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

Numerical treatment on the new fractional-order SIDARTHE COVID-19 pandemic differential model via neural networks

In this study, modeling the COVID-19 pandemic via a novel fractional-order SIDARTHE (FO-SIDARTHE) differential system is presented. The purpose of this research seemed to be to show the consequences and relevance of the fractional-order (FO) COVID-19 SIDARTHE differential system, as well as FO required conditions underlying four control measures, called SI,  SD,  SA, and SR. The FO-SIDARTHE system incorporates eight phases of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatening (T), healed (H), and extinct (E). Our objective of all these investigations is to use fractional derivatives to increase the accuracy of the SIDARTHE system. A FO-SIDARTHE system has yet to be disclosed, nor has it yet been treated using the strength of stochastic solvers. Stochastic solvers based on the Levenberg-Marquardt backpropagation methodology (L-MB) and neural networks (NNs), specifically L-MBNNs, are being used to analyze a FO-SIDARTHE problem. Three cases having varied values under the same fractional order are being presented to resolve the FO-SIDARTHE system. The statistics employed to provide numerical solutions toward the FO-SIDARTHE system are classified as obeys: 72% toward training, 18% in testing, and 10% for authorization. To establish the accuracy of such L-MBNNs utilizing Adams-Bashforth-Moulton, the numerical findings were compared with the reference solutions.

What drives innovation? Lessons from COVID-19 R&D

This paper studies the global R&D effort to fight the deadliest diseases. We find: (1) the elasticity of R&D effort with respect to market size is about 1/2 in the cross-section of diseases; (2) given this elasticity, the R&D response to COVID-19 has been 4 to 26 times greater than that implied by its market size; (3) the aggregate short-term elasticity of science and innovation can be very large, as demonstrated by the aggregate flow of clinical trials increasing by 38% in 2020, with limited crowding out of trials for non-COVID diseases; and (4) public institutions and government-led incentives were a key driver of the COVID-19 R&D effort-with public research institutions accounting for 70 percent of all COVID-19 clinical trials globally. Overall, our work suggests that leveraging early-stage incentives, non-monetary incentives, and public institutions may be important for scaling up global innovation.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power.

Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death.

Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients.

Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

Seroprevalence of SARS-Cov-2 IgG antibodies in patients at a single center in Saudi Arabia

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has had a massive impact on public health as well as the economy. Understanding the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among undiagnosed individuals is important for developing an informed pandemic response.

OBJECTIVE

Investigate the prevalence of undiagnosed COVID-19 disease.

DESIGN

Cross-sectional.

SETTING

Tertiary care center in Madinah, Saudi Arabia.

SUBJECTS AND METHODS

All participants were on follow-up visits to various clinics and had not been previously diagnosed with COVID-19. Enzyme-linked immunosorbent assay was used to specifically assess the anti-spike IgG antibody seropositivity in serum samples. We associated the seropositivity rates of the participants with age, body mass index (BMI), nationality, blood groups, and sex with uni- and multivariate analyses.

MAIN OUTCOME MEASURES

Seropositivity for IgG anti-spike antibodies against SARS-CoV-2.

SAMPLE SIZE AND CHARACTERISTICS

527 subjects, with a median (interquartile percentiles) age of the 527 subjects was 34 (24-41).

RESULTS

Of the 527 samples, about one-fourth (n=124, 23.5%) were positive for anti-spike IgG antibody against SARS CoV-2. Age was associated with anti-spike IgG antibody positivity (P<.002). Participants >30 years were more likely to be seropositive (28-29%) than younger participants (15.4%). Additionally, seropositivity was associated with female gender (P<.001) and a higher BMI (P<.006). In the multivariate logistic regression, age >30, female gender and BMI >40 were associated with seropositivity.

CONCLUSION

The percentage of seropositive individuals reflects the high level of undiagnosed COVID-19 patients among the population. Our results will help in a better evaluation of the public health measures applied during the COVID-19 pandemic and any future public health crises.

LIMITATIONS

Sample size was small, single-center study and no rural areas were included.

CONFLICT OF INTEREST

None.

A COVID-19 mortality prediction model for Korean patients using nationwide Korean disease control and prevention agency database

The experience of the early nationwide COVID-19 pandemic in South Korea led to an early shortage of medical resources. For efficient resource allocation, accurate prediction of the prognosis or mortality of confirmed patients is essential. Therefore, the aim of this study was to develop an accurate model for predicting COVID-19 mortality using epidemiolocal and clinical variables and for identifying a high-risk group of confirmed patients. Clinical and epidemiolocal variables of 4049 patients with confirmed COVID-19 between January 20, 2020 and April 30, 2020 collected by the Korean Disease Control and Prevention Agency were used. Among the 4049 total confirmed patients, 223 patients died, while 3826 patients were released from isolation. Patients who had the following risk factors showed significantly higher risk scores: age over 60 years, male sex, difficulty breathing, diabetes, cancer, dementia, change of consciousness, and hospitalization in the intensive care unit. High accuracy was shown for both the development set (n = 2467) and the validation set (n = 1582), with AUCs of 0.96 and 0.97, respectively. The prediction model developed in this study based on clinical features and epidemiological factors could be used for screening high-risk groups of patients and for evidence-based allocation of medical resources.

Evolution of resistance to COVID-19 vaccination with dynamic social distancing

The greatest hope for a return to normalcy following the COVID-19 pandemic is worldwide vaccination. Yet, a relaxation of social distancing that allows increased transmissibility, coupled with selection pressure due to vaccination, will probably lead to the emergence of vaccine resistance. We analyse the evolutionary dynamics of COVID-19 in the presence of dynamic contact reduction and in response to vaccination. We use infection and vaccination data from six different countries. We show that under slow vaccination, resistance is very likely to appear even if social distancing is maintained. Under fast vaccination, the emergence of mutants can be prevented if social distancing is maintained during vaccination. We analyse multiple human factors that affect the evolutionary potential of the virus, including the extent of dynamic social distancing, vaccination campaigns, vaccine design, boosters and vaccine hesitancy. We provide guidelines for policies that aim to minimize the probability of emergence of vaccine-resistant variants.

Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model

In the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers from breathing inabilities. The highly contagious nature of this corona virus disease (COVID-19) caused the rapid outbreak of the disease around the world. India contracted the disease from China and reported its first case on January 30, 2020, in Kerala. Despite several counter measures taken by Government, India like other countries could not restrict the outbreak of the epidemic. However, it is believed that the strict policies adopted by the Indian Government have slowed the rate of the epidemic to a certain extent. This article proposes an adaptive SEIR disease model and a sequence-to-sequence (Seq2Seq) learning model to predict the future trend of COVID-19 outbreak in India and analyze the performance of these models. Optimization of hyper parameters using RMSProp is done to obtain an efficient model with lower convergence time. This article focuses on evaluating the performance of deep learning networks and epidemiological models in predicting a pandemic outbreak.

Modeling Vaccine Efficacy for COVID-19 Outbreak in New York City

Simple Summary

This article aims to study the COVID-19 data for New York City. We use both the daily number of second dose vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.

Abstract

In this article we study the efficacy of vaccination in epidemiological reconstructions of COVID-19 epidemics from reported cases data. Given an epidemiological model, we developed in previous studies a method that allowed the computation of an instantaneous transmission rate that produced an exact fit of reported cases data of the COVID-19 outbreak. In this article, we improve the method by incorporating vaccination data. More precisely, we develop a model in which vaccination is variable in its effectiveness. We develop a new technique to compute the transmission rate in this model, which produces an exact fit to reported cases data, while quantifying the efficacy of the vaccine and the daily number of vaccinated. We apply our method to the reported cases data and vaccination data of New York City.

Stochastic mathematical models for the spread of COVID-19: a novel epidemiological approach

In this paper, three stochastic mathematical models are developed for the spread of the coronavirus disease (COVID-19). These models take into account the known special characteristics of this disease such as the existence of infectious undetected cases and the different social and infectiousness conditions of infected people. In particular, they include a novel approach that considers the social structure, the fraction of detected cases over the real total infected cases, the influx of undetected infected people from outside the borders, as well as contact-tracing and quarantine period for travellers. Two of these models are discrete time-discrete state space models (one is simplified and the other is complete) while the third one is a continuous time-continuous state space stochastic integro-differential model obtained by a formal passing to the limit from the proposed simplified discrete model. From a numerical point of view, the particular case of Lebanon has been studied and its reported data have been used to estimate the complete discrete model parameters, which can be of interest in estimating the spread of COVID-19 in other countries. The obtained simulation results have shown a good agreement with the reported data. Moreover, a parameters' analysis is presented in order to better understand the role of some of the parameters. This may help policy makers in deciding on different social distancing measures.

Potential role of Drug Repositioning Strategy (DRS) for management of tauopathy

Tauopathy is a term that has been used to represent a pathological condition in which hyperphosphorylated tau protein aggregates in neurons and glia which results in neurodegeneration, synapse loss and dysfunction and cognitive impairments. Recently, drug repositioning strategy (DRS) becomes a promising field and an alternative approach to advancing new treatments from actually developed and FDA approved drugs for an indication other than the indication it was originally intended for. This paradigm provides an advantage because the safety of the candidate compound has already been established, which abolishes the need for further preclinical safety testing and thus substantially reduces the time and cost involved in progressing of clinical trials. In the present review, we focused on correlation between tauopathy and common diseases as type 2 diabetes mellitus and the global virus COVID-19 and how tau pathology can aggravate development of these diseases in addition to how these diseases can be a risk factor for development of tauopathy. Moreover, correlation between COVID-19 and type 2 diabetes mellitus was also discussed. Therefore, repositioning of a drug in the daily clinical practice of patients to manage or prevent two or more diseases at the same time with lower side effects and drug-drug interactions is a promising idea. This review concluded the results of pre-clinical and clinical studies applied on antidiabetics, COVID-19 medications, antihypertensives, antidepressants and cholesterol lowering drugs for possible drug repositioning for management of tauopathy.

Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong

Hong Kong employed a strategy of intermittent public health and social measures alongside increasingly stringent travel regulations to eliminate domestic SARS-CoV-2 transmission. By analyzing 1899 genome sequences (>18% of confirmed cases) from 23-January-2020 to 26-January-2021, we reveal the effects of fluctuating control measures on the evolution and epidemiology of SARS-CoV-2 lineages in Hong Kong. Despite numerous importations, only three introductions were responsible for 90% of locally-acquired cases. Community outbreaks were caused by novel introductions rather than a resurgence of circulating strains. Thus, local outbreak prevention requires strong border control and community surveillance, especially during periods of less stringent social restriction. Non-adherence to prolonged preventative measures may explain sustained local transmission observed during wave four in late 2020 and early 2021. We also found that, due to a tight transmission bottleneck, transmission of low-frequency single nucