Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

Social media use for coping with stress and psychological adjustment: A transactional model of stress and coping perspective

Introduction

The psychological well-being of individuals has become an essential issue during the global pandemic. As a pervasive activity for individuals to pull through COVID-19, social media use may play a role in psychological well-being. Drawing on the transactional model of stress and coping, the current study investigated the relationships between COVID-19-related stressors and the use of social media to facilitate specific coping strategies. We further investigated how social media coping strategies impact users' psychological adjustment.

Methods

We collected the data from 641 quarantined residents through a two-wave survey that was conducted in two cities in China during government-mandated lockdowns.

Results

The results showed that perceived COVID-19 stress was related to the intensity of social media use for problem-focused coping, socioemotional coping, and mental disengagement. In addition, individuals' psychological adjustment was positively associated with social media use for socioemotional coping and mental disengagement while negatively related to problem-focused coping. Age was also found to be a moderator of the relationship between socioemotional coping and psychological adjustment.

Discussion

To relieve pandemic-related stress, individuals can actively utilize social media to implement various coping strategies. However, coping activities with social media may not always induce psychological benefits. By revealing the different levels of psychological adjustment among social media users with specific coping strategies, the current research enriched the literature on the effects of social media use on mental health. Findings from this study suggest the need for the prudent use of social media to cope with public health crises.

A mathematical model reveals the influence of NPIs and vaccination on SARS-CoV-2 Omicron Variant

An SVEIR SARS-CoV-2 Omicron variant model is proposed to provide some insights to coordinate non-pharmaceutical interventions (NPIs) and vaccination. Mathematically, we define the basic reproduction number R 0 and the effective reproduction number R e to measure the infection potential of Omicron variant and formulate an optimal disease control strategy. Our inversion results imply that the sick period of Omicron variant in the United States is longer than that of Delta variant in India. The decrease in the infectious period of the infection with infectiousness implies that the risk of hospitalization is reduced; but the increasing period of the infection with non-infectiousness signifies that Omicron variant lengthens the period of nucleic acid test being negative. Optimistically, Omicron's death rate is only a quarter of Delta's. Moreover, we forecast that the cumulative cases will exceed 100 million in the United States on February 28, 2022, and the daily confirmed cases will reach a peak on February 2, 2022. The results of parameters sensitivity analysis imply that NPIs are helpful to reduce the number of confirmed cases. In particular, NPIs are indispensable even if all the people were vaccinated when the efficiency of vaccine is relatively low. By simulating the relationships of the effective reproduction number R e , the vaccination rate and the efficacy of vaccine, we find that it is impossible to achieve the herd immunity without NPIs while the efficiency of vaccine is lower than 88.7 % . Therefore, the herd immunity area is defined by the evolution of relationships between the vaccination rate and the efficacy of vaccine. Finally, we present that the disease-induced mortality rate demonstrates the periodic oscillation and an almost periodic function is deduced to match the curve. A discussion completes the paper.

Harnessing intelligent technologies to curb COVID-19 pandemic: taxonomy and open challenges

The world has changed dramatically since the outbreak of COVID-19 pandemic. This has not only affected the humanity, but has also badly damaged the world’s socio-economic system. Currently, people are looking for a magical solution to overcome this pandemic. Similarly, scientists across the globe are working to find remedies to overcome this challenge. The role of technologies is not far behind in this situation, which attracts many sectors from government agencies to medical practitioners, and market analysts. This is quite true that in a few months of time, scientists, researchers, and industrialists have come up with some acceptable innovative solutions and harnessing existing technologies to stop the spread of COVID-19. Therefore, it is pertinent to highlight the role of intelligent technologies, which play a pivotal role in curbing this pandemic. In this paper, we devise a taxonomy related to the technologies being used in the current pandemic. We show that the most prominent technologies are artificial intelligence, machine learning, cloud computing, big data analytics, and blockchain. Moreover, we highlight some key open challenges, which technologists might face to control this outbreak. Finally, we conclude that to impede this pandemic, a collective effort is required from different professionals in support of using existing and new technologies. Finally, we conclude that to stop this pandemic, machine learning approaches with integration of cloud computing using high performance computing could provision the pandemic with minimum cost and time.

Community centered public safety resilience under public emergencies: A case study of COVID-19

During public emergencies, the level of public safety will be resilient and follow a process from decline to rise. Regarding the concept and influencing factors of public safety resilience, a three-level public safety resilience framework that includes personal, community, and government levels was proposed in this study. It provided the overall metrics that used the resistance and recovery ability to describe the dynamic characteristics of public safety resilience as well as the resilience assessment indexes on three levels. In the context of the Coronavirus Disease 2019 (COVID-19) pandemic, this study applied the proposed framework in a case study on public safety resilience at the Beihang community, Beijing, China through descriptive statistics, structural equation model, and principal component regression analysis of questionnaire data. The data analysis results showed that community resilience was the most important of the three levels of public safety resilience. In addition, community resilience could improve personal resilience, and government resilience had a positive effect on community and personal resilience. Compared with the resistance ability, the recovery ability was influenced more by the operation and improvement of the community. This study is conducive to understanding and improving public safety resilience on the personal, community, and government levels and can help relevant parties improve their ability to respond to the COVID-19 pandemic. Furthermore, the methods used in this study can be extended to other studies on public emergencies.

Numerical simulations on scale-free and random networks for the spread of COVID-19 in Pakistan☆

Epidemiology is the study of how and why an infectious disease occurs in a group of people. Several epidemiological models have been developed to get information on the spread of a disease in society. That information is used to plan strategies to prevent illness and manage patients. But, most of these models consider only random diffusion of the disease and hence ignore the number of interactions among people. To take into account the interactions among individuals, the network approach is becoming increasingly popular. It is novel to consider the dynamics of infectious disease using various networks rather than classical differential equation models. In this paper, we numerically simulate the Susceptible-Infected-Recoverd (SIR) model on Barabási-Albert network and Erdös-Rényi network to analyze the spread of COVID-19 in Pakistan so that we know the severity of the disease. We also show how a situation becomes alarming if hubs in a network get infected.

A Mathematical Evaluation of the Cost-Effectiveness of Self-Protection, Vaccination, and Disinfectant Spraying for COVID-19 Control

The world is on its path from the post-COVID period, but a fresh wave of the coronavirus infection engulfing most European countries makes the pandemic catastrophic. Mathematical models are of significant importance in unveiling strategies that could stem the spread of the disease. In this paper, a deterministic mathematical model of COVID-19 is studied to characterize a range of feasible control strategies to mitigate the disease. We carried out an analytical investigation of the model’s dynamic behaviour at its equilibria and observed that the disease-free equilibrium is globally asymptotically stable when the basic reproduction number, ${\mathcal{R}}_0$ is less than unity. The endemic equilibrium is also shown to be globally asymptotically stable when ${\mathcal{R}}_0>1$ . Further, we showed that the model exhibits forward bifurcation around ${\mathcal{R}}_0=1$ . Sensitivity analysis was carried out to determine the impact of various factors on the basic reproduction number ${\mathcal{R}}_0$ and consequently, the spread of the disease. An optimal control problem was formulated from the sensitivity analysis. Cost-effectiveness analysis is conducted to determine the most cost-effective strategy that can be adopted to control the spread of COVID-19. The investigation revealed that combining self-protection and environmental control is the most cost-effective control strategy among the enlisted strategies.

Physical, Functional, Psychological, and Social Effects of a Physical Activity Program in Adults and Older Adults During and/or After Hospitalization for COVID-19: A Systematic Review

Background

It is necessary to investigate the effects of physical activity (PA) on the recovery of adults and the elderly, considering PA positively affects pathologies that share similarities with COVID-19. We present the results of a systematic review whose objective was to analyze the physical, functional, psychological, and social effects of PA in adults and the elderly during and/or after hospitalization for COVID-19.

Methods

Searches were conducted between July and August 2021, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Randomized clinical trials (RCTs) and non-randomized interventional studies were included in the databases: PubMed, Web of Science, Scopus, EBSCOhost, Science Direct, Cochrane Library, Physiotherapy Evidence Database (PEDro), and electronic search engines. Study quality was assessed using the PEDro for RCTs and the methodological index scale for non-randomized studies. This systematic review included original articles investigating the physical, functional, psychological, and social effects of any PA program on adults and older adults.

Results

A total of 302 studies were found. After applying filters according to the eligibility criteria, five studies were finally included for analysis, three RCTs and two intervention studies without a control group. Although the studies measured different variables of the physical, functional, and psychological components, the results showed significant differences in the variables between the control and intervention groups in both the RCTs and the single-group studies. The variables assessed in the social aspect were less homogeneous.

Conclusion

In the available scientific evidence, respiratory muscle training was the most widely used intervention, which showed positive results in the physical, pulmonary, psychological, and social components. More research is required on the effects of PA on the population studied.

The presence of SARS-CoV-2 in multiple clinical specimens of a fatal case of COVID-19: a case report

BACKGROUND

The risk of developing severe and even fatal coronavirus disease 2019 (COVID-19) increases with various factors such as advanced age and chronic diseases, especially those treated with immunosuppressive drugs. Viral ribonucleic acid (RNA) and viral load detection in extra-pulmonary specimens have been proposed to indicate disease severity.

CASE PRESENTATION

Here we describe a fatal COVID-19 case of an 83-year-old Caucasian male patient with various underlying comorbidities, including cardiovascular and autoimmune disorders, as well as immunosuppression due to lymphoma treatment. Upon admission, the patient was radiologically diagnosed with severe COVID-19. The patient was febrile and presented with diarrhea, continued dyspnea, tachypnea, and low blood oxygen saturation, treated with high-concentration oxygen supplementation and antibacterial therapy. Overall the patient was treated for COVID-19 for 19 days. Blood tests were performed upon admission, on the fifth, 10th, 13th, and 19th day. In addition, nasopharyngeal swab, blood, urine, and fecal samples were collected from the patient on the 14th day for virological and immunological investigations. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detectable in all samples collected from this patient, including blood plasma and peripheral blood mononuclear cells (PBMC), with very high viral loads. However, neither virus-specific IgA, IgM, nor IgG antibodies were detectable.

CONCLUSIONS

The various cardiovascular, autoimmune, and oncological disorders, advanced age, and the high levels of inflammatory markers predisposed the patient to severe COVID-19 and determined the fatal outcome of the disease. We believe that the multiple specimen SARS-CoV-2 positivity and extremely high viral loads in nasopharyngeal swab and fecal samples to be the result of COVID-19 severity, the inability of viral clearance and weakened immune response due to advanced age, comorbidities, and the presence of non-Hodgkin's lymphoma and the immunosuppressive treatment for it, highlighting the risks of COVID-19 in such patients.

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.

Predictors of severe COVID-19 among healthcare workers in Sabah, Malaysia

BACKGROUND

Healthcare workers (HCWs) is the high-risk group for COVID-19 infection due to increased workplace exposure. However, evidence of the disease burden and factors associated with severe COVID-19 infection among HCWs is limited. Therefore, this article aims to describe the prevalence of severe COVID-19 disease among HCWs in Sabah, Malaysia, and to determine the factors associated with severe COVID-19 infection.

METHOD

A retrospective cross-sectional study was carried out by assessing the data of COVID-19-infected HCWs in Sabah, Malaysia, from 1st March 2021 until 30th September 2021. Logistic regression analysis was used in this study.

RESULTS

Three thousand and forty HCWs were diagnosed with COVID-19 from 1st March 2021 until 30th September 2021. Of the 3040 HCWs, 2948 (97.0%) HCWs were mild, whereas 92 (3.0%) were severe. The multivariate logistic regression model showed that severe COVID-19 among HCWs in Sabah was associated with those do not receive any COVID-19 vaccination (aOR 6.061, 95% CI 3.408 - 10.780), underlying co-morbidity (aOR 3.335, 95% CI 2.183 - 5.096), and female (aOR 1.833, 95% CI 1.090 - 3.081).

CONCLUSION

HCWs should strictly adhere to preventive measures, including vaccination, personal protective equipment, and early referral to a physician upon identifying severe COVID-19 infection. Early screening and aggressive co-morbidity treatment among HCWs are essential for public health practitioners to prevent severe COVID-19 disease. Regardless of co-morbidity status, HCWs should stay up to date with COVID-19 vaccination, including booster doses.

Spatio-temporal modeling of infectious diseases by integrating compartment and point process models

Infectious disease modeling plays an important role in understanding disease spreading dynamics and can be used for prevention and control. The well-known SIR (Susceptible, Infected, and Recovered) compartment model and spatial and spatio-temporal statistical models are common choices for studying problems of this kind. This paper proposes a spatio-temporal modeling framework to characterize infectious disease dynamics by integrating the SIR compartment and log-Gaussian Cox process (LGCP) models. The method's performance is assessed via simulation using a combination of real and synthetic data for a region in São Paulo, Brazil. We also apply our modeling approach to analyze COVID-19 dynamics in Cali, Colombia. The results show that our modified LGCP model, which takes advantage of information obtained from the previous SIR modeling step, leads to a better forecasting performance than equivalent models that do not do that. Finally, the proposed method also allows the incorporation of age-stratified contact information, which provides valuable decision-making insights.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02354-4.

Association between the risk of seizure and COVID-19 vaccinations: A self-controlled case-series study

OBJECTIVE

The risk of seizure following BNT162b2 and CoronaVac vaccinations has been sparsely investigated. This study aimed to evaluate this association.

METHOD

Patients who had their first seizure-related hospitalization between February 23, 2021 and January 31, 2022, were identified in Hong Kong. All seizure episodes happening on the day of vaccination (day 0) were excluded, since clinicians validated that most of the cases on day 0 were syncopal episodes. Within-individual comparison using a modified self-controlled case series analysis was applied to estimate the incidence rate ratio (IRR) with 95% confidence intervals (CIs) of seizure using conditional Poisson regression.

