A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing.

Coronavirus (COVID-19) is a very contagious infection that has drawn the world's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion.

Food insufficiency and Twitter emotions during a pandemic.

The COVID-19 pandemic initially caused worldwide concerns about food insecurity. Tweets analyzed in real-time may help food assistance providers target food supplies to where they are most urgently needed. In this exploratory study, we use natural language processing to extract sentiments and emotions expressed in food security-related tweets early in the pandemic in U.S. states. The emotion joy dominated in these tweets nationally, but only anger, disgust, and fear were also statistically correlated with contemporaneous food insufficiency rates reported in the Household Pulse Survey; more nuanced and statistically stronger correlations are detected within states, including a negative correlation with joy.

Nuclear medicine practices during the COVID-19 pandemic—review of some recently published protocols

BackgroundWith the global surge in COVID-19 pandemic, it has become inevitable for everyone, inclusive of nuclear medicine personnel, to play their role in combating and containing its transmission. During fall 2019, China encountered a novel coronavirus in Wuhan city which was later on termed as COVID-19. The pneumonia caused by COVID-19 is characterized by dry cough, fever, fatigue, and shortness of breathing (dyspnea). Until now, this virus has spread worldwide and continues to cause exponential causalities.Main bodyThis global catastrophic scenario calls for stringent measures to control COVID-19 infection. Thus herein, the respective authors have endeavored to review precautionary measures for nuclear medicine department, encompassing its personnel as well as the patients so that intradepartmental transmission can be prevented. This requires development and execution of a robust and dynamic plan elaborating the healthcare guidelines. Hence, our review paper covers the arena of nuclear medicine services in particular.ConclusionNuclear medicine can play its role in mitigating COVID-19 transmission to personnel and patients if provided with ample PPEs and guidelines are strictly followed. With implementing SOPs (standard operating procedures) based on these guidelines, nuclear medicine facilities will be better prepared for impromptu actions in case of any future outbreak while retaining the smooth flow of obligatory healthcare services.

Design and Analysis of a New COVID-19 Model with Comparative Study of Control Strategies

The COVID-19 pandemic has become a worldwide concern and has caused great frustration in the human community. Governments all over the world are struggling to combat the disease. In an effort to understand and address the situation, we conduct a thorough study of a COVID-19 model that provides insights into the dynamics of the disease. For this, we propose a new L S H S E A I H R COVID-19 model, where susceptible populations are divided into two sub-classes: low-risk susceptible populations, L S , and high-risk susceptible populations, H S . The aim of the subdivision of susceptible populations is to construct a model that is more reliable and realistic for disease control. We first prove the existence of a unique solution to the purposed model with the help of fundamental theorems of functional analysis and show that the solution lies in an invariant region. We compute the basic reproduction number and describe constraints that ensure the local and global asymptotic stability at equilibrium points. A sensitivity analysis is also carried out to identify the model's most influential parameters. Next, as a disease transmission control technique, a class of isolation is added to the intended L S H S E A I H R model. We suggest simple fixed controls through the adjustment of quarantine rates as a first control technique. To reduce the spread of COVID-19 as well as to minimize the cost functional, we constitute an optimal control problem and develop necessary conditions using Pontryagin's maximum principle. Finally, numerical simulations with and without controls are presented to demonstrate the efficiency and efficacy of the optimal control approach. The optimal control approach is also compared with an approach where the state model is solved numerically with different time-independent controls. The numerical results, which exhibit dynamical behavior of the COVID-19 system under the influence of various parameters, suggest that the implemented strategies, particularly the quarantine of infectious individuals, are effective in significantly reducing the number of infected individuals and achieving herd immunity. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Factors Associated with Psychiatric Morbidity among Patients who tested Positive and Isolated for COVID-19

ABSTRACT Objective: To look for the psychiatric morbidity and associated socio-demographic factors among patients who tested positive and isolated for COVID-19. Study Design: Cross-sectional study. Place and Duration of Study: Combined Military Hospital, Malir Pakistan, from Mar to May 2020. Methodology: All patients who tested positive for COVID-19 and were admitted to the COVID-19 Ward without complications were included in the study. General Health Questionnaire-12 (GHQ-12) was administered to look for the presence of psychiatric morbidity. Results: Out of 61 patients included in the study, 45(73.7%) showed the presence of psychiatric morbidity, while 16(26.3%) did not show psychiatric morbidity when screened with GHQ-12. 43(70.4%) were male, while 18(29.6%) were female. The mean age of the patients was 35.21±2.355 years. The advanced age and female gender have a statistically significant relationship (p-value<0.05) with the presence of psychiatric morbidity among patients of COVID-19. Conclusion: Many patients had psychiatric morbidity after being tested positive for COVID-19 and were isolated in the health facility. Female patients and patients aged more than 40 years were found to be more at risk of developing psychiatric morbidity among the patients admitted to COVID-19 ward.

When fear about health hurts performance: COVID-19 and its impact on employee's work

This study utilized terror management and conservation of resources theory to fulfill its aim of investigating the effects of fear of contamination of COVID-19 on performance of employees in the banking sector of Pakistan. A survey was conducted to collect data in two waves from 206 bank employees in Punjab region. SPSS was used for data analysis. The results demonstrated that such fear leads to emotional exhaustion which in turn negatively affects employee's work performance. However, the perceptions of better precautionary measures taken by the organization against the spread of the disease moderated the said relationship and weakened the strength of fear on performance through emotional exhaustion. Amid the widespread fear, panic and detrimental effects of COVID-19 on organizations and economies of the worlds, this research has implications for policy makers by showing the importance of organizational measures taken and displayed to employees in decreasing the negative effects of extensive fear and uncertainty prevailing due to the pandemic.

Exploiting the co-crystal ligands shape, features and structure-based approaches for identification of SARS-CoV-2 Mpro inhibitors.

SARS-CoV-2 enters the host cell through the ACE2 receptor and replicates its genome using an RNA-Dependent RNA Polymerase (RDRP). The functional RDRP is released from pro-protein pp1ab by the proteolytic activity of Main protease (Mpro) which is encoded within the viral genome. Due to its vital role in proteolysis of viral polyprotein chains, it has become an attractive potential drug target. We employed a hierarchical virtual screening approach to identify small synthetic protease inhibitors. Statistically optimized molecular shape and color-based features (various functional groups) from co-crystal ligands were used to screen different databases through various scoring schemes. Then, the electrostatic complementarity of screened compounds was matched with the most active molecule to further reduce the hit molecules' size. Finally, five hundred eighty-seven molecules were docked in Mpro catalytic binding site, out of which 29 common best hits were selected based on Glide and FRED scores. Five best-fitting compounds in complex with Mpro were subjected to MD simulations to analyze their structural stability and binding affinities with Mpro using MM/GB(PB)SA models. Modeling results suggest that identified hits can act as the lead compounds for designing better active Mpro inhibitors to enhance the chemical space to combat COVID-19.Communicated by Ramaswamy H. Sarma.

Predictors and outcomes of acute pulmonary embolism in COVID-19; insights from US National COVID cohort collaborative.

OBJECTIVES: To investigate whether COVID-19 patients with pulmonary embolism had higher mortality and assess the utility of D-dimer in predicting acute pulmonary embolism. PATIENTS AND METHODS: Using the National Collaborative COVID-19 retrospective cohort, a cohort of hospitalized COVID-19 patients was studied to compare 90-day mortality and intubation outcomes in patients with and without pulmonary embolism in a multivariable cox regression analysis. The secondary measured outcomes in 1:4 propensity score-matched analysis included length of stay, chest pain incidence, heart rate, history of pulmonary embolism or DVT, and admission laboratory parameters. RESULTS: Among 31,500 hospitalized COVID-19 patients, 1117 (3.5%) patients were diagnosed with acute pulmonary embolism. Patients with acute pulmonary embolism were noted to have higher mortality (23.6% vs.12.8%; adjusted Hazard Ratio (aHR) = 1.36, 95% CI [1.20-1.55]), and intubation rates (17.6% vs. 9.3%, aHR = 1.38[1.18-1.61]). Pulmonary embolism patients had higher admission D-dimer FEU (Odds Ratio(OR) = 1.13; 95%CI [1.1-1.15]). As the D-dimer value increased, the specificity, positive predictive value, and accuracy of the test increased; however, sensitivity decreased (AUC 0.70). At cut-off D-dimer FEU 1.8 mcg/ml, the test had clinical utility (accuracy 70%) in predicting pulmonary embolism. Patients with acute pulmonary embolism had a higher incidence of chest pain and history of pulmonary embolism or deep vein thrombosis. CONCLUSIONS: Acute pulmonary embolism is associated with worse mortality and morbidity outcomes in COVID-19. We present D-dimer as a predictive risk tool in the form of a clinical calculator for the diagnosis of acute pulmonary embolism in COVID-19.

