Does SARS-CoV-2 has a longer incubation period than SARS and MERS?

Mathematical assessment of control strategies against the spread of MERS-CoV in humans and camels in Saudi Arabia

A new mathematical model for the transmission dynamics and control of the Middle Eastern respiratory syndrome (MERS), a respiratory virus caused by MERS-CoV coronavirus (and primarily spread to humans by dromedary camels) that first emerged out of the Kingdom of Saudi Arabia (KSA) in 2012, was designed and used to study the transmission dynamics of the disease in a human-camel population within the KSA. Rigorous analysis of the model, which was fitted and cross-validated using the observed MERS-CoV data for the KSA, showed that its disease-free equilibrium was locally asymptotically stable whenever its reproduction number (denoted by $ {\mathbb R}_{0M} $) was less than unity. Using the fixed and estimated parameters of the model, the value of $ {\mathbb R}_{0M} $ for the KSA was estimated to be 0.84, suggesting that the prospects for MERS-CoV elimination are highly promising. The model was extended to allow for the assessment of public health intervention strategies, notably the potential use of vaccines for both humans and camels and the use of face masks by humans in public or when in close proximity with camels. Simulations of the extended model showed that the use of the face mask by humans who come in close proximity with camels, as a sole public health intervention strategy, significantly reduced human-to-camel and camel-to-human transmission of the disease, and this reduction depends on the efficacy and coverage of the mask type used in the community. For instance, if surgical masks are prioritized, the disease can be eliminated in both the human and camel population if at least 45% of individuals who have close contact with camels wear them consistently. The simulations further showed that while vaccinating humans as a sole intervention strategy only had marginal impact in reducing the disease burden in the human population, an intervention strategy based on vaccinating camels only resulted in a significant reduction in the disease burden in camels (and, consequently, in humans as well). Thus, this study suggests that attention should be focused on effectively combating the disease in the camel population, rather than in the human population. Furthermore, the extended model was used to simulate a hybrid strategy, which combined vaccination of both humans and camels as well as the use of face masks by humans. This simulation showed a marked reduction of the disease burden in both humans and camels, with an increasing effectiveness level of this intervention, in comparison to the baseline scenario or any of the aforementioned sole vaccination scenarios. In summary, this study showed that the prospect of the elimination of MERS-CoV-2 in the Kingdom of Saudi Arabia is promising using pharmaceutical (vaccination) and nonpharmaceutical (mask) intervention strategies, implemented in isolation or (preferably) in combination, that are focused on reducing the disease burden in the camel population.

Dynamics of simplicial SEIRS epidemic model: global asymptotic stability and neural Lyapunov functions

The transmission of infectious diseases on a particular network is ubiquitous in the physical world. Here, we investigate the transmission mechanism of infectious diseases with an incubation period using a networked compartment model that contains simplicial interactions, a typical high-order structure. We establish a simplicial SEIRS model and find that the proportion of infected individuals in equilibrium increases due to the many-body connections, regardless of the type of connections used. We analyze the dynamics of the established model, including existence and local asymptotic stability, and highlight differences from existing models. Significantly, we demonstrate global asymptotic stability using the neural Lyapunov function, a machine learning technique, with both numerical simulations and rigorous analytical arguments. We believe that our model owns the potential to provide valuable insights into transmission mechanisms of infectious diseases on high-order network structures, and that our approach and theory of using neural Lyapunov functions to validate model asymptotic stability can significantly advance investigations on complex dynamics of infectious disease.

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study

BACKGROUND

Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures.

OBJECTIVE

This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses.

METHODS

We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020.

RESULTS

The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong.

CONCLUSIONS

Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.

Mathematical Modeling of COVID-19 Cases and Deaths and the Impact of Vaccinations during Three Years of the Pandemic in Peru

The COVID-19 pandemic has caused widespread infections, deaths, and substantial economic losses. Vaccine development efforts have led to authorized candidates reducing hospitalizations and mortality, although variant emergence remains a concern. Peru faced a significant impact due to healthcare deficiencies. This study employed logistic regression to mathematically model COVID-19's dynamics in Peru over three years and assessed the correlations between cases, deaths, and people vaccinated. We estimated the critical time (tc) for cases (627 days), deaths (389 days), and people vaccinated (268 days), which led to the maximum speed values on those days. Negative correlations were identified between people vaccinated and cases (-0.40) and between people vaccinated and deaths (-0.75), suggesting reciprocal relationships between those pairs of variables. In addition, Granger causality tests determined that the vaccinated population dynamics can be used to forecast the behavior of deaths (p-value < 0.05), evidencing the impact of vaccinations against COVID-19. Also, the coefficient of determination (R2) indicated a robust representation of the real data. Using the Peruvian context as an example case, the logistic model's projections of cases, deaths, and vaccinations provide crucial insights into the pandemic, guiding public health tactics and reaffirming the essential role of vaccinations and resource distribution for an effective fight against COVID-19.

Nonpharmaceutical Interventions in Georgia: Public Health Implications

OBJECTIVES

As coronavirus disease 2019 (COVID-19) spread, many states implemented nonpharmaceutical interventions in the absence of effective therapies with varying degrees of success. Our aim was to evaluate restrictions comparing two regions of Georgia and their impact on outcomes as measured by confirmed illness and deaths.

METHODS

Using The New York Times COVID-19 incidence data and mandate information from various web sites, we examined trends in cases and deaths using joinpoint analysis at the region and county level before and after the implementation of a mandate.

RESULTS

We found that rates of cases and deaths showed the greatest decrease in acceleration after the simultaneous implementation of a statewide shelter-in-place for vulnerable populations combined with social distancing for businesses and limiting gatherings to <10 people. County-level shelters-in-place, business closures, limits on gatherings to <10, and mask mandates showed significant case rate decreases after a county implemented them. School closures had no consistent effect on either outcome.

CONCLUSIONS

Our findings indicate that protecting vulnerable populations, implementing social distancing, and mandating masks may be effective countermeasures to containment while mitigating the economic and psychosocial effects of strict shelters-in-place and business closures. In addition, states should consider allowing local municipalities the flexibility to enact nonpharmaceutical interventions that are more or less restrictive than the state-level mandates under some conditions in which the data indicate it is necessary to protect communities from disease or undue economic burden.

[Estimation of serial interval and basic reproductive number for imported cases of COVID-19]

OBJECTIVE

To estimate the serial interval and the basic reproduction number of COVID-19 between imported cases during the containment phase in Pereira-Colombia, 2020.

METHOD

A quantitative study was carried out to determine the transmission dynamics for COVID-19. Field epidemiological data were used, which included 12 laboratory-confirmed cases with RT-PCR for imported SARS-CoV-2 and their corresponding confirmed secondary cases, including family and social contacts.

RESULTS

The serial intervals in COVID-19 fit a Gamma distribution, with a mean of the serial interval of 3.8 days (2.7) and an R0 of 1.7 (95% CI 1.06-2.7) lower than that found in other populations with onset of the outbreak.

CONCLUSIONS

A serial interval lower than the incubation period such as that estimated in this study, suggests a presymptomatic transmission period that according to other investigations reaches an average peak at 3.8 days, suggesting that during the field epidemiological investigation the search for contacts Narrowing is performed from at least 2 days before the onset of symptoms of the initial case.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

A bibliometric analysis of inflammatory bowel disease and COVID-19 researches

Background

Patients with inflammatory bowel disease (IBD) often require immunosuppressive therapy and are hence susceptible to various opportunistic viral and bacterial infections. In this regard, many studies on IBD and COVID-19 have been conducted. However, no bibliometric analysis has been performed. This study provides a general overview of IBD and COVID-19.