RESULTS

We identified 1656 individuals who had their first seizure-related hospitalization (BNT162b2: 426; CoronaVac: 263; unvaccinated: 967) within the observation period. The incidence of seizure was 1.04 (95% CI .80-1.33) and 1.11 (95% CI .80-1.50) per 100 000 doses of BNT162b2 and CoronaVac administered, respectively. Sixteen and 17 individuals, respectively, received a second dose after having a first seizure within 28 days after the first dose of BNT162b2 and CoronaVac vaccinations. None had recurrent seizures after the second dose. There was no increased risk during day 1-6 after the first (BNT162b2: IRR = 1.39, 95% CI = .75-2.58; CoronaVac: IRR = 1.19, 95% CI = .50-2.83) and second doses (BNT162b2: IRR = 1.36, 95% CI = .72-2.57; CoronaVac: IRR = .71, 95% CI = .22-2.30) of vaccinations. During 7-13, 14-20, and 21-27 days post-vaccination, no association was observed for either vaccine.

SIGNIFICANCE

The findings demonstrated no increased risk of seizure following BNT162b2 and CoronaVac vaccinations. Future studies will be warranted to evaluate the risk of seizure following COVID-19 vaccinations in different populations, with subsequent doses to ensure the generalizability.

Modelling the propagation of infectious disease via transportation networks

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention.

Alterations of the fecal microbiota in relation to acute COVID-19 infection and recovery

People with acute COVID-19 due to SARS-CoV-2 infection experience a range of symptoms, but major factors contributing to severe clinical outcomes remain to be understood. Emerging evidence suggests associations between the gut microbiome and the severity and progression of COVID-19. To better understand the host-microbiota interactions in acute COVID-19, we characterized the intestinal microbiome of patients with active SARS-CoV-2 infection in comparison to recovered patients and uninfected healthy controls. We performed 16S rRNA sequencing of stool samples collected between May 2020 and January 2021 from 20 COVID-19-positive patients, 20 COVID-19-recovered subjects and 20 healthy controls. COVID-19-positive patients had altered microbiome community characteristics compared to the recovered and control subjects, as assessed by both α- and β-diversity differences. In COVID-19-positive patients, we observed depletion of Bacteroidaceae, Ruminococcaceae, and Lachnospiraceae, as well as decreased relative abundances of the genera Faecalibacterium, Adlercreutzia, and the Eubacterium brachy group. The enrichment of Prevotellaceae with COVID-19 infection continued after viral clearance; antibiotic use induced further gut microbiota perturbations in COVID-19-positive patients. In conclusion, we present evidence that acute COVID-19 induces gut microbiota dysbiosis with depletion of particular populations of commensal bacteria, a phenomenon heightened by antibiotic exposure, but the general effects do not persist post-recovery.

Alterations in mental health and quality of life among healthcare workers in times of COVID-19: Four-stage cross-sectional study during first four pandemic waves in Poland

Background

The COVID-19 pandemic has had many unexpected effects that have affected the mental health of healthcare workers. In response to the crisis, healthcare workers appear to be the most vulnerable to the psychological effects of the pandemic. The purpose of the study was to assess the prevalence of depressive and anxiety symptoms and healthcare workers' quality of life during the different stages of the COVID-19 pandemic.

Materials and methods

The questionnaire was distributed in four stages corresponding to the different waves of the pandemic in Poland. The last stage of the study covered the period from November 1, 2021 to November 31, 2021 which coincided with the fourth wave of COVID-19 in Poland. The Beck Depression Inventory II (BDI-II), Generalized Anxiety Disorderd-7 (GAD-7), and Manchester Brief Assessment of Quality of Life (MANSA) scales were used.

Results

A total of 1,243 respondents participated. A gradual increase in moderate and severe anxiety was observed as the pandemic continued, comparing waves I and IV of the pandemic. No statistically significant differences were observed in comparing the mean values of the BDI-II, GAD-7, and MANSA scales across waves. A decrease in fear due to the disease and neighbor's quarantine was found. Women, single people and those with a psychiatric history are more likely to be affected by the destructive impact of the pandemic.

Conclusion

The COVID-19 pandemic is significantly affecting the mental health and quality of life of healthcare workers, but trend is not uniform. It is necessary to continue monitoring the mental health of medical workers, who are the most important link in the fight against the pandemic.

Effectiveness of Bacille Calmette-Guerin vaccination policies in reducing infection and mortality of COVID-19: a systematic review

BACKGROUND

COVID-19 vaccination has been advocated as the most effective way to curb the pandemic. But with its inequitable distribution and slow rollout, especially in low- to middle- income countries, it will still take a long time before herd immunity is achieved. Alternative measures must therefore be explored to bolster current COVID-19 vaccination efforts. In particular, the Bacille Calmette-Guerin vaccine has been studied extensively as to its proposed conferment of non-specific immunity against different infections, including COVID-19. The aim of this study, therefore, is to evaluate the current evidence on the effectiveness of national BCG vaccination policies in reducing infection and mortality of COVID-19.

METHODS

A systematic review was conducted between April to August 2021 following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-P) guidelines. Literature was retrieved from PubMed, Cochrane, HERDIN, Web of Science, EBSCO, and Western Pacific Region Index Medicus (WPRIM). Studies conducted from January 2020 to August 2021 that fell within Level 1A to 2C of the Oxford Center for Evidence-Based Medicine were included in the review. Quality assessment was performed using the appropriate Joanna Briggs Institute critical appraisal tool and a quality assessment checklist for ecological studies adapted from Betran et al. RESULTS: A total of 13 studies were included in this review. Nine studies reported significant association between BCG vaccination policies and COVID-19 outcomes, even when controlling for confounding variables. In addition, among other mandated vaccines, such as pneumococcal, influenza, diphtheria-tetanus-pertussis, and measles, only BCG vaccination showed significant association with decreased COVID-19 adverse outcomes. However, other factors also showed positive association with COVID-19 outcomes, particularly markers of high economic status of countries, higher median age, and greater population densities.

CONCLUSION

The lower incidence and mortality of COVID-19 in countries with mandated BCG vaccination may not solely be attributable to BCG vaccination policies, but there is still some evidence that demonstrates a possible protective effect. Clinical trials must be continued before recommendations of BCG vaccinations are to be used as an alternative or booster vaccine against COVID-19.

Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data

Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.

Nanomedicine approaches to reduce cytokine storms in severe infections

During a cytokine storm, dysregulated proinflammatory cytokines are produced in excess. Cytokine storms occur in multiple infectious diseases, including Coronavirus 2019 (COVID-19). Thus, eliminating cytokine storms to enhance patient outcomes is crucial. Given the numerous cytokines involved, individual therapies might have little effect. Traditional cytokines might be less effective than medicines that target malfunctioning macrophages. Nanomedicine-based therapeutics reduce cytokine production in animal models of proinflammatory illnesses. The unique physicochemical features and controlled nano-bio interactions of nanotechnology show promise in healthcare and could be used to treat several stages of this virus-induced sickness, including cytokine storm mortality. Macrophage-oriented nanomedicines can minimize cytokine storms and associated harmful effects, enhancing patient outcomes. Here, we also discuss engineering possibilities for enhancing macrophage efficacy with nanodrug carriers.