Honey and Nigella sativa against COVID-19 in Pakistan (HNS-COVID-PK): A multicenter placebo-controlled randomized clinical trial.

Until now, no specific and effective treatment exists for coronavirus disease 2019 (COVID-19). Since honey and Nigella sativa (HNS) have established antiviral, antibacterial, antiinflammatory, antioxidant, and immunomodulatory properties, we tested their efficacy for this disease in a multicenter, placebo-controlled, and randomized clinical trial at four medical care facilities in Pakistan. RT-PCR confirmed COVID-19 adults showing moderate or severe disease were enrolled in the trial. Patients were randomly assigned in a 1:1 ratio to receive either honey (1 g kg-1 day-1 ) and Nigella sativa seeds (80 mg kg-1 day-1 ) or a placebo for up to 13 days along with standard care. The outcomes included symptoms' alleviation, viral clearance, and 30-day mortality in the intention-to-treat population. Three hundred and thirteen patients, 210 with moderate and 103 with severe disease, underwent randomization from April 30 to July 29, 2020. Among the moderate cases, 107 were assigned to HNS, whereas 103 were assigned to the placebo group. Among the severe cases, 50 were given HNS, and 53 were given the placebo. HNS resulted in ~50% reduction in time taken to alleviate symptoms as compared to placebo (moderate cases: 4 vs. 7 days, Hazard Ratio [HR]: 6.11; 95% Confidence Interval [CI]: 4.23-8.84, p < 0.0001 and for severe cases: 6 vs. 13 days, HR: 4.04; 95% CI: 2.46-6.64; p < 0.0001). HNS also cleared the virus earlier than placebo in both moderate cases (6 vs. 10 days, HR: 5.53; 95% CI: 3.76-8.14, p < 0.0001) and severe cases (8.5 vs. 12 days, HR: 4.32; 95% CI: 2.62-7.13, p < 0.0001). HNS further led to a better clinical score on day 6 with normal activity resumption in 63.6% vs. 10.9% among moderate cases (OR: 0.07; 95% CI: 0.03-0.13, p < 0.0001) and hospital discharge in 50% versus 2.8% in severe cases (OR: 0.03; 95% CI: 0.01-0.09, p < 0.0001). In severe cases, the mortality rate was less than 1/4th in the HNS group than in placebo (4% vs. 18.87%, OR: 0.18; 95% CI: 0.02-0.92, p = 0.029). No HNS-related adverse effects were observed. HNS, compared with placebo, significantly improved symptoms, expedited viral load clearance, and reduced mortality in COVID-19 patients. This trial was registered on April 15, 2020 with ClinicalTrials.gov Identifier: NCT04347382.

A global portrait of expressed mental health signals towards COVID-19 in social media space.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

Bleeding Hazard of Percutaneous Tracheostomy in COVID-19 Patients Supported With Venovenous Extracorporeal Membrane Oxygenation: A Case Series.

