affect people like oneself (social distance), and following the United States news about the pandemic. Inter-individual trend differences were shaped most by temporal distance, whether local coronavirus infections were accelerating their upward trend, and subjective knowledge about viral transmission. Associations of subjective knowledge and risk trend with risk perceptions could change signs (e.g. from positive to negative) over time. These findings hold theoretical implications for risk perception dynamics and taxonomies, and research design implications for studying risk perception dynamics and their comparison across hazards. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Characterizations of COVID-19 risk: review and suggestions for improvement of current practices

To handle the risks related to coronavirus and the COVID-19 disease, governments worldwide have adopted different policies and strategies. These policies and strategies build on various approaches and methods to assess and convey the risks. This paper looks more closely into these approaches and methods. We review and discuss practices in four countries (Norway, the UK, the US and Sweden), focusing on the approaches, methods and models used to assess and describe the risks related to COVID-19. The main aims are to present some current thinking, reveal differences and suggest areas for improvement. The paper concludes that current practices can be enhanced by incorporating ideas and approaches from contemporary risk science, particularly in relation to how to treat uncertainties and reflect degrees of knowledge.

Estimating SARS-CoV-2 infection risk in university dormitories using CO2 pulse injections

Air movement dynamics within three student dormitories were studied with simulated carbon dioxide (CO2) pulse injections to understand SARS-CoV-2 transmission risk. CO2 decay rate, proportion of shared air, and transport time were calculated from dynamic CO2 measurement data within simulated source and adjacent receptor rooms. Applying a Wells-Riley infection risk analysis with these results, the risk of SARS-CoV-2 infection in adjacent rooms ranged from 1% to 58% assuming an average emission rate of 5 quanta per hour and exposure duration of 3.5 days. Door opening status was very influential in resulting risk and vertical transport from source to above rooms was observed in all dormitories. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Comparative analysis of factors associated with the severe course of COVID-19 in the age groups under 60 and over 60 years of age

Introduction: Coronavirus infection is a disease that causes respiratory failure and complications in certain groups of people, leading to death. The factors associated with the severe course of COVID-19 have been fairly well studied by now;at the present stage, it is necessary to search for and study them in separate groups of people that differ in age, gender, ethnicity, the presence of background diseases, etc. to develop more personalized approaches to severe disease prevention. Background: To conduct a comparative analysis of the factors associated with the severe course of COVID-19 in people under and over 60 years of age and evaluate their prognostic significance in combination of factors. Materials and methods: A retrospective analysis of the clinical and laboratory parameters of 812 COVID-19 patients was carried out. Multiple logistic regression analysis was used to identify factors associated with the development of severe COVID-19. ROC analysis was performed to assess the prognostic significance of the set of identified statistically significant factors in the development of a severe course of COVID-19. Results: Multivariate logistic regression analysis showed that patients under 60 diabetes mellitus (OR=2,561, p=0,048), lymphopenia (OR=2,133, p=0,030), and pneumonia at admission (OR=2,556, p=0,011), rapid breathing (OR=3,497, p=0,001), low saturation (OR=4,076, p=0,006) were significantly associated with the development of severe COVID-19. At the same time, in patients older than 60 years, the presence of diabetes mellitus (OR=1,899, p=0,029), rapid breathing (OR=2,338, p=0,007) and low saturation (OR=4,248, p < 0,0001) were significantly associated with the development of a severe course of COVID-19. In groups under 60 and over 60 years of age, the prognostic value of the combination of all statistically significant factors corresponding to the groups was equal to the average level (AUC=0,760 and AUC=0,709, respectively) Conclusion: Factors associated with the development of a severe course of COVID-19 in elderly and middle-aged people have some differences related to the pathogenesis of the disease. For individuals under 60 years of age, factors associated with severe COVID-19 are diabetes mellitus, the presence of pneumonia on admission, dyspnea, low oxygen saturation, and lymphopenia. For individuals over 60 years of age, factors associated with severe COVID-19 are the presence of diabetes mellitus, shortness of breath, and low saturation. The combination of all the studied factors significantly increases the risk of developing a severe course of COVID-19 in both age groups.