The pros and cons of entry restrictions: are entry restrictions really effective in preventing the spread of SARS-CoV-2?

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide, leading the World Health Organization to declare coronavirus disease 2019 (COVID-19) a pandemic. To curb the unchecked spread of SARS-CoV-2 infection, most countries have enforced travel restrictions. However, it is debatable whether such restrictions are effective in containing infections and preventing pandemics. Rather, they may negatively impact economies and diplomatic relationships. Each government should conduct an extensive and appropriate analysis of its national economy, diplomatic status, and COVID-19 preparedness to decide whether it is best to restrict entering travelers. Even if travelers from other countries are allowed entry, extensive contact tracing is required to prevent the spread of COVID-19. In addition, governments can implement “travel bubbles,” which allow the quarantine-free flow of people among countries with relatively low levels of community transmission. An accurate evaluation of the benefits and losses due to entry restrictions during the COVID-19 pandemic would be helpful in determining whether entry restrictions are an effective measure to reduce the spread of infection in future pandemics.

Neuro–Immune Interactions in Severe COVID-19 Infection

SARS-CoV-2 is a new coronavirus that has affected the world since 2019. Interstitial pneumonia is the most common clinical presentation, but additional symptoms have been reported, including neurological manifestations. Severe forms of infection, especially in elderly patients, present as an excessive inflammatory response called “cytokine storm”, which can lead to acute respiratory distress syndrome (ARDS), multiorgan failure and death. Little is known about the relationship between symptoms and clinical outcomes or the characteristics of virus–host interactions. The aim of this narrative review is to highlight possible links between neurological involvement and respiratory damage mediated by pathological inflammatory pathways in SARS-CoV-2 infection. We will focus on neuro–immune interactions and age-related immunity decline and discuss some pathological mechanisms that contribute to negative outcomes in COVID-19 patients. Furthermore, we will describe available therapeutic strategies and their effects on COVID-19 neurological symptoms.

Molecular characterization and sequecing analysis of SARS-CoV-2 genome in Minas Gerais, Brazil

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first identified in Wuhan, China, is the causative agent of the coronavirus disease 2019 (COVID-19). Since its first notification in São Paulo state (SP) on 26th February 2020, more than 22,300,000 cases and 619,000 deaths were reported in Brazil. In early pandemic, SARS-CoV-2 spread locally, however, over time, this virus was disseminated to other regions of the country. Herein, we performed genomic sequencing and phylogenetic analysis of SARS-CoV-2 using 20 clinical samples of COVID-19 confirmed cases from 9 cities of Minas Gerais state (MG), in order to evaluate the molecular properties of circulating viral strains in this locality from March to May 2020. Our analyses demonstrated the circulation of B.1 lineage isolates in the investigated locations and nucleotide substitutions were observed into the genomic regions related to important viral structures. Additionally, sequences generated in this study clustered with isolates from SP, suggesting a dissemination route between these two states. Alternatively, monophyletic groups of sequences from MG and other states or country were observed, indicating independent events of virus introduction. These results reinforce the need of genomic surveillance for understand the ongoing spread of emerging viral pathogens.

A mathematical model for simulating the spread of a disease through a country divided into geographical regions with different population densities

The SIR (susceptible-infectious-recovered) model is a well known method for predicting the number of people (or animals) in a population who become infected by and then recover from a disease. Modifications can include categories such people who have been exposed to the disease but are not yet infectious or those who die from the disease. However, the model has nearly always been applied to the entire population of a country or state but there is considerable observational evidence that diseases can spread at different rates in densely populated urban regions and sparsely populated rural areas. This work presents a new approach that applies a SIR type model to a country or state that has been divided into a number of geographical regions, and uses different infection rates in each region which depend on the population density in that region. Further, the model contains a simple matrix based method for simulating the movement of people between different regions. The model is applied to the spread of disease in the United Kingdom and the state of Rio Grande do Sul in Brazil.

Recent advances in the vaccine development for the prophylaxis of SARS Covid-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-caused Coronavirus Disease 2019 (COVID-19) is currently a global pandemic that has wreaked havoc on public health, lives, and the global economy. The present COVID-19 outbreak has put pressure on the scientific community to develop medications and vaccinations to combat COVID-19. However, according to highly optimistic forecasts, we could not have a COVID-19 vaccine until September 2020. This is due to the fact that a successful COVID-19 vaccine will necessitate a careful validation of effectiveness and adverse reactivity given that the target vaccine population includes high-risk people over 60, particularly those with severe co-morbid conditions, frontline healthcare professionals, and those involved in essential industrial sectors. For passive immunization, which is being considered for Covid-19, there are several platforms for vaccine development, each with its own advantages and disadvantages. The COVID-19 pandemic, which is arguably the deadliest in the last 100 years after the Spanish flu, necessitates a swift assessment of the various approaches for their ability to incite protective immunity and safety to prevent unintended immune potentiation, which is crucial to the pathogenesis of this virus. Considering the pandemic's high fatality rate and rapid spread, an efficient vaccination is critical for its management. As a result, academia, industry, and government are collaborating in unprecedented ways to create and test a wide range of vaccinations. In this review, we summarize the Covid-19 vaccine development initiatives, recent trends, difficulties, comparison between traditional vaccines development and Covid-19 vaccines development also listed the approved/authorized, phase-3 and pre-clinical trials Covid-19 vaccines in different countries.

From individual-based epidemic models to McKendrick-von Foerster PDEs: a guide to modeling and inferring COVID-19 dynamics

We present a unifying, tractable approach for studying the spread of viruses causing complex diseases requiring to be modeled using a large number of types (e.g., infective stage, clinical state, risk factor class). We show that recording each infected individual’s infection age, i.e., the time elapsed since infection, has three benefits. First, regardless of the number of types, the age distribution of the population can be described by means of a first-order, one-dimensional partial differential equation (PDE) known as the McKendrick-von Foerster equation. The frequency of type i is simply obtained by integrating the probability of being in state i at a given age against the age distribution. This representation induces a simple methodology based on the additional assumption of Poisson sampling to infer and forecast the epidemic. We illustrate this technique using French data from the COVID-19 epidemic. Second, our approach generalizes and simplifies standard compartmental models using high-dimensional systems of ordinary differential equations (ODEs) to account for disease complexity. We show that such models can always be rewritten in our framework, thus, providing a low-dimensional yet equivalent representation of these complex models. Third, beyond the simplicity of the approach, we show that our population model naturally appears as a universal scaling limit of a large class of fully stochastic individual-based epidemic models, where the initial condition of the PDE emerges as the limiting age structure of an exponentially growing population starting from a single individual.

SARS-CoV-2 in Brazil and Psychosocial Repercussions on Men's Health: Health Literacy Is Important

This study aimed at analyzing the psychosocial repercussions of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic on the health of men living in Brazil. For this, we carried out a socio-historical and qualitative study, with the participation of 200 men who answered an online questionnaire. The data collected were processed in the NVIVO12® software, structured by the Collective Subject Discourse method, and analyzed from the epidemic disease theoretical framework proposed by Charles Rosenberg. Our results showed that the SARS-CoV-2 pandemic brought about repercussions of different dimensions that compromised the health of men living in Brazil. The repercussions evidenced were behavioral changes and emergence of new habits due to the pandemic; uncomfortable family situations; impaired affective and sexual relationships; harms in marital relationships; and insecurity and psychological distress. It is important to implement strategies that maximize men's health literacy, promoting better communication in terms of health, and search for help and suitable information about health/mental health.

Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.

Kombe I, Ojal J, Parisi A, Keeling MJ, Nokes DJ, Manson-Sawko R, House T. A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.

Risk factors associated with COVID-19 infection and mortality in nursing homes

OBJECTIVE

The aim of this paper was to analyse the association of demographic, clinical and pharmacological risk factors with the presence of SARS-COV-2 virus infection, as well as to know the variables related to mortality from COVID-19 in nursing home (NH) residents.

DESIGN

Retrospective case-control study. The study variables of those residents who acquired the infection (case) were compared with those of the residents who did not acquire it (control). A subgroup analysis was carried out to study those variables related to mortality. SITE: Nursing homes in the region of Guipúzcoa (Spain).

PARTICIPANTS AND INTERVENTIONS

4 NHs with outbreaks of SARS-CoV-2 between March and December 2020 participated in the study. The infectivity and, secondary, mortality was studied, as well as demographic, clinical and pharmacological variables associated with them. Data were collected from the computerised clinical records.

MAIN MEASUREMENTS

Infection and mortality rate. Risk factors associated with infection and mortality.

RESULTS

436 residents were studied (median age 87 years (IQR 11)), 173 acquired SARS-CoV-2 (39.7%). People with dementia and Global Deterioration Scale ≥6 were less likely to be infected by SARS-CoV-2 virus [OR=0.65 (95% CI 0.43-0.97; p<.05)]. Overall case fatality rate was 10.3% (a mortality of 26% among those who acquired the infection). COVID-19 mortality was significantly associated with a Global Deterioration Scale ≥6 (OR=4.9 (95% CI 1.5-16.1)), COPD diagnosis (OR=7.8 (95% CI 1.9-31.3)) and antipsychotic use (OR=3.1 (95% CI 1.0-9.0)).

CONCLUSIONS

Advanced dementia has been associated with less risk of SARS-CoV-2 infection but higher risk of COVID-19 mortality. COPD and chronic use of antipsychotics have also been associated with mortality. These results highlight the importance of determining the stage of diseases such as dementia as well as maintaining some caution in the use of some drugs such as antipsychotics.

A meta-analysis result: Uneven influences of season, geo-spatial scale and latitude on relationship between meteorological factors and the COVID-19 transmission

Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.

Atmospheric particulate matter effects on SARS-CoV-2 infection and spreading dynamics: A spatio-temporal point process model

Particulate matter (PM) may play a role in differential distribution and transmission rates of SARS-CoV-2. For public health surveillance, identification of factors affecting the transmission dynamics concerning the endemic (persistent sporadic) and epidemic (rapidly clustered) component of infection can help to implement intervention strategies to reduce the disease burden. The aim of this study is to assess the effect of long-term residential exposure to outdoor PM ≤ 10 μm (PM₁₀) concentrations on SARS-CoV-2 incidence and on its spreading dynamics in Marche region (Central Italy) during the first wave of the COVID-19 pandemic (February to May 2020), using the endemic-epidemic spatio-temporal regression model for individual-level data. Environmental and climatic factors were estimated at 10 km² grid cells. 10-years average exposure to PM₁₀ was associated with an increased risk of new endemic (Rate Ratio for 10 μg/m³ increase 1.14, 95%CI 1.04-1.24) and epidemic (Rate Ratio 1.15, 95%CI 1.08-1.22) infection. Male gender, older age, living in Nursing Homes and Long-Term Care Facilities residence and socio-economic deprivation index increased Rate Ratio (RR) in epidemic component. Lockdown increased the risk of becoming positive to SARS-CoV-2 as concerning endemic component while it reduced virus spreading in epidemic one. Increased temperature was associated with a reduction of endemic and epidemic infection. Results showed an increment of RR for exposure to increased levels of PM₁₀ both in endemic and epidemic components. Targeted interventions are necessary to improve air quality in most polluted areas, where deprived populations are more likely to live, to minimize the burden of endemic and epidemic COVID-19 disease and to reduce unequal distribution of health risk.

Bibliometric analysis for economy in COVID-19 pandemic

Since the outbreak of COVID-19, various fields have been damaged to varying degrees, especially in the economic field. According to a comprehensive study of bibliometrics and content analysis, this paper aims at summarizing studies related to the development of the economic field during the COVID-19 epidemic. We search in Web of Science Core Collection using the subjects such as “economics”, “economy”, “economic”, “financial”, and then 2274 related documents are collected, which are published from 2020 to 2022. First, this paper uses the mixed qualitative and quantitative analysis methods to analyze the publication status of the countries, institutions, and authors, respectively, and conducts the document co-citation analysis by CiteSpace software. The results showed that the most popular journal is Sustainability, and the most productive research institutions, countries, and authors are primarily located in North American and European countries. Then, it makes an in-depth study of the cooperative network. With the support of Gephi software, this paper employs the social network analysis method to analyze the situation of the country/region cooperation and institutional cooperation. Finally, the content analysis of the related studies is presented to further explore the current challenges. On this basis, this paper analyzes the economic development in the post-epidemic era and draws some conclusions, which provide some references for scholars interested in this field.

High-dose intravenous vitamin C decreases rates of mechanical ventilation and cardiac arrest in severe COVID-19

Intravenous vitamin C (IV-VitC) has been suggested as a treatment for severe sepsis and acute respiratory distress syndrome; however, there are limited studies evaluating its use in severe COVID-19. Efficacy and safety of high-dose IV-VitC (HDIVC) in patients with severe COVID-19 were evaluated. This observational cohort was conducted at a single-center, 530 bed, community teaching hospital and took place from March 2020 through July 2020. Inverse probability treatment weighting (IPTW) was utilized to compare outcomes in patients with severe COVID-19 treated with and without HDIVC. Patients were enrolled if they were older than 18 years of age and were hospitalized secondary to severe COVID-19 infection, indicated by an oxygenation index < 300. Primary study outcomes included mortality, mechanical ventilation, intensive care unit (ICU) admission, and cardiac arrest. From a total of 100 patients enrolled, 25 patients were in the HDIVC group and 75 patients in the control group. The average time to death was significantly longer for HDIVC patients (P = 0.0139), with an average of 22.9 days versus 13.7 days for control patients. Patients who received HDIVC also had significantly lower rates of mechanical ventilation (52.93% vs. 73.14%; OR_IPTW = 0.27; P = 0.0499) and cardiac arrest (2.46% vs. 9.06%; OR_IPTW = 0.23; P = 0.0439). HDIVC may be an effective treatment in decreasing the rates of mechanical ventilation and cardiac arrest in hospitalized patients with severe COVID-19. A longer hospital stay and prolonged time to death may suggest that HDIVC may protect against clinical deterioration in severe COVID-19.