Methods

Publications about IBD and COVID-19 from 2020 to 2022 were retrieved from the Web of Science Core Collection (WoSCC) database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and HistCite.

Results

A total of 396 publications were retrieved and considered in this study. The maximum number of publications were from the United States, Italy, and England, and the contributions of these countries were significant. Kappelman ranked first in article citations. The Icahn School of Medicine at Mount Sinai and Inflammatory Bowel Diseases were the most prolific affiliation and journal, respectively. The most influential research topics were "management", "impact", "vaccination", and "receptor". The following keywords represented research frontiers: "depression", "the quality of life of IBD patients", "infliximab", "COVID-19 vaccine", and "second vaccination".

Conclusions

Over the past 3 years, most studies on IBD and COVID-19 have focused on clinical research. In particular, topics such as "depression", "the quality of life of IBD patients", "infliximab", "COVID-19 vaccine", and "second vaccination" were noted to have received much attention recently. Future research should focus on our understanding of the immune response to COVID-19 vaccination in biologically treated patients, the psychological impact of COVID-19, IBD management guidelines, and the long-term impact of COVID-19 in IBD patients. This study will provide researchers with a better understanding of research trends on IBD during COVID-19.

Multiple pathways of SARS-CoV-2 nosocomial transmission uncovered by integrated genomic and epidemiological analyses during the second wave of the COVID-19 pandemic in the UK

Introduction

Throughout the global COVID-19 pandemic, nosocomial transmission has represented a major concern for healthcare settings and has accounted for many infections diagnosed within hospitals. As restrictions ease and novel variants continue to spread, it is important to uncover the specific pathways by which nosocomial outbreaks occur to understand the most suitable transmission control strategies for the future.

Methods

In this investigation, SARS-CoV-2 genome sequences obtained from 694 healthcare workers and 1,181 patients were analyzed at a large acute NHS hospital in the UK between September 2020 and May 2021. These viral genomic data were combined with epidemiological data to uncover transmission routes within the hospital. We also investigated the effects of the introduction of the highly transmissible variant of concern (VOC), Alpha, over this period, as well as the effects of the national vaccination program on SARS-CoV-2 infection in the hospital.

Results

Our results show that infections of all variants within the hospital increased as community prevalence of Alpha increased, resulting in several outbreaks and super-spreader events. Nosocomial infections were enriched amongst older and more vulnerable patients more likely to be in hospital for longer periods but had no impact on disease severity. Infections appeared to be transmitted most regularly from patient to patient and from patients to HCWs. In contrast, infections from HCWs to patients appeared rare, highlighting the benefits of PPE in infection control. The introduction of the vaccine at this time also reduced infections amongst HCWs by over four-times.

Discussion

These analyses have highlighted the importance of control measures such as regular testing, rapid lateral flow testing alongside polymerase chain reaction (PCR) testing, isolation of positive patients in the emergency department (where possible), and physical distancing of patient beds on hospital wards to minimize nosocomial transmission of infectious diseases such as COVID-19.

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus's possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early "warning signal"; however, a lot of limitations of wastewater surveillance have also been identified.

Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis

Background: The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak to be a public health emergency and international concern and recognized it as a pandemic. This study aimed to estimate the epidemiologic parameters of the COVID-19 pandemic for clinical and epidemiological help. Methods: In this systematic review and meta-analysis study, 4 electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar were searched for the literature published from early December 2019 up to 23 March 2020. After screening, we selected 76 articles based on epidemiological parameters, including basic reproduction number, serial interval, incubation period, doubling time, growth rate, case-fatality rate, and the onset of symptom to hospitalization as eligibility criteria. For the estimation of overall pooled epidemiologic parameters, fixed and random effect models with 95% CI were used based on the value of between-study heterogeneity (I2). Results: A total of 76 observational studies were included in the analysis. The pooled estimate for R0 was 2.99 (95% CI, 2.71-3.27) for COVID-19. The overall R0 was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively. The overall serial interval, doubling time, and incubation period were 4.45 (95% CI, 4.03-4.87), 4.14 (95% CI, 2.67-5.62), and 4.24 (95% CI, 3.03-5.44) days for COVID-19. In addition, the overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively. Conclusion: The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by computing the pooled estimate of the epidemiological parameters, opening the door for health policymakers to consider additional control measures.

Comparative highlights on MERS-CoV, SARS-CoV-1, SARS-CoV-2, and NEO-CoV

The severe acute respiratory syndrome (SARS-CoV, now SARS-CoV-1), middle east respiratory syndrome (MERS-CoV), Neo-CoV, and 2019 novel coronavirus (SARS-CoV-2/COVID-19) are the most notable coronaviruses, infecting the number of people worldwide by targeting the respiratory system. All these viruses are of zoonotic origin, predominantly from bats which are one of the natural reservoir hosts for coronaviruses. Thus, the major goal of our review article is to compare and contrast the characteristics and attributes of these coronaviruses. The SARS-CoV-1, MERS-CoV, and COVID-19 have many viral similarities due to their classification, they are not genetically related. COVID-19 shares approximately 79 % of its genome with SARS-CoV-1 and about 50 % with MERS-CoV. The shared receptor protein, ACE2 exhibit the most striking genetic similarities between SARS-CoV-1 and SARS-CoV-2. SARS-CoV primarily replicates in the epithelial cells of the respiratory system, but it may also affect macrophages, monocytes, activated T cells, and dendritic cells. MERS-CoV not only infects and replicates inside the epithelial and immune cells, but it may lyse them too, which is one of the common reasons for MERS's higher mortality rate. The details of infections caused by SARS-CoV-2 and lytic replication mechanisms in host cells are currently mysterious. In this review article, we will discuss the comparative highlights of SARS-CoV-1, MERS-CoV, SARS-CoV-2, and Neo-CoV, concerning their structural features, morphological characteristics, sources of virus origin and their evolutionary transitions, infection mechanism, computational study approaches, pathogenesis and their severity towards several diseases, possible therapeutic approaches, and preventive measures.

An overview of gastrointestinal diseases in patients with COVID-19: A narrative review

Coronavirus disease-2019 (COVID-19), caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has emerged as a global health concern. This study aimed to review the epidemiology and pathophysiology of COVID-19 and provide evidence for the implementation of control measures. We utilized several online databases, including MEDLINE (National Library of Medicine, Bethesda, Maryland, USA), PubMed, EMBASE, Web of Science, and Google Scholar, to collect relevant published papers using a combination of the following keywords: "COVID-19," "SARS-CoV-2," "novel coronavirus," "epidemiology," and "pathophysiology." The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used in this study. Globally, approximately 3-46% of patients with SARS-CoV-2 infection experience gastrointestinal symptoms. The clinical spectrum of COVID-19 is wide, ranging from mild to severe, and even fatal. COVID-19 was initially reported as a respiratory tract disease; however, gastrointestinal symptoms have only recently been reported. COVID-19 Patients with gastrointestinal symptoms may have more severe clinical manifestations and poor prognosis. This study highlights the need to better understand the mechanisms involved in the development of gastrointestinal symptoms in patients with COVID-19 to prevent the further spread of this pathogen.