OBJECTIVES: Tracheostomy usually is performed to aid weaning from mechanical ventilation and facilitate rehabilitation and secretion clearance. Little is known about the safety of percutaneous tracheostomy in patients with severe COVID-19 supported on venovenous extracorporeal membrane oxygenation (VV-ECMO). This study aimed to investigate the bleeding risk of bedside percutaneous tracheostomy in patients with COVID-19 infection supported with VV-ECMO. DESIGN: A Retrospective review of electronic data for routine care of patients on ECMO. SETTING: Tertiary, university-affiliated national ECMO center. PARTICIPANTS: Patients with COVID-19 who underwent percutaneous tracheostomy while on VV-ECMO support. INTERVENTIONS: No intervention was conducted during this study. MEASUREMENTS AND MAIN RESULTS: Electronic medical records of 16 confirmed patients with COVID-19 who underwent percutaneous tracheostomy while on VV-ECMO support, including patient demographics, severity of illness, clinical variables, procedural complications, and outcomes, were compared with 16 non-COVID-19 patients. The SPSS statistical software was used for statistical analysis. The demographic data were compared using the chi-square test, and normality assumption was tested using the Shapiro-Wilk test. The indications for tracheostomy in all the patients were prolonged mechanical ventilation and sedation management. None of the patients suffered a life-threatening procedural complication within 48 hours. Moderate-to-severe bleeding was similar in both groups. There was no difference in 30- and 90-days mortality between both groups. As per routine screening results, none of the staff involved contracted COVID-19 infection. CONCLUSIONS: In this case series, percutaneous tracheostomy during VV-ECMO in patients with COVID-19 appeared to be safe and did not pose additional risks to patients or healthcare workers.

Optimal control strategies for the reliable and competitive mathematical analysis of Covid-19 pandemic model.

To understand dynamics of the COVID-19 disease realistically, a new SEIAPHR model has been proposed in this article where the infectious individuals have been categorized as symptomatic, asymptomatic, and super-spreaders. The model has been investigated for existence of a unique solution. To measure the contagiousness of COVID-19, reproduction number R 0 is also computed using next generation matrix method. It is shown that the model is locally stable at disease-free equilibrium point when R 0 < 1 and unstable for R 0 > 1 . The model has been analyzed for global stability at both of the disease-free and endemic equilibrium points. Sensitivity analysis is also included to examine the effect of parameters of the model on reproduction number R 0 . A couple of optimal control problems have been designed to study the effect of control strategies for disease control and eradication from the society. Numerical results show that the adopted control approaches are much effective in reducing new infections.

Application of comprehensive evaluation framework to Coronavirus Disease 19 studies: A systematic review of translational aspects of artificial intelligence in health care (preprint)

Background: Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in healthcare environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended. Objective: We have previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to healthcare environments. In this study, we apply the TEHAI to COVID-19 literature in order to assess how well translational topics are covered. Methods: A systematic literature search for COVID-AI studies published between December 2019-2020 resulted in 3,830 records. A subset of 102 papers that passed inclusion criteria were sampled for full review. Nine reviewers assessed the papers for translational value and collected descriptive data (each study was assessed by two reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. Results: We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, non-maleficence and service adoption received failed scores in most studies. Conclusions: Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 healthcare environments.

Synthesis, characterization, molecular docking and network pharmacology of bioactive metallic sulfonamide-isatin ligands against promising drug targets

• Synthesis of new Sulfonamide-isatin based scaffolds to incorporate first row metals. • Molecular docking to find a best docking pocket for COVID-19 protein. • Online network pharmacology to find a best target for Alzheimer and carbonic anhydrase-II related gene targets. • Characterization with most promising analytical techniques and DFT based studies. • In vitro enzyme inhibition and antimicrobial profiling of new compounds. A series of sulfonamide and isatin based Schiff bases, (S1) and (S2), and their metal (Co2+, Ni2+, Cu2+ and Zn2+) complexes (1)-(8) were synthesized and characterized by spectroscopic (UV, IR, MS, 1H and 13C-NMR), elemental, magnetic and physical techniques. The non-electrolytic character of Co2+, Ni2+, and Zn2+ compounds and electrolytic nature of Cu2+ was established by their conductance studies. The energies of Frontier Molecular Orbitals (FMOs) were also used to explore various global and quantum chemical qualities. To find the activity and molecular targets in curing Alzheimer's Disease (AD) and Carbonic Anhydrase II (CA-II) inhibition, Network Pharmacology modeling was used. The prospective targets were predicted using the Swiss Target PredictionR online facility. The Gene CardsR database has been used to find genes linked to AD and CA-II. We also conducted Gene OntologyR (GO) analysis on the intersecting genes targets on active targets of synthesized compounds by DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Services using the CytoscapeR program. The in vitro enzyme inhibition assays were done against protease, amylase, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) while their antimicrobial studies were performed against pathogenic bacterial and fungal species. The antioxidant values, evaluated as 2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing assay power (FRAP) (%) ranged between 51.0±0.11-68.1±0.11% with IC 50 ranging 146.84-196.08 µL/mol. [Display omitted] [ FROM AUTHOR]

Theoretical Analysis of a COVID-19 CF-Fractional Model to Optimally Control the Spread of Pandemic

In this manuscript, we formulate a mathematical model of the deadly COVID-19 pandemic to understand the dynamic behavior of COVID-19. For the dynamic study, a new SEIAPHR fractional model was purposed in which infectious individuals were divided into three sub-compartments. The purpose is to construct a more reliable and realistic model for a complete mathematical and computational analysis and design of different control strategies for the proposed Caputo-Fabrizio fractional model. We prove the existence and uniqueness of solutions by employing well-known theorems of fractional calculus and functional analyses. The positivity and boundedness of the solutions are proved using the fractional-order properties of the Laplace transformation. The basic reproduction number for the model is computed using a next-generation technique to handle the future dynamics of the pandemic. The local-global stability of the model was also investigated at each equilibrium point. We propose basic fixed controls through manipulation of quarantine rates and formulate an optimal control problem to find the best controls (quarantine rates) employed on infected, asymptomatic, and 'superspreader';humans, respectively, to restrict the spread of the disease. For the numerical solution of the fractional model, a computationally efficient Adams-Bashforth method is presented. A fractional-order optimal control problem and the associated optimality conditions of Pontryagin maximum principle are discussed in order to optimally reduce the number of infected, asymptomatic, and superspreader humans. The obtained numerical results are discussed and shown through graphs.

Generalized Quasi Trees with Respect to Degree Based Topological Indices and Their Applications to COVID-19 Drugs

The l-generalized quasi tree is a graph G for which we can find W⊂V(G) with ;W;=l such that G-W is a tree but for an arbitrary Y⊂V(G) with ;Y;<l, G-Y is not a tree. In this paper, inequalities with respect to zeroth-order Randićand hyper-Zagreb indices are studied in the class of l-generalized quasi trees. The corresponding extremal graphs corresponding to these indices in the class of l-generalized quasi trees are also obtained. In addition, we carry QSPR analysis of COVID-19 drugs with zeroth-order Randićand hyper-Zagreb indices (energy).

Deep Feature Extraction for Detection of COVID-19 Using Deep Learning

SARS-CoV-2, a severe acute respiratory syndrome that is related to COVID-19, is a novel type of influenza virus that has infected the entire international community. It has created severe health and safety concerns all over the globe. Identifying the outbreak in the initial phase may aid successful recovery. The rapid and exact identification of COVID-19 limits the risk of spreading this fatal disease. Patients with COVID-19 have distinctive radiographic characteristics on chest X-rays and CT scans. CXR images can be used for people with COVID-19 to diagnose their disease early. This research was focused on the deep feature extraction, accurate detection, and prediction of COVID-19 from X-ray images. The proposed concatenated CNN model is based on deep learning models (Xception and ResNet101) for CXR images. For the extraction of features, CNN models (Xception and ResNet101) were utilized, and then these features were combined using a concatenated model technique. In the proposed scheme, the particle swarm optimization method is applied to the concatenated features that provide optimal features from an overall feature vector. The selection of these optimal features helps to decrease the classification period. To evaluate the performance of the proposed approach, experiments were conducted with CXR images. Datasets of CXR images were collected from three different sources. The results demonstrated the efficiency of the proposed scheme for detecting COVID-19 with average accuracies of 99.77%, 99.72%, and 99.73% for datasets 1, 2 and 3, respectively. Moreover, the proposed model also achieved average COVID-19 sensitivities of 96.6%, 97.18%, and 98.88% for datasets 1, 2, and 3, respectively. The maximum overall accuracy of all classes - normal, pneumonia, and COVID-19 - was about 98.02%.

COVID‐19 among health care workers and their impact on the health care system in a teaching hospital in Pakistan: A cross sectional observational study

Background and Aims Health care workers (HCWs) are thought to be high‐risk population for acquiring coronavirus disease (COVID‐19). The COVID‐19 emergence has had a profound effect on healthcare system. We sought to investigate the COVID‐19 among HCWs and their effects on the healthcare system. Methods A cross sectional observational study was conducted at Timergara teaching hospital. The study included HCWs with positive real time polymerase chain reaction (Q‐PCR) for severe acute respiratory syndrome coronavirus (SARS‐CoV‐2). The study duration was from April to September, 2020. The demographic profile of each recruited subject was collected through structured interview. The patient's admissions to hospital were collected for the 5 months before (October 2019–February 2020) and 5 months after lockdown (March–July 2020). Results A total of 72 out of 689 (10%) HCWs were tested positive for SARS‐CoV‐2, of whom 83% were front‐liners. The majority were male (72%), with comorbidities (14%) and no mortality. The structured interview of all participants showed that the healthcare setting was the major possible source of infection (97%). The patient admissions into the hospital were reduced by 42% during lockdown than prelockdown period. The patients admission was significantly decreased in the medical ward during lockdown (60% decrease;p < 0.01) with slightly similar trends in other departments. Conclusion In conclusion, we found increased risk of COVID‐19 for front‐line HCWs. Lack of mortality was the favorable outcome. Lack of replacing the infected HCWs possibly explained the marked decrease in hospital admissions, and potential inadequate healthcare delivery during the lockdown. Understanding SARS‐CoV‐2 among HCWs and their impact on health‐care system will be crucial for countries under COVID‐19 crises or in case of future pandemic to deliver proper health services.

A survey on computer vision based human analysis in the COVID-19 era.

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of ( i ) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and ( ii ) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

COVID-19 among health care workers and their impact on the health care system in a teaching hospital in Pakistan: A cross sectional observational study.

Background and Aims: Health care workers (HCWs) are thought to be high-risk population for acquiring coronavirus disease (COVID-19). The COVID-19 emergence has had a profound effect on healthcare system. We sought to investigate the COVID-19 among HCWs and their effects on the healthcare system. Methods: A cross sectional observational study was conducted at Timergara teaching hospital. The study included HCWs with positive real time polymerase chain reaction (Q-PCR) for severe acute respiratory syndrome coronavirus (SARS-CoV-2). The study duration was from April to September, 2020. The demographic profile of each recruited subject was collected through structured interview. The patient's admissions to hospital were collected for the 5 months before (October 2019-February 2020) and 5 months after lockdown (March-July 2020). Results: A total of 72 out of 689 (10%) HCWs were tested positive for SARS-CoV-2, of whom 83% were front-liners. The majority were male (72%), with comorbidities (14%) and no mortality. The structured interview of all participants showed that the healthcare setting was the major possible source of infection (97%). The patient admissions into the hospital were reduced by 42% during lockdown than prelockdown period. The patients admission was significantly decreased in the medical ward during lockdown (60% decrease; p < 0.01) with slightly similar trends in other departments. Conclusion: In conclusion, we found increased risk of COVID-19 for front-line HCWs. Lack of mortality was the favorable outcome. Lack of replacing the infected HCWs possibly explained the marked decrease in hospital admissions, and potential inadequate healthcare delivery during the lockdown. Understanding SARS-CoV-2 among HCWs and their impact on health-care system will be crucial for countries under COVID-19 crises or in case of future pandemic to deliver proper health services.