Comparing COVID-19 fatality across countries: a synthetic demographic indicator

Background

The case fatality rate (CFR) is one of the most important measures for monitoring disease progression and evaluating appropriate policy health measures over the course of the COVID-19 pandemic. To remove biases arising from the age structure of COVID-19 cases in international comparisons of the CFR, existing studies have relied mainly on direct standardisation.

Objective

We propose and validate a synthetic indicator of COVID-19 fatality (SCFR) that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases without relying on the arbitrary choice of a standard population.

Results

Contrary to what comparisons of the crude CFR suggest, differences in COVID-19 fatality across countries according to the proposed SCFR are not very stark. Importantly, once we adjust for the age structure of COVID-19 cases, the higher case fatality among men emerges as the main driver of international differences in COVID-19 CFR.

Conclusions

The SCFR is a simple indicator that is useful for monitoring the fatality of SARS-CoV-2 mutations and the efficacy of health policy measures for COVID-19, including vaccination.

Contributions

(1) A simple synthetic indicator of COVID-19 fatality that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases; (2) Evidence that sex differences in COVID-19 fatality drive international differences in the overall CFR.

Genomic epidemiology and emergence of SARS-CoV-2 variants of concern in the United Arab Emirates

Since the declaration of SARS-CoV-2 outbreak as a pandemic, the United Arab Emirates (UAE) public health authorities have adopted strict measures to reduce transmission as early as March 2020. As a result of these measures, flight suspension, nationwide RT-PCR and surveillance of viral sequences were extensively implemented. This study aims to characterize the epidemiology, transmission pattern, and emergence of variants of concerns (VOCs) and variants of interests (VOIs) of SARS-CoV-2 in the UAE, followed by the investigation of mutations associated with hospitalized cases. A total of 1274 samples were collected and sequenced from all seven emirates between the period of 25 April 2020 to 15 February 2021. Phylogenetic analysis demonstrated multiple introductions of SARS-CoV-2 into the UAE in the early pandemic, followed by a local spread of root clades (A, B, B.1 and B.1.1). As the international flight resumed, the frequencies of VOCs surged indicating the January peak of positive cases. We observed that the hospitalized cases were significantly associated with the presence of B.1.1.7 (p < 0.001), B.1.351 (p < 0.001) and A.23.1 (p = 0.009). Deceased cases are more likely to occur in the presence of B.1.351 (p < 0.001) and A.23.1 (p = 0.022). Logistic and ridge regression showed that 51 mutations are significantly associated with hospitalized cases with the highest proportion originated from S and ORF1a genes (31% and 29% respectively). Our study provides an epidemiological insight of the emergence of VOCs and VOIs following the borders reopening and worldwide travels. It provides reassurance that hospitalization is markedly more associated with the presence of VOCs. This study can contribute to understand the global transmission of SARS-CoV-2 variants.

Mathematical modeling for COVID-19 with focus on intervention strategies and cost-effectiveness analysis

The realistic assessments of public health intervention strategies are of great significance to effectively combat the COVID-19 epidemic and the formation of intervention policy. In this paper, an extended COVID-19 epidemic model is devised to assess the severity of the pandemic and explore effective control strategies. The model is characterized by ordinary differential equations with seven-state variables, and it incorporates some parameters associated with the interventions (i.e., media publicity, home isolation, vaccination and face-mask wearing) to investigate the impacts of these interventions on the spread of the COVID-19 epidemic. Some dynamic behaviors of the model, such as forward and backward bifurcation, are analyzed. Specifically, we calibrate the model parameters using actual COVID-19 infected data in Brazil by Markov Chain Monte Carlo algorithm such that we can study the effects of interventions on a practical case. Through a comprehensive exploration of model design and analysis, model calibration, sensitivity analysis, implementation of optimal control problems and cost-effectiveness analysis, the rationality of our model is verified, and the effective strategies to combat the epidemic in Brazil are revealed. The results show that the asymptomatic infected individuals are the main drivers of COVID-19 transmission, and rapid detection of asymptomatic infections is critical to combat the COVID-19 epidemic in Brazil. Interestingly, the effect of the vaccination rate associated with pharmaceutical intervention on the basic reproduction number is much lower than that of non-pharmaceutical interventions (NPIs). Our study also highlights the importance of media publicity. To reduce the infected individuals, the multi-pronged NPIs have considerable positive effects on controlling the outbreak of COVID-19. The infections are significantly decreased by the early implementation of media publicity complemented with home isolation and face-mask wearing strategy. When the cost of implementation is taken into account, the early implementation of media publicity complemented with a face-mask wearing strategy can significantly mitigate the second wave of the epidemic in Brazil. These results provide some management implications for controlling COVID-19.

Fatality assessment and variant risk monitoring for COVID-19 using three new hospital occupancy related metrics

BACKGROUND

Though case fatality rate (CFR) is widely used to reflect COVID-19 fatality risk, its use is limited by large temporal and spatial variation. Hospital mortality rate (HMR) is also used to assess the severity of COVID-19, but HMR data is not directly available globally. Alternative metrics are needed for COVID-19 severity and fatality assessment.

METHODS

We introduce new metrics for COVID-19 fatality risk measurements/monitoring and a new mathematical model to estimate average hospital length of stay for deaths (L_dead) and discharges (L_dis). Multiple data sources were used for our analyses.

FINDINGS

We propose three, new metrics: hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR), and ratio of hospital occupancy to cases (HOCR), for dynamic assessment of COVID-19 fatality risk. Estimated L_dead and L_dis for 501,079 COVID-19 hospitalizations in 34 US states between 7 August 2020 and 1 March 2021 were 18·2(95%CI:17·9-18·5) and 14·0(95%CI:13·9-14·0) days, respectively. We found the dramatic changes in COVID-19 CFR observed in 27 countries during early stages of the pandemic were mostly caused by undiagnosed cases. Compared to the first week of November 2021, the week mean HOCRs (mimics hospitalization-to-case ratio) for Omicron variant (58·6% of US new cases as of 25 December 2021) decreased 65·16% in the US as of 16 January 2022.

INTERPRETATION

The new and reliable measurements described here could be useful for COVID-19 fatality risk and variant-associated risk monitoring.

FUNDING

No specific funding was associated with the present study.

The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

Understanding the circumstances that lead to pandemics is important for their prevention. We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted "A" and "B." Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October to 8 December), and the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans before November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events.

A novel discrete-time COVID-19 epidemic model including the compartment of vaccinated individuals

Referring tothe study of epidemic mathematical models, this manuscript presents a noveldiscrete-time COVID-19 model that includes the number of vaccinated individuals as an additional state variable in the system equations. The paper shows that the proposed compartment model, described by difference equations, has two fixed points, i.e., a disease-free fixed point and an epidemic fixed point. By considering both the forward difference system and the backward difference system, some stability analyses of the disease-free fixed point are carried out.In particular, for the backward difference system a novel theorem is proved, which gives a condition for the disappearance of the pandemic when an inequality involving some epidemic parameters is satisfied. Finally, simulation results of the conceived discrete model are carried out, along with comparisons regarding the performances of both the forward difference system and the backward difference system.