Assessment of risk factors for thrombosis in ICU patients with COVID-19

Introduction: According to scientific studies, a high incidence of thrombotic events is known in hospitalized patients with COVID-19. Less than 50% of pulmonary embolisms (PE) are associated with signs of deep vein thrombosis (DVT) of the lower extremities. Background: To identify significant risk factors for thrombosis thrombosis (DVT) in intensive care patients with COVID-19. Materials and methods: We conducted a prospective cross-sectional study that included 465 adult patients with laboratory-confirmed COVID-19 admitted to the intensive care unit. All patients underwent computer tomography of the chest organs, ultrasound angioscanning of lower extremities, body mass index was calculated, the presence of comorbotity diseases and indicators of volumetric blood saturation were considered. The level of D-dimer in blood plasma, coagulation parameters (fibrinogen, factor VIII) were taken from laboratory parameters in calculations. For subgroups with 5 or fewer people, the chi-square test and Fisher's exact test were used. For quantitative variables, analysis of variance (ANOVA) and the Pearson and Spearman correlation coefficient were used. For multiple variables, ordered logistic regression models were built, with likelihood ratio tests performed to compare the models. Results: A total of 465 patients were included in the study. Comorbidities were present in 435 of 465 patients (93.55%) had at least one comorbidity. The most common was arterial hypertension - 370 (79.57%), followed by chronic heart failure - 196 (42.15%), obesity - 161 (34.62%), diabetes mellitus - 144 (30.97%), chronic renal failure (CRF) -58 (12.47%) and oncological diseases -25 (5.38%). The average body mass index was 29.7 kg/m2. In patients with DVT and venostasis, the body mass index (BMI) was more than 30 kg/m2 than without DVT (32.57+or-10.92 kg/m2, and 30.24+or-6.85 kg/m2, versus 29.22+or-6.46 kg/m2, respectively). Ultrasound angioscanning (USAS) confirmed deep vein thrombosis in 60 patients (13.8%) and was associated with older age (71.12+or-13.98 versus 67.20+or-11.16, p < 0.006), venous stasis was detected in 56 patients (12%) no DVT was detected in the rest of the studied patients. In the majority of cases, DVT was detected in the tibial segment -26 (43.33%), in 18 (30%) patients it was diagnosed in the popliteal veins and in 14 (23.33%) cases in the femoral segment. Diabetes mellitus (p=0.041), obesity (p=0.01) and CRF (p=0.028) were also significant risk factors for DVT. Conclusions: Significant risk factors for deep vein thrombosis in intensive care patients with COVID-19 are high levels of D-dimer (>=2.33 g/ml) and comorbidities such as obesity, chronic kidney failure, and diabetes mellitus.

Risk factors for SARS-CoV-2 seropositivity in a health care worker population during the early pandemic.

BACKGROUND: While others have reported severe acute respiratory syndrome-related coronavirus 2(SARS-CoV-2) seroprevalence studies in health care workers (HCWs), we leverage the use of a highly sensitive coronavirus antigen microarray to identify a group of seropositive health care workers who were missed by daily symptom screening that was instituted prior to any epidemiologically significant local outbreak. Given that most health care facilities rely on daily symptom screening as the primary method to identify SARS-CoV-2 among health care workers, here, we aim to determine how demographic, occupational, and clinical variables influence SARS-CoV-2 seropositivity among health care workers. METHODS: We designed a cross-sectional survey of HCWs for SARS-CoV-2 seropositivity conducted from May 15th to June 30th 2020 at a 418-bed academic hospital in Orange County, California. From an eligible population of 5,349 HCWs, study participants were recruited in two ways: an open cohort, and a targeted cohort. The open cohort was open to anyone, whereas the targeted cohort that recruited HCWs previously screened for COVID-19 or work in high-risk units. A total of 1,557 HCWs completed the survey and provided specimens, including 1,044 in the open cohort and 513 in the targeted cohort. Demographic, occupational, and clinical variables were surveyed electronically. SARS-CoV-2 seropositivity was assessed using a coronavirus antigen microarray (CoVAM), which measures antibodies against eleven viral antigens to identify prior infection with 98% specificity and 93% sensitivity. RESULTS: Among tested HCWs (n = 1,557), SARS-CoV-2 seropositivity was 10.8%, and risk factors included male gender (OR 1.48, 95% CI 1.05-2.06), exposure to COVID-19 outside of work (2.29, 1.14-4.29), working in food or environmental services (4.85, 1.51-14.85), and working in COVID-19 units (ICU: 2.28, 1.29-3.96; ward: 1.59, 1.01-2.48). Amongst 1,103 HCWs not previously screened, seropositivity was 8.0%, and additional risk factors included younger age (1.57, 1.00-2.45) and working in administration (2.69, 1.10-7.10). CONCLUSION: SARS-CoV-2 seropositivity is significantly higher than reported case counts even among HCWs who are meticulously screened. Seropositive HCWs missed by screening were more likely to be younger, work outside direct patient care, or have exposure outside of work.