A Multiscale Model of COVID-19 Dynamics

COVID-19, caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global pandemic and created unprecedented public health challenges throughout the world. Despite significant progresses in understanding the disease pathogenesis and progression, the epidemiological triad of pathogen, host, and environment remains unclear. In this paper, we develop a multiscale model to study the coupled within-host and between-host dynamics of COVID-19. The model includes multiple transmission routes (both human-to-human and environment-to-human) and connects multiple scales (both the population and individual levels). A detailed analysis on the local and global dynamics of the fast system, slow system and full system shows that rich dynamics, including both forward and backward bifurcations, emerge with the coupling of viral infection and epidemiological models. Model fitting to both virological and epidemiological data facilitates the evaluation of the influence of a few infection characteristics and antiviral treatment on the spread of the disease. Our work underlines the potential role that the environment can play in the transmission of COVID-19. Antiviral treatment of infected individuals can delay but cannot prevent the emergence of disease outbreaks. These results highlight the implementation of comprehensive intervention measures such as social distancing and wearing masks that aim to stop airborne transmission, combined with surface disinfection and hand hygiene that can prevent environmental transmission. The model also provides a multiscale modeling framework to study other infectious diseases when the environment can serve as a reservoir of pathogens.

Interferon induction, evasion, and paradoxical roles during SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has caused millions of deaths in the past two years. Although initially little was understood about this virus, recent research has significantly advanced and landed interferons (IFNs) in the spotlight. While Type I and III IFN have long been known as central to antiviral immunity, in the case of COVID-19 their role was initially controversial. However, the protective function of IFN is now well supported by the identification of human deficiencies in IFN responses as a predictor of disease severity. Here, we will review the cell types and pathways that lead to IFN production as well as the importance of IFN timing and location for disease outcome. We will further discuss the mechanisms that SARS-CoV-2 uses to evade IFN responses, and the current efforts to implement IFNs as therapeutics in the treatment of COVID-19. It is essential to understand the relationships between SARS-CoV-2 and IFN to better inform treatments that exploit IFN functions to alleviate COVID-19.

Bayesian sequential data assimilation for COVID-19 forecasting

We introduce a Bayesian sequential data assimilation and forecasting method for non-autonomous dynamical systems. We applied this method to the current COVID-19 pandemic. It is assumed that suitable transmission, epidemic and observation models are available and previously validated. The transmission and epidemic models are coded into a dynamical system. The observation model depends on the dynamical system state variables and parameters, and is cast as a likelihood function. The forecast is sequentially updated over a sliding window of epidemic records as new data becomes available. Prior distributions for the state variables at the new forecasting time are assembled using the dynamical system, calibrated for the previous forecast. Epidemic outbreaks are non-autonomous dynamical systems depending on human behavior, viral evolution and climate, among other factors, rendering it impossible to make reliable long-term epidemic forecasts. We show our forecasting method's performance using a SEIR type model and COVID-19 data from several Mexican localities. Moreover, we derive further insights into the COVID-19 pandemic from our model predictions. The rationale of our approach is that sequential data assimilation is an adequate compromise between data fitting and dynamical system prediction.

Statistical analysis of COVID-19 infection severity in lung lobes from chest CT

Detection of the COVID 19 virus is possible through the reverse transcription-polymerase chain reaction (RT-PCR) kits and computed tomography (CT) images of the lungs. Diagnosis via CT images provides a faster diagnosis than the RT-PCR method does. In addition to low false-negative rate, CT is also used for prognosis in determining the severity of the disease and the proposed treatment method. In this study, we estimated a probability density function (PDF) to examine the infections caused by the virus. We collected 232 chest CT of suspected patients and had them labeled by two radiologists in 6 classes, including a healthy class and 5 classes of different infection severity. To segment the lung lobes, we used a pre-trained U-Net model with an average Dice similarity coefficient (DSC) greater than 0.96. First, we extracted the PDF to grade the infection of each lobe and selected five specific thresholds as feature vectors. We then assigned this feature vector to a support vector machine (SVM) model and made the final prediction of the infection severity. Using the T-Test statistics, we calculated the p-value at different pixel thresholds and reported the significant differences in the pixel values. In most cases, the p-value was less than 0.05. Our developed model was developed on roughly labeled data without any manual segmentation, which estimated lung infection involvements with the area under the curve (AUC) in the range of [0.64, 0.87]. The introduced model can be used to generate a systematic automated report for individual patients infected by COVID-19.

Role of the Microbiome in the Pathogenesis of COVID-19

The ongoing pandemic coronavirus disease COVID-19 is caused by the highly contagious single-stranded RNA virus, SARS-coronavirus 2 (SARS-CoV-2), which has a high rate of evolution like other RNA viruses. The first genome sequences of SARS-CoV-2 were available in early 2020. Subsequent whole-genome sequencing revealed that the virus had accumulated several mutations in genes associated with viral replication and pathogenesis. These variants showed enhanced transmissibility and infectivity. Soon after the first outbreak due to the wild-type strain in December 2019, a genetic variant D614G emerged in late January to early February 2020 and became the dominant genotype worldwide. Thereafter, several variants emerged, which were found to harbor mutations in essential viral genes encoding proteins that could act as drug and vaccine targets. Numerous vaccines have been successfully developed to assuage the burden of COVID-19. These have different rates of efficacy, including, although rarely, a number of vaccinated individuals exhibiting side effects like thrombosis. However, the recent emergence of the Britain strain with 70% more transmissibility and South African variants with higher resistance to vaccines at a time when several countries have approved these for mass immunization has raised tremendous concern regarding the long-lasting impact of currently available prophylaxis. Apart from studies addressing the pathophysiology, pathogenesis, and therapeutic targets of SARS-CoV-2, analysis of the gut, oral, nasopharyngeal, and lung microbiome dysbiosis has also been undertaken to find a link between the microbiome and the pathogenesis of COVID-19. Therefore, in the current scenario of skepticism regarding vaccine efficacy and challenges over the direct effects of currently available drugs looming large, investigation of alternative therapeutic avenues based on the microbiome can be a rewarding finding. This review presents the currently available understanding of microbiome dysbiosis and its association with cause and consequence of COVID-19. Taking cues from other inflammatory diseases, we propose a hypothesis of how the microbiome may be influencing homeostasis, pro-inflammatory condition, and the onset of inflammation. This accentuates the importance of a healthy microbiome as a protective element to prevent the onset of COVID-19. Finally, the review attempts to identify areas where the application of microbiome research can help in reducing the burden of the disease.

Updating the use of nano-biosensors as promising devices for the diagnosis of coronavirus family members: A systematic review

Coronavidae viruses, such as SARS-CoV, SARS-CoV-2, and MERS-CoV, cause severe lower respiratory tract infection, acute respiratory distress syndrome and extrapulmonary manifestations, such as diarrhea and fever, eventually leading to death. Fast, accurate, reproductible, and cost-effective SARS-CoV-2 identification can be achieved employing nano-biosensors, reinforcing conventional methodologies to avoid the spread of COVID-19 within and across communities. Nano-biosensors built using gold, silver, graphene, In2O3 nanowire and iron oxide nanoparticles, Quantum Dots and carbon nanofibers have been successfully employed to detect specific virus antigens - nucleic acid sequences and/or proteins -or host antibodies produced in response to viral infection. Biorecognition counterpart molecules have been immobilized on the surface of these nanomaterials, leading to selective virus detection by optical or electrochemical transducer systems. This systematic review assessed studies on described and tested immunonsensors and genosensors designed from distinct nanomaterials available at the Pubmed, Scopus, and Science Direct databases. Twenty-three nano biosensors were found suitable for unequivocal coronavirus detection in clinical samples. Nano-biosensors coupled to RT-LAMP/RT-PCR assays can optimize RNA extraction, reduce analysis times and/or eliminate sophisticated instrumentation. Although promising for the diagnosis of Coronavidae family members, further trials in large populations must be adequately and rigorously conducted to address nano-biosensor applicability in the clinical practice for early coronavirus infection detection.