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.

The unmitigated profile of COVID-19 infectiousness

Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOCs), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics.

The Role of Inflammasomes in COVID-19: Potential Therapeutic Targets

The coronavirus 2019 disease (COVID-19) pandemic has caused massive morbidity and mortality worldwide. In severe cases, it is mainly associated with acute pneumonia, cytokine storm, and multi-organ dysfunction. Inflammasomes play a primary role in various pathological processes such as infection, injury, and cancer. However, their role in COVID-19-related complications has not been explored. In addition, the role of underlying medical conditions on COVID-19 disease severity remains unclear. Therefore, this review expounds on the mechanisms of inflammasomes following COVID-19 infection and provides recent evidence on the potential double-edged sword effect of inflammasomes during COVID-19 pathogenesis. The assembly and activation of inflammasomes are critical for inducing effective antiviral immune responses and disease resolution. However, uncontrolled activation of inflammasomes causes excessive production of proinflammatory cytokines (cytokine storm), increased risk of acute respiratory distress syndrome, and death. Therefore, discoveries in the role of the inflammasome in mediating organ injury are key to identifying therapeutic targets and treatment modifications to prevent or reduce COVID-19-related complications.

A community-level study on COVID-19 transmission and policy interventions in Wuhan, China

The specific factors and response strategies that affect COVID-19 transmission in local communities remain under-explored in the current literature due to a lack of data. Based on primary COVID-19 data collected at the community level in Wuhan, China, our study contributes a community-level investigation on COVID-19 transmission and response strategies by addressing two research questions: 1) What community factors are associated with viral transmission? and 2) What are the key mechanisms behind policy interventions towards controlling viral transmission within local communities? We conducted two sets of analyses to address these two questions-quantitative analyses of the relationship between community factors and viral transmission and qualitative analyses of policy interventions on community transmission. Our findings show that the viral spread in local communities is irrelevant to the built environment of a community and its socioeconomic position but is related to its demographic composition. Specifically, groups under the age of 18 play an important role in viral transmission. Moreover, a series of community shutdown management initiatives (e.g., group buying, delivering supplies, and self-reporting of health conditions) play an important role in curbing viral transmission at the local level that can be applied to other geographic contexts.

Time from symptom onset to severe COVID-19 and risk factors among patients in Southern Ethiopia: a survival analysis

Objective

This study aimed to assess the time to severe coronavirus disease 2019 (COVID-19) and risk factors among confirmed COVID-19 cases in Southern Ethiopia.

Method

This two-center retrospective cohort study involved patients with confirmed COVID-19 from 1 October 2020 to 30 September 2021. Kaplan–Meier graphs and log-rank tests were used to determine the pattern of COVID-19 severity among categories of variables. Bivariable and multivariable Cox proportional regression models were used to identify the risk factors of severe COVID-19.

Results

Four hundred thirteen patients with COVID-19 with a mean age of 41.9 ± 15.3 years were involved in the study. There were 194 severe cases (46.9.1%), including 77 (39.6%) deaths. The median time from symptom onset to severe COVID-19 was 8 days (interquartile range: 7–12 days). The risk factors for severe COVID-19 were age >65 (adjusted hazard ratio [AHR] = 2.65, 95% confidence interval [95%CI]: 1.02, 3.72), cough (AHR = 1.59, 95%CI: 1.39, 2.84), chest pain (AHR = 1.47, 95%CI: 1.34, 2.66), headache (AHR = 2.04, 95%CI: 1.43, 2.88), comorbidity (AHR = 1.3, 95%CI: 1.01, 2.04), asthma (AHR = 1.6. 95%CI: 1.04, 2.24), and symptom onset to admission more than 5 days (AHR = 0.48, 95%CI: 0.34, 0.68).

Conclusion

Patients with symptoms and comorbidities should be closely monitored.

Nowcasting and forecasting COVID-19 waves: the recursive and stochastic nature of transmission

We propose a parsimonious, yet effective, susceptible-exposed-infected-removed-type model that incorporates the time change in the transmission and death rates. The model is calibrated by Tikhonov-type regularization from official reports from New York City (NYC), Chicago, the State of São Paulo, in Brazil and British Columbia, in Canada. To forecast, we propose different ways to extend the transmission parameter, considering its estimated values. The forecast accuracy is then evaluated using real data from the above referred places. All the techniques accurately provided forecast scenarios for periods 15 days long. One of the models effectively predicted the magnitude of the four waves of infections in NYC, including the one caused by the Omicron variant for periods of 45 days using out-of-sample data.

Safety of an inactivated, whole-virion COVID-19 vaccine (CoronaVac) in people aged 60 years or older in Hong Kong: a modified self-controlled case series

Background

Because evidence on the safety of COVID-19 vaccines in older adults is scarce, we aimed to evaluate the incidence and risk of adverse events after CoronaVac (Sinovac Biotech) vaccination in adults aged 60 years or older.

Methods

In this modified self-controlled case series, we enrolled adults aged 60 years or older who had received at least one dose of CoronaVac in Hong Kong between Feb 23, 2021, and Jan 31, 2022. We extracted population-based, electronic health record data from the clinical management system of the Hospital Authority on adverse events of special interest (from Jan 1, 2005, to Feb 23, 2022) and patients' demographic information (from Jan 1, 2018, to Jan 31, 2022), previous diagnoses (from Jan 1, 2018, to Jan 31, 2022), medication history (from Jan 1, 2018, to Jan 31, 2022), and laboratory tests, including those for SARS-CoV-2 infection (from Jan 1, 2018, to Jan 31, 2022). Details of vaccination status were provided by the Department of Health of the Hong Kong Government and were linked to data from the Hospital Authority with identity card numbers or passport numbers. Our outcomes were the overall incidence of any adverse event of special interest and the incidence rates of 30 adverse events of special interest, as suggested by the WHO Global Advisory Committee on Vaccine Safety, in the inpatient setting within 21 days (2 days for anaphylaxis) of either the first, second, or third CoronaVac dose compared with a baseline period. Individuals who had a history of a particular event between Jan 1, 2005, and Feb 23, 2021, were excluded from the corresponding analysis. We evaluated the risk of an adverse event of special interest using conditional Poisson regression, adjusting for seasonal effects.

Findings

Of 1 253 497 individuals who received at least one dose of CoronaVac during the study period, 622 317 (49·6%) were aged at least 60 years and were included in the analysis. Our analysis sample received 1 229 423 doses of CoronaVac and had a mean age of 70·40 years (SD 8·10). 293 086 (47·1%) of 622 317 participants were men and 329 231 (52·9%) were women. The incidence of individual adverse events of interest ranged from 0·00 per 100 000 people to 57·49 per 100 000 people (thromboembolism). The first and third doses of CoronaVac were not associated with a significant excess risk of an adverse event of special interest within 21 days (or 2 days for anaphylaxis) of vaccination. After the second dose, the only significantly increased risk was for anaphylaxis (adjusted incidence rate ratio 2·61, 95% CI 1·08–6·31; risk difference per 100 000 people 0·61, 95% CI 0·03–1·81).

Interpretation

Because older age is associated with poor outcomes after SARS-CoV-2 infection, the benefits of CoronaVac vaccination in older adults outweigh the risks in regions where COVID-19 is prevalent. Ongoing monitoring of vaccine safety is warranted.

Funding

The Food and Health Bureau of the Government, Hong Kong Special Administrative Region, China and AIR@InnoHK, administered by the Innovation and Technology Commission.

Translation

For the Chinese translation of the abstract see Supplementary Materials section.

Single cell gene expression profiling of nasal ciliated cells reveals distinctive biological processes related to epigenetic mechanisms in patients with severe COVID-19

Objective

To explore the molecular processes associated with cellular regulatory programs in patients with COVID-19, including gene activation or repression mediated by epigenetic mechanisms. We hypothesized that a comprehensive gene expression profiling of nasopharyngeal epithelial cells might expand our understanding of the pathogenic mechanisms of severe COVID-19.

Methods

We used single-cell RNA sequencing (scRNAseq) profiling of ciliated cells (n = 12,725) from healthy controls (SARS-CoV-2 negative n = 13) and patients with mild/moderate (n = 13) and severe (n = 14) COVID-19. ScRNAseq data at the patient level were used to perform gene set and pathway enrichment analyses. We prioritized candidate miRNA-target interactions and epigenetic mechanisms.

Results

We found that mild/moderate COVID-19 compared to healthy controls had upregulation of gene expression signatures associated with mitochondrial function, misfolded proteins, and membrane permeability.

In addition, we found that compared to mild/moderate disease, severe COVID-19 had downregulation of epigenetic mechanisms, including DNA and histone H3K4 methylation and chromatin remodelling regulation. Furthermore, we found 11-ranked miRNAs that may explain miRNA-dependent regulation of histone methylation, some of which share seed sequences with SARS-CoV-2 miRNAs.

Conclusion

Our results may provide novel insights into the epigenetic mechanisms mediating the clinical course of SARS-CoV-2 infection.

Highly Prevalent SARS-CoV-2 Antigenemia in COVID-19 Patients

Background

Many issues, such as severity assessment and antibody responses, remain to be answered eagerly for evaluation and understanding of COVID-19. Immune lesion is one of key pathogenesis of the disease. It would be helpful to understand the disease if an investigation on antigenemia and association was conducted in the patients with SARS-CoV-2 infection.

Methods

A total of 156 patients admitted to the First People's Hospital of Hefei or Anhui Provincial Hospital on January to February 2020 were involved in this study. SARS-CoV-2 nucleocapsid (NP) antigen, specific IgM/IgG antibodies, and RNA were detected in sequential sera from three COVID-19 patients, and additional 153 COVID-19 patients by means of NP-antigen capture enzyme-linked immunosorbent assay, colloidal gold quick diagnosis, and real-time RT-PCR, respectively. The clinical types of COVID-19 patients were classified into asymptomatic, mild, moderate, severe, and critical, following on the Chinese guideline of COVID-19 diagnosis and treatment. The demographic and clinical data of patients were obtained for comparable analysis.

Results

NP antigen was detected in 5 of 20 sequential sera collected from three COVID-19 patients with typically clinical symptoms, and 60.13% (92/153) expanded samples collected within 17 days after illness onset. No SARS-CoV-2 RNA segment was detected in these sera. The NP positive proportion reached a peak (84.85%, 28/33) on 6 to 8 days after illness onset. Both NP concentration and positive proportion were increased with the increase of clinical severity of COVID-19. Compared to NP negative patients, NP positive patients had older age [years, medians (interquartile ranges (IQR)), 49 (6) vs. 31 (11)], lower positive proportion of NP specific IgM [27.17% (25/92) vs. 59.02% (36/61)], and IgG [21.74% (20/92) vs. 59.02% (36/61)] antibodies, and longer duration [days, medians (IQR), 24 (10) vs. 21 (13)] from illness to recovery.

Conclusions

SARS-CoV-2 NP antigenemia occurred in COVID-19, and presented highly prevalent at early stage of the disease. The antigenemia was related to clinical severity of the disease, and may be responsible for the delay of detectable SARS-Cov-2 IgM.

Age-Dependent Risks of COVID-19 Putatively Caused by Variant Alpha in Japan

Background

Osaka, the third largest prefecture in Japan, experienced a rapid replacement of preexisting strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by variant alpha during March-April 2021. Assessing the burden of variant alpha on health centers and medical institutions is vital to anticipating the surge of patients. The present study aimed to estimate the age-dependent risks of coronavirus disease (COVID-19) putatively caused by variant alpha in Japan, focusing on epidemiological dynamics in Osaka.

Methods

Descriptive analyses were conducted using data on confirmed, severe and fatal cases of COVID-19 from 16 November 2020 to 22 May 2021. All cases were divided into 6-9 age groups to compare the risks of confirmed diagnosis, severe illness and death from COVID-19 with variant alpha to those caused by preexisting strains.

Results

Individuals with COVID-19 aged under 30 years were more likely to be infected with variant alpha than those in their 40s. The incidence of severe illness and death among all age groups with COVID-19 due to variant alpha was higher than that due to preexisting strains. Patients older than 40 years experienced an increased risk of severe illness and death if infected with variant alpha. However, the proportion of severe cases was lower in the group aged 80 years and older infected with variant alpha than in those infected with preexisting strains.

Conclusion

Our analysis suggests that the incidence of infection among young people aged below 30 years old increased relative to ordinary strains. Risks of severe illness and death in patients with variant alpha COVID-19 was higher than in those with preexisting strains in Osaka, Japan. However, a decrease in the risk of severe illness was observed in people aged ≥80 years, which is probably because medical facilities in Osaka were overwhelmed in April and May 2021. Continuous monitoring of COVID-19 cases with new variants is vital to secure sufficient medical resources for all patients who require medical care.

Development of COVID-19 severity assessment score in adults presenting with COVID-19 to the emergency department

BACKGROUND

Critically-ill Covid-19 patients require extensive resources which can overburden a healthcare system already under strain due to a pandemic. A good disease severity prediction score can help allocate resources to where they are needed most.

OBJECTIVES

We developed a Covid-19 Severity Assessment Score (CoSAS) to predict those patients likely to suffer from mortalities within 28 days of hospital admission. We also compared this score to Quick Sequential Organ Failure Assessment (qSOFA) in adults.

METHODS

CoSAS includes the following 10 components: Age, gender, Clinical Frailty Score, number of comorbidities, Ferritin level, D-dimer level, neutrophil/lymphocyte ratio, C-reactive Protein levels, systolic blood pressure and oxygen saturation. Our study was a single center study with data collected via chart review and phone calls. 309 patients were included in the study.

RESULTS

CoSAS proved to be a good score to predict Covid-19 mortality with an Area under the Curve (AUC) of 0.78. It also proved better than qSOFA (AUC of 0.70). More studies are needed to externally validate CoSAS.

CONCLUSION

CoSAS is an accurate score to predict Covid-19 mortality in the Pakistani population.