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment.

Cloud computing adoption has been increasing rapidly amid COVID-19 as organisations accelerate the implementation of their digital strategies. Most models adopt traditional dynamic risk assessment, which does not adequately quantify or monetise risks to enable business-appropriate decision-making. In view of this challenge, a new model is proposed in this paper for assignment of monetary losses terms to the consequences nodes, thereby enabling experts to understand better the financial risks of any consequence. The proposed model is named Cloud Enterprise Dynamic Risk Assessment (CEDRA) model that uses CVSS, threat intelligence feeds and information about exploitation availability in the wild using dynamic Bayesian networks to predict vulnerability exploitations and financial losses. A case study of a scenario based on the Capital One breach attack was conducted to demonstrate experimentally the applicability of the model proposed in this paper. The methods presented in this study has improved vulnerability and financial losses prediction.

Adaptive elastic-net sliced inverse regression to identify risk factors affecting COVID-19 disease fatality rate

In this article, we proposed a plan based on Adaptive Elastic-net Sliced Inverse Regression to identify risk factors for the coronavirus disease (Covid-19) disease in the presence of collinearity between explanatory variables. Considering the penalty of elastic-net and sliced inverse regression, this method leads to sufficient dimension reduction and the presentation of a more stable and accurate model for variable selection.We applied the proposed method to simulated data and a new real-world Covid-19 disease dataset. We observed that the proposed method reduced the experimental standard error of bootstrapping by 12\% and 13\% compared to the previous superior methods in this approach, respectively, for both datasets. According to the results, during the outbreak of the Covid disease and its re-intensification, countries should quickly implement the following policies: declaring quarantine with minimal exceptions, making vaccines available by prioritizing specific groups, declaring a ban on gatherings, especially gatherings of more than 1000 people, closing schools at all levels, closing some works or declaring remote work, and holding information campaigns. Especially countries with more 0-14-year-old population, higher life expectancy, lower human development index, and colder weather should make more serious decisions in their implementation because they are more at risk.

Interactions among the human and organizational factors within the public sector regarding epidemic prevention and control.

The management of human and organizational factors (HOFs) within the public sector directly concerns the efficacy of epidemic prevention and control (EPC). Insufficient examination of such HOFs has led to defective countermeasures. This study attempts to comprehensively identify the HOFs within the public sector critical to EPC and investigate their interactions with the weighted network theory. A total of 55 HOFs were identified, and their interactions were assessed and visualized in the Chinese context. Then, the established weighted network was analyzed to investigate the interactions and diagnose critical factors and sectors. The analysis shows that there are strong interactions among HOFs, and that the human and organizational risks emerging from administrative departments of public health, centers for disease control and prevention, and medical institutions act as the key risk sources in the complex interconnected EPC system, exacerbating risk and causing a significant domino effect. It is recommended that the authorities devote more resources to the core sectors and endeavor to reinforce those critical HOFs by implementing closer risk communication, collaboration, and response. This study may deepen and broaden the authorities' awareness and understanding of interactions among HOFs regarding epidemic mitigation, and strengthen their capacity to perceive, evaluate, and manage these factors in a proactive and effective way, thereby facilitating the success of EPC.