Immune Responses in SARS-CoV-2, SARS-CoV, and MERS-CoV Infections: A Comparative Review

Coronavirus, discovered in the 1960s, is able to infect human hosts and causes mild to serious respiratory problems. In the last two decades, the severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been recognized. It has long been demonstrated that MERS-CoV binds to dipeptidyl peptidase 4 and SARS-CoV binds to angiotensin-converting enzyme 2. A "cytokine storm" is the main pathophysiology of aforementioned viruses. Infiltration of neutrophils at the site of the infection is a risk factor for the development of acute respiratory distress syndrome and death. The new coronavirus, SARS-CoV-2, has infected more people than SARS-Cov and MERS-CoV as it can easily be transmitted from person to person. Epidemiological studies indicate that majority of individuals are asymptomatic; therefore, an effective and an efficient tool is required for rapid testing. Identification of various cytokine and inflammatory factor expression levels can help in outcome prediction. In this study we reviewed immune responses in SARS-CoV, Mers-CoV, and SARS-COV-2 infections and the role of inflammatory cells.

Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.

Masks for COVID-19

Sustainable solutions on fabricating and using a face mask to block the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread during this coronavirus pandemic of 2019 (COVID-19) are required as society is directed by the World Health Organization (WHO) toward wearing it, resulting in an increasingly huge demand with over 4 000 000 000 masks used per day globally. Herein, various new mask technologies and advanced materials are reviewed to deal with critical shortages, cross-infection, and secondary transmission risk of masks. A number of countries have used cloth masks and 3D-printed masks as substitutes, whose filtration efficiencies can be improved by using nanofibers or mixing other polymers into them. Since 2020, researchers continue to improve the performance of masks by adding various functionalities, for example using metal nanoparticles and herbal extracts to inactivate pathogens, using graphene to make masks photothermal and superhydrophobic, and using triboelectric nanogenerator (TENG) to prolong mask lifetime. The recent advances in material technology have led to the development of antimicrobial coatings, which are introduced in this review. When incorporated into masks, these advanced materials and technologies can aid in the prevention of secondary transmission of the virus.

SARS, MERS and CoVID-19: An overview and comparison of clinical, laboratory and radiological features

In the 21st century, we have seen a total of three outbreaks by members of the coronavirus family. Although the first two outbreaks did not result in a pandemic, the third and the latest outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) culminated in a pandemic. This pandemic has been extremely significant on a social and international level. As these viruses belong to the same family, they are closely related. Despite their numerous similarities, they have slight distinctions that render them distinct from one another. The Severe Acute Respiratory Distress Syndrome and Middle East Respiratory Syndrome (MERS) cases were reported to have a very high case fatality rate of 9.5 and 34.4% respectively. In contrast, the CoVID-19 has a case fatality rate of 2.13%. Also, there are no clear medical countermeasures for these coronaviruses yet. We can cross information gaps, including cultural weapons for fighting and controlling the spread of MERS-CoV and SARS-CoV-2, and plan efficient and comprehensive defensive lines against coronaviruses that might arise or reemerge in the future by gaining a deeper understanding of these coronaviruses and the illnesses caused by them. The review thoroughly summarises the state-of-the-art information and compares the biochemical properties of these deadly coronaviruses with the clinical characteristics, laboratory features and radiological manifestations of illnesses induced by them, with an emphasis on comparing and contrasting their similarities and differences.

Coronavirus Disease (COVID-19) Control between Drug Repurposing and Vaccination: A Comprehensive Overview

Respiratory viruses represent a major public health concern, as they are highly mutated, resulting in new strains emerging with high pathogenicity. Currently, the world is suffering from the newly evolving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus is the cause of coronavirus disease 2019 (COVID-19), a mild-to-severe respiratory tract infection with frequent ability to give rise to fatal pneumonia in humans. The overwhelming outbreak of SARS-CoV-2 continues to unfold all over the world, urging scientists to put an end to this global pandemic through biological and pharmaceutical interventions. Currently, there is no specific treatment option that is capable of COVID-19 pandemic eradication, so several repurposed drugs and newly conditionally approved vaccines are in use and heavily applied to control the COVID-19 pandemic. The emergence of new variants of the virus that partially or totally escape from the immune response elicited by the approved vaccines requires continuous monitoring of the emerging variants to update the content of the developed vaccines or modify them totally to match the new variants. Herein, we discuss the potential therapeutic and prophylactic interventions including repurposed drugs and the newly developed/approved vaccines, highlighting the impact of virus evolution on the immune evasion of the virus from currently licensed vaccines for COVID-19.

Economic evaluation of remdesivir for the treatment of severe COVID-19 patients in China under different scenarios

AIMS

The present study aimed to evaluate the cost-effectiveness of the 5-day remdesivir regimen compared with standard of care among severe COVID-19 patients in China, the evidence on which is essential to inform the necessity of securing access to remdesivir.

METHODS

A dynamic transmission model that extended the susceptible-exposed-infected-recovered framework by incorporating asymptomatic, presymptomatic and waiting-to-be-diagnosed patients was constructed to conduct the cost-effectiveness analysis from the healthcare system perspective. To estimate epidemic parameters, the model was first calibrated to the observed epidemic curve in Wuhan from 23 January to 19 March 2020. Following the calibration, the infected compartment was replaced by 3 severity-defined health states to reflect differential costs and quality of life associated with disease gravity. Costs and quality-adjusted life year (QALY) outcomes of 9 million simulated people were accrued across time to evaluate the incremental cost-effectiveness ratio of remdesivir. As robustness checks, an alternative modelling technique using decision tree, additional epidemic scenarios representing different epidemic intensities, and 1-way parameter variations were also analysed.

RESULTS

Remdesivir treatment cost CN¥97.93 million more than standard of care. Also, the net QALY gain from 5-day remdesivir treatment was 6947 QALYs. As such, the incremental cost-effectiveness ratio was CN¥14 098/QALY, substantially lower than the gross domestic product per capita threshold. The peak daily number of severe cases was 19% lower in the remdesivir treatment strategy. Overall, results were robust in alternative scenarios and sensitivity analyses.

CONCLUSION

Given the cost-effectiveness profile, access to remdesivir for severe COVID-19 patients in China should be considered.

Impact of SARS-CoV-2 on pregnancy outcomes (Review)

The impact of the pandemic outbreak associated with coronavirus 2019 disease (COVID-19) on pregnant women is of interest to obstetricians and gynecologists due to the vulnerability of this target group. In pregnant women and their infants, an exceptional clinical management is warranted. Current epidemiological findings provide information regarding the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on pregnant patients and potential adverse perinatal outcomes. Overall, these findings are a strong indication that an increased antenatal surveillance for pregnant patients infected with COVID-19 is warranted. The aim of the present narrative review was to summarize the data obtained to date regarding the health of women during pregnancy, as well as that of the fetus associated with the risk of severe infection due to COVID-19. The present review aimed to provide further insight into the effects of this pandemic on pregnancy, also providing the experience of the authors on this matter as an example.

Association of diabetes with severity and mortality in hospitalized patients with COVID-19 in Wuhan, China: a single-centered, retrospective study

OBJECTIVE

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide. The aim this study was to investigate the association of diabetes with severity and mortality among hospitalized patients with COVID-19 in Wuhan, China.

METHODS

This retrospective, single-center case study enrolled a total of 564 patients diagnosed with COVID-19 at the Seventh Hospital of Wuhan City, between January 20 and March 15, 2020.

RESULTS

Among the 564 patients with confirmed COVID-19, 509 (85.1%) were discharged and 55 (9.8%) died. The median age was 59 years (range, 10-93 years). A total of 85 (15.1%) patients were diagnosed with diabetes on admission (median age, 65.0 [range, 34-91] years). Patients with diabetes had significantly higher proportions of critical cases (24 [28.2%] vs. 66 [13.8%]) and in-hospital mortality (17 [20%] vs. 38 [7.9%]). Moreover, patients with diabetes presented abnormal levels of multiple indicators concerning lymphopenia, inflammation, heart, liver, kidney, and lung function on admission, while diabetic patient group still display higher troponin T (TnT) levels when approaching discharge. The Kaplan-Meier survival curve indicated a trend toward poorer survival in diabetic patients compared to non-diabetic patients, also evidenced by abnormal laboratory biomarker changes regarding multiple system impairments among COVID-19 patients with diabetes with in-hospital death.

CONCLUSION

The detailed clinical investigation of 564 hospitalized patients with COVID-19 indicated a considerable association between diabetes and COVID-19 severity or mortality. Thus, more intensive treatment may be considered for COVID-19 patients with diabetes, especially regarding to cardiac injury.

Surface Functionalized Anodic Aluminum Oxide Membrane for Opto-Nanofluidic SARS-CoV-2 Genomic Target Detection

An ultra-thin and highly sensitive SARS-CoV-2 detection platform was demonstrated using a nano-porous anodic aluminum oxide (AAO) membrane. The membrane surface was functionalized to enable efficient trapping and identification of SARS-CoV-2 genomic targets through DNA-DNA and DNA-RNA hybridization. To immobilize the probe oligonucleotides on the AAO membrane, the pore surface was first coated with the linking reagents, 3-aminopropyltrimethoxysilane (APTMS) and glutaraldehyde (GA), by a compact vacuum infiltration module. After that, complementary target oligos with fluorescent modifier was pulled and infiltrated into the nano-fluidic channels formed by the AAO pores. The fluorescent signal applying the AAO membrane sensors was two orders stronger than a flat glass template. In addition, the dependence between the nano-pore size and the fluorescent intensity was evaluated. The optimized pore diameter d is 200 nm, which can accommodate the assembled oligonucleotide and aminosilane layers without blocking the AAO nano-fluidic channels. Our DNA functionalized membrane sensor is an accurate and high throughput platform supporting rapid virus tests, which is critical for population-wide diagnostic applications result in a page being rejected by search engines.

Evolution of RNA viruses from SARS to SARS-CoV-2 and diagnostic techniques for COVID-19: a review

Background

From the start of the twenty-first century up to the year 2021, RNA viruses are the main causative agents of the majority of the disease outbreaks the world has confronted. Recently published reviews on SARS-CoV-2 have mainly focused on its structure, development of the outbreak, relevant precautions, management trials and available therapies. However, in this review, we aim to explore the history, evolution of all coronaviruses and the associated viral outbreaks along with the diagnostics for COVID-19 in the twenty-first century.

Main body

We have focused on different RNA viruses’ viz. SARS-CoV, MERS-CoV, and SARS-CoV-2, their classification, and the various disease outbreaks caused by them. In the subsequent section, the comparison of different RNA viruses affecting humans has been made based on the viral genome, structure, time of the outbreak, mode of spread, virulence, causative agents, and transmission. Due to the current mayhem caused by the rapidly emerging virus, special attention is given to SARS-CoV-2, its genome updates, and infectivity. Finally, the current diagnostic techniques such as nucleic acid testing (real time-polymerase chain reaction and loop-mediated isothermal amplification), CRISPR-based diagnostics (CRISPR based DETECTR assay, CRISPR based SHERLOCK test, AIOD-CRISPR, FELUDA, CREST), chest radiographs (computed tomography, X-ray), and serological tests (Lateral flow assay, enzyme-linked immunosorbent assay, chemiluminescent immunoassay, neutralization assay, nano-sensors, blood test, viral sequencing) with their pros and cons, and future diagnostic prospective have been described.

Conclusions

The present gloomy scenario mandates clinical manifestations, contact tracing, and laboratory tests as important parameters that need to be taken into consideration to make the final diagnosis.

Epidemiology of COVID-19: An updated review

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a zoonotic infection, is responsible for COVID-19 pandemic and also is known as a public health concern. However, so far, the origin of the causative virus and its intermediate hosts is yet to be fully determined. SARS-CoV-2 contains nearly 30,000 letters of RNA that allows the virus to infect cells and hijack them to make new viruses. On the other hand, among 14 detected mutations in the SARS-CoV-2 S protein that provide advantages to virus for transmission and evasion form treatment, the D614G mutation (substitution of aspartic acid [D] with glycine [G] in codon 614 was particular which could provide the facilitation of the transmission of the virus and virulence. To date, in contrary to the global effort to come up with various aspects of SARS-CoV-2, there are still great pitfalls in the knowledge of this disease and many angles remain unclear. That's why, the monitoring and periodical investigation of this emerging infection in an epidemiological study seems to be essential. The present study characterizes the current epidemiological status (i.e., possible transmission route, mortality and morbidity risk, emerging SARS-CoV-2 variants, and clinical feature) of the SARS-CoV-2 in the world during these pandemic.

Common Symptoms in COVID-19 Confirmed Cases Followed-Up at Home by Primary Healthcare Services

Aim: This study aims to determine common symptoms in COVID-19 confirmed patients who were followed up at home by primary health care services. Methods: This descriptive study was conducted in Kırklareli, Turkey between July 2020 and December 2020. The study was conducted with 315 patients aged 18 years and over who were confirmed positive by the Polymerase Chain Reaction test and followed up at home by a Family Health Center. Results: The mean age of the participants was 43.13±16.33 years. 53.7% were male, 87% lived in rural areas, 98.1% had health insurance and 54.9% were employed. Of the adults, 5.1% had the adult vaccine and 25.4% had a chronic disease. While at least one symptom was observed in 84.8% of the patients, 15.2% had no symptoms. The symptoms detected among the participants were cough (26.3%), headache (26.3%), and high fever (24.1%). The frequency of symptoms was higher in women than in men, and myalgia, arthralgia, nausea, and vomiting were also more frequent. The symptom of chills was more common in adults at 40 years and over compared to those under 40. Conclusion: In case of cough, headache, or high fever among adults, a healthcare provider should be consulted and screening for other symptoms. In terms of follow-up, symptom management, and treatment of the disease, women, and individuals 40 years and over should be prioritized, carefully followed up, and provided with the necessary isolation. There are also asymptomatic cases of COVID-19 disease. Isolation of asymptomatic patients is also important because of the high contagiousness of the disease. Keywords: COVID-19, symptoms, primary care, home care

Mapping the footprints of COVID-19 pandemic

The new member of the coronavirus family created havoc in the world in few days of its discovery and was declared as a pandemic by March 2020. The enveloped, single-stranded RNA virus was first identified in a patient with dry cough, pain, and weakness for the past1 week with unknown etiology in Wuhan. The coronavirus soon spread across the globe in the next few weeks. To curb the spread of coronavirus, stringent measures such as lockdown and social distancing were enforced resulted in a declining of cases in various countries. However, unlocking, relaxation of preventive measures, and changing human behaviors led to a drastic increase in the number of COVID-19 cases resulting in the second wave of transmission. This resulted in reimposing of lockdown measures in countries such as Nepal, Italy, France, Netherland, and Germany. At present, the virus has affected over 110.7 million peoples and over 2.4 million deaths across the world, with India having the second-highest number of COVID-19 cases, following the United States of America. Furthermore, a cross-sectional view of the disease states several new strains being reported across the globe at one end and at another end there is rolling out of vaccine against COVID-19. There is still uncertainty related to curbing of the pandemic as effect of vaccine on new strains is undetermined. Thus, it is important to understand the transforming epidemiology of the virus as it helps in planning necessary steps for physicians and policymakers. The present review summarizes the updated information primarily about the epidemiology of COVID-19, from initiation to the present scenario.

Early reports of epidemiological parameters of the COVID-19 pandemic

Background

The emergence of a new pathogen requires a rapid assessment of its transmissibility, to inform appropriate public health interventions.

Methods

The peer-reviewed literature published between 1 January and 30 April 2020 on COVID-19 in PubMed was searched. Estimates of the incubation period, serial interval and reproduction number for COVID-19 were obtained and compared.

Results

A total of 86 studies met the inclusion criteria. Of these, 33 estimated the mean incubation period (4–7 days) and 15 included estimates of the serial interval (mean 4–8 days; median length 4–5 days). Fifty-two studies estimated the reproduction number. Although reproduction number estimates ranged from 0.3 to 14.8, in 33 studies (63%), they fell between 2 and 3.

Discussion

Studies calculating the incubation period and effective reproduction number were published from the beginning of the pandemic until the end of the study period (30 April 2020); however, most of the studies calculating the serial interval were published in April 2020. The calculated incubation period was similar over the study period and in different settings, whereas estimates of the serial interval and effective reproduction number were setting-specific. Estimates of the serial interval were shorter at the end of the study period as increasing evidence of pre-symptomatic transmission was documented and as jurisdictions enacted outbreak control measures. Estimates of the effective reproduction number varied with the setting and the underlying model assumptions. Early analysis of epidemic parameters provides vital information to inform the outbreak response.

Multiepitope Proteins for the Differential Detection of IgG Antibodies against RBD of the Spike Protein and Non-RBD Regions of SARS-CoV-2

The COVID-19 pandemic has exposed the extent of global connectivity and collective vulnerability to emerging diseases. From its suspected origins in Wuhan, China, it spread to all corners of the world in a matter of months. The absence of high-performance, rapid diagnostic methods that could identify asymptomatic carriers contributed to its worldwide transmission. Serological tests offer numerous benefits compared to other assay platforms to screen large populations. First-generation assays contain targets that represent proteins from SARS-CoV-2. While they could be quickly produced, each actually has a mixture of specific and non-specific epitopes that vary in their reactivity for antibodies. To generate the next generation of the assay, epitopes were identified in three SARS-Cov-2 proteins (S, N, and Orf3a) by SPOT synthesis analysis. After their similarity to other pathogen sequences was analyzed, 11 epitopes outside of the receptor-binding domain (RBD) of the spike protein that showed high reactivity and uniqueness to the virus. These were incorporated into a ß-barrel protein core to create a highly chimeric protein. Another de novo protein was designed that contained only epitopes in the RBD. In-house ELISAs suggest that both multiepitope proteins can serve as targets for high-performance diagnostic tests. Our approach to bioengineer chimeric proteins is highly amenable to other pathogens and immunological uses.

Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review

The objective of this review was to summarize the most pertinent CT imaging findings in patients with coronavirus disease 2019 (COVID-19). A literature search retrieved eligible studies in PubMed, EMBASE, Cochrane Library and Web of Science up to June 1, 2020. A comprehensive review of publications of the Chinese Medical Association about COVID-19 was also performed. A total of 84 articles with more than 5340 participants were included and reviewed. Chest CT comprised 92.61% of abnormal CT findings overall. Compared with real-time polymerase chain reaction result, CT findings has a sensitivity of 96.14% but a low specificity of 40.48% in diagnosing COVID-19. Ground glass opacity (GGO), pure (57.31%) or mixed with consolidation (41.51%) were the most common CT features with a majority of bilateral (80.32%) and peripheral (66.21%) lung involvement. The opacity might associate with other imaging features, including air bronchogram (41.07%), vascular enlargement (54.33%), bronchial wall thickening (19.12%), crazy-paving pattern (27.55%), interlobular septal thickening (42.48%), halo sign (25.48%), reverse halo sign (12.29%), bronchiectasis (32.44%), and pulmonary fibrosis (26.22%). Other accompanying signs including pleural effusion, lymphadenopathy and pericardial effusion were rare, but pleural thickening was common. The younger or early stage patients tended to have more GGOs, while extensive/multilobar involvement with consolidation was prevalent in the older or severe population. Children with COVID-19 showed significantly lower incidences of some ancillary findings than those of adults and showed a better performance on CT during follow up. Follow-up CT showed GGO lesions gradually decreased, and the consolidation lesions first increased and then remained relatively stable at 6-13 days, and then absorbed and fibrosis increased after 14 days. Chest CT imaging is an important component in the diagnosis, staging, disease progression and follow-up of patients with COVID-19.

Clustered Regularly Interspaced Short Palindromic Repeats-Mediated Amplification-Free Detection of Viral DNAs Using Surface-Enhanced Raman Spectroscopy-Active Nanoarray

Nucleic acid biomarkers have been widely used to detect various viral-associated diseases, including the recent pandemic COVID-19. The CRISPR-Cas-based trans-activating phenomenon has shown excellent potential for developing sensitive and selective detection of nucleic acids. However, the nucleic acid amplification steps are typically required when sensitive and selective monitoring of the target nucleic acid is needed. To overcome the aforementioned challenges, we developed a CRISPR-Cas12a-based nucleic acid amplification-free biosensor by a surface-enhanced Raman spectroscopy (SERS)-assisted ultrasensitive detection system. We integrated the activated CRISPR-Cas12a by viral DNA with a Raman-sensitive system composed of ssDNA-immobilized Raman probe-functionalized Au nanoparticles (RAuNPs) on the graphene oxide (GO)/triangle Au nanoflower array. Using this CRISPR-based Raman-sensitive system improved the detection sensitivity of the multiviral DNAs such as hepatitis B virus (HBV), human papillomavirus 16 (HPV-16), and HPV-18 with an extremely low detection limit and vast detection range from 1 aM to 100 pM without the amplification steps. We suggest that this ultrasensitive amplification-free detection system for nucleic acids can be widely applied to the precise and early diagnosis of viral infections, cancers, and several genetic diseases.

Prevalence and significance of pulmonary disease on lung ultrasonography in outpatients with SARS-CoV-2 infection

BACKGROUND

The majority of patients with SARS-CoV-2 infection are diagnosed and managed as outpatients; however, little is known about the burden of pulmonary disease in this setting. Lung ultrasound (LUS) is a convenient tool for detection of COVID-19 pneumonia. Identifying SARS-CoV-2 infected outpatients with pulmonary disease may be important for early risk stratification.

OBJECTIVES

To investigate the prevalence, natural history and clinical significance of pulmonary disease in outpatients with SARS-CoV-2.

METHODS

SARS-CoV-2 PCR positive outpatients (CV(+)) were assessed with LUS to identify the presence of interstitial pneumonia. Studies were considered positive based on the presence of B-lines, pleural irregularity and consolidations. A subset of patients underwent longitudinal examinations. Correlations between LUS findings and patient symptoms, demographics, comorbidities and clinical outcomes over 8 weeks were evaluated.

RESULTS

102 CV(+) patients underwent LUS with 42 (41%) demonstrating pulmonary involvement. Baseline LUS severity scores correlated with shortness of breath on multivariate analysis. Of the CV(+) patients followed longitudinally, a majority showed improvement or resolution in LUS findings after 1-2 weeks. Only one patient in the CV(+) cohort was briefly hospitalised, and no patient died or required mechanical ventilation.

CONCLUSION

We found a high prevalence of LUS findings in outpatients with SARS-CoV-2 infection. Given the pervasiveness of pulmonary disease across a broad spectrum of LUS severity scores and lack of adverse outcomes, our findings suggest that LUS may not be a useful as a risk stratification tool in SARS-CoV-2 in the general outpatient population.

COVID-19 and Progesterone: Part 2. Unraveling High Severity, Immunity Patterns, Immunity grading, Progesterone and its potential clinical use

Severely ill COVID–19 (Corona Virus Disease of 2019) patients have a hyperinflammatory condition with a high concentration of pro-inflammatory cytokines termed the cytokine storm. This milieu is reported to cause acute lung injury, oxygen deprivation, multiorgan damage, critical illness, and often death. Post SARS–CoV–2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection, the fight between the invading virus and the host's immune system would either terminate in recovery, with eradication of the infection and regulation of the immune system; or there would be a continuation of immune attacks even after the virus has been cleared, leading to immune dysregulation and disease. This outcome is chiefly dependent on two factors: (1) the patient's immune response, and (2) sufficiency plus efficiency of the regulator(s). Concerning the first, the present research introduces a framework based on different types of immune responses to SARS–CoV–2 along with known disease examples, and how this relates to varying clinical outcomes and treatment needs for COVID–19 patients. About the second factor of ‘regulator(s)’, part 1 of the manuscript described in depth the regulatory role of progesterone in COVID–19. The present study investigates five immunity patterns and the status of the regulatory hormone progesterone with respect to the two established demographic risk factors for COVID–19 high-severity: male sex, and old age. The study evaluates the status of progesterone as a credible determinant of immune regulation and dysregulation. It duly relates the immunity patterns to clinical outcomes and evinces indications for clinical use of progesterone in COVID–19. It proposes a clear answer to the question: "why are males and old patients most likely to have critical illness due to COVID–19?" The study highlights clinical domains for the use of progesterone in COVID–19. Part 2 of this research introduces the concept of immunity patterns and immunity grading. These concepts herewith provided for the clinical course of COVID–19 also apply to other hyperinflammatory conditions. Possible clinical applications of progesterone to treat critically ill COVID–19 patients will open an avenue for hormonal treatments of infections and other immune-related diseases.

Nurses' burnout and associated risk factors during the COVID-19 pandemic: A systematic review and meta-analysis

AIMS

To examine the nurses' burnout and associated risk factors during the COVID-19 pandemic.

DESIGN

We followed the Cochrane criteria and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines for this systematic review and meta-analysis.

DATA SOURCES

PubMed, Scopus, ProQuest, Cochrane COVID-19 registry, CINAHL and pre-print services (medRχiv and PsyArXiv) were searched from January 1 to November 15, 2020 and we removed duplicates.

REVIEW METHODS

We applied a random effect model to estimate pooled effects since the heterogeneity between results was very high.

RESULTS

Sixteen studies, including 18,935 nurses met the inclusion criteria. The overall prevalence of emotional exhaustion was 34.1%, of depersonalization was 12.6% and of lack of personal accomplishment was 15.2%. The main risk factors that increased nurses' burnout were the following: younger age, decreased social support, low family and colleagues readiness to cope with COVID-19 outbreak, increased perceived threat of Covid-19, longer working time in quarantine areas, working in a high-risk environment, working in hospitals with inadequate and insufficient material and human resources, increased workload and lower level of specialized training regarding COVID-19.

CONCLUSION

Nurses experience high levels of burnout during the COVID-19 pandemic, while several sociodemographic, social and occupational factors affect this burnout.

IMPACT

We found that burnout among nurses is a crucial issue during the COVID-19 pandemic. There is an urgent need to prepare nurses to cope better with COVID-19 pandemic. Identification of risk factors for burnout could be a significant weapon giving nurses and health care systems the ability to response in a better way against the following COVID-19 waves in the near future.

A comprehensive review of COVID-19 in India: A frequent catch of the information

The arrival of novel health crisis by a novel member of coronavirus group named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by World Health Organization took the whole world in global emergency by affecting 206 countries. The virus infects 206 countries with 86,839,226 confirmed cases, 61,565,949 recoveries, and 1,876,243 deaths as on January 6, 2021. Evidence pointed out the fact that virus might first originated in bats in China and it took only 2 months to spread over almost every country of the world. SARS-CoV-2 belongs to beta coronavirus and is enveloped, positive sense, and single-stranded RNA virus. The treatment would be difficult as SARS-CoV-2 is an RNA virus and thus the mutation rate is higher in comparison with the DNA viruses. The virus infection also leads to generation of effective protective immune response of tumor necrosis factor, interleukin (IL)-1β, IL-6, IL-8, granulocyte colony-stimulating factor, granulocyte-macrophage colony-stimulating factor, and so on that may help in virus elimination. The speed of the global spread of the current pandemic is of major concern and it has created a significant threat to economic and human health across the world. In India, the infection spreads with an infection and fatality rates of the disease are 1.7% and 2.8%, respectively. By this review, we want to emphasize the actual situation and major factors associated with COVID-19 pandemic, its significance, destructions, important findings, treatments, and preventive measures taken by all nations to provide better cure without having much loss.

Epidemiological characteristics and incubation period of SARS-CoV-2 during the 2020-2021 winter pandemic wave in North China: An observational study

As the emergence of new variants of SARS‐CoV‐2 persists across the world, it is of importance to understand the distributional behavior of the incubation period of the variants for both medical research and public health policy‐making. We collected the published individual‐level data of 941 patients of the 2020–2021 winter pandemic wave in Hebei province, North China. We computed some epidemiological characteristics of the wave and estimated the distribution of the incubation period. We further assessed the covariate effects of sex, age, and living with a case with respect to the incubation period by a model. The infection‐fatality rate was only 0.1%. The estimated median incubation period was at least 22 days, significantly extended from the estimates (ranging from 4 to 8.5 days) of the previous wave in mainland China and those ever reported elsewhere around the world. The proportion of asymptomatic patients was 90.6%. No significant covariate effect was found. The distribution of incubation period of the new variants showed a clear extension from their early generations.

Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study

OBJECTIVES

This study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated.

SETTING

We developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a 'T' compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed.

PRIMARY AND SECONDARY OUTCOME MEASURES

Simulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people.

RESULTS

(1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate.

CONCLUSIONS

Reducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.

Relative synonymous codon usage of ORF1ab in SARS-CoV-2 and SARS-CoV

Background

COVID-19, as a novel coronavirus disease caused by new coronavirus SARS-CoV-2, spreads all over the world, and brings harm to human in many countries. Humans suffered a lot from both SARS-CoV-2 now and by SARS-CoV in the year 2003. It is important to understand the differences and the relationships between these two types of viruses.

Objective

To compare relative synonymous codon usage of ORF1ab gene in SARS-CoV-2 and SARS-CoV, relative synonymous codon usage of their genomes are studied in this paper from the bioinformatics perspective.

Methods

The ORF1ab gene, which is an important non-structural polyprotein coding gene and now used for nucleic acid detection markers in many measurement method, in both SARS-CoV-2 (30 strains) and SARS-CoV (20 strains) are considered to be the research object in the present paper. The relative synonymous codon usage values of the ORF1ab gene are calculated to characterize the differences and the evolutionary characteristics among 50 strains.

Results

There is a significant difference between SARS-CoV and SARS-CoV-2 when the relative synonymous codon usage value of ORF1ab genes is concerned. The results suggest that codon usage pattern of SARS-CoV is more similar to human than that of the SARS-CoV-2, and that the inner difference in SARS-CoV-2 strains is larger than that of SARS-CoV, which denote the larger diversity exits in the SARS-CoV-2 virus.

Conclusion

These results show that the relative synonymous codon usage values in the coronavirus could be used for further research on their evolutionary phenomenon.

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers

Background

The outbreak of COVID-19 has been defined by the World Health Organization as a pandemic, and containment depends on traditional public health measures. However, the explosive growth of the number of infected cases in a short period of time has caused tremendous pressure on medical systems. Adequate isolation facilities are essential to control outbreaks, so this study aims to quickly estimate the demand and number of isolation beds.

Methods

We established a discrete simulation model for epidemiology. By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility. Finally, a method for predicting the number of isolation beds was summarized through multiple linear regression. This is a city level model that simulates the epidemic situation from the perspective of population mobility.

Results

Through simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease healing time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility have a significant impact on the demand and number of isolation beds (P <0.05), which agrees with the following regression equation: N = P × (−0.273 + 0.009I + 0.234M + 0.012T1 + 0.015T2) × (1 + V).

Origin, Pathogenesis, Diagnosis and Treatment Options for SARS-CoV-2: A Review

Emerging viral infections are among the greatest challenges in the public health sector in the twenty-first century. Among these, most of the viruses jump from other species of animals to humans called zoonotic viruses. The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), by crossing species-barrier, has infected the human population for the third time in the current century and has caused the coronavirus disease-2019 (COVID-19) . Mutation and adaptation for years have greatly influenced the co-evolution and existence of coronaviruses and their possible hosts including humans. The appearance of SARS-CoV-2 in China thrust coronaviruses into the limelight and shocked the world. Presently, no coronavirus vaccines are clinically available to combat the virus's devastating effects. To counter the emergence of the COVID-19 pandemic, it is therefore important to understand the complex nature of coronaviruses and their clinical attributes. SARS and MERS outbreaks had ultimately led to socio-economic deprivation in the previous decades. In addressing the recent disastrous situation, the COVID-19 pandemic still needs some lessons from prior experience. In this review, we have highlighted the chronological order of coronavirus strains, their genomic features, the mechanism of action of SARS-CoV-2, and its disastrous repercussions on the world. We have also suggested some therapeutic options that could be effective against the COVID-19.

Monitoring scheme for early detection of coronavirus and other respiratory virus outbreaks

In December 2019, an outbreak of pneumonia caused by a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) began in Wuhan, China. SARS-CoV-2 exhibited efficient person-to-person transmission of what became labeled as COVID-19. It has spread worldwide with over 83,000,000 infected cases and more than 1,800,000 deaths to date (December 31, 2020). This research proposes a statistical monitoring scheme in which an optimized np control chart is utilized by sentinel metropolitan airports worldwide for early detection of coronavirus and other respiratory virus outbreaks. The sample size of this chart is optimized to ensure the best overall performance for detecting a wide range of shifts in the infection rate, based on the available resources, such as the inspection rate and the allowable false alarm rate. The effectiveness of the proposed optimized np chart is compared with that of the traditional np chart with a predetermined sample size under both sampling inspection and 100% inspection. For a variety of scenarios including a real case, the optimized np control chart is found to substantially outperform its traditional counterpart in terms of the average number of infections. Therefore, this control chart has potential to be an effective tool for early detection of respiratory virus outbreaks, promoting early outbreak investigation and mitigation.

Review of epidemic, containment strategies, clinical management, and economic evaluation of COVID-19 pandemic

The spread of the emerging pathogen, named as SARS-CoV-2, has led to an unprecedented COVID-19 pandemic since 1918 influenza pandemic. This review first sheds light on the similarity on global transmission, surges of pandemics, and the disparity of prevention between two pandemics. Such a brief comparison also provides an insight into the potential sequelae of COVID-19 based on the inference drawn from the fact that a cascade of successive influenza pandemic occurred after 1918 and also the previous experience on the epidemic of SARS and MERS occurring in 2003 and 2015, respectively. We then propose a systematic framework for elucidating emerging infectious disease (EID) such as COVID-19 with a panorama viewpoint from natural infection and disease process, public health interventions (non-pharmaceutical interventions (NPIs) and vaccine), clinical treatments and therapies (antivirals), until global aspects of health and economic loss, and economic evaluation of interventions with emphasis on mass vaccination. This review not only concisely delves for evidence-based scientific literatures from the origin of outbreak, the spread of SARS-CoV-2 to three surges of pandemic, and NPIs and vaccine uptakes but also provides a new insight into how to apply big data analytics to identify unprecedented discoveries through COVID-19 pandemic scenario embracing from biomedical to economic viewpoints.

Monitoring Symptoms of Infectious Diseases: Perspectives for Printed Wearable Sensors

Infectious diseases possess a serious threat to the world's population, economies, and healthcare systems. In this review, we cover the infectious diseases that are most likely to cause a pandemic according to the WHO (World Health Organization). The list includes COVID-19, Crimean-Congo Hemorrhagic Fever (CCHF), Ebola Virus Disease (EBOV), Marburg Virus Disease (MARV), Lassa Hemorrhagic Fever (LHF), Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), Nipah Virus diseases (NiV), and Rift Valley fever (RVF). This review also investigates research trends in infectious diseases by analyzing published research history on each disease from 2000-2020 in PubMed. A comprehensive review of sensor printing methods including flexographic printing, gravure printing, inkjet printing, and screen printing is conducted to provide guidelines for the best method depending on the printing scale, resolution, design modification ability, and other requirements. Printed sensors for respiratory rate, heart rate, oxygen saturation, body temperature, and blood pressure are reviewed for the possibility of being used for disease symptom monitoring. Printed wearable sensors are of great potential for continuous monitoring of vital signs in patients and the quarantined as tools for epidemiological screening.

The Incubation Period of COVID-19: Current Understanding and Modeling Technique

This chapter aims to answer the following questions regarding the incubation period of COVID-19. Why is understanding the incubation period of COVID-19 important? How long is the incubation time, and what are the associating factors? How should the incubation period be modeled given the current pandemic situation? Where should we go from here? As a critical epidemiological metric, the incubation period is of public health and clinical importance. While the incubation time of COVID-19 is generally similar to that of SARS and MERS, recent studies identifying factors that impact the incubation period of COVID-19, travel history, for example, only tell part of the story. Therefore, in addition to reviewing current findings, this chapter also explores the modeling technique and future research directions of the incubation period of COVID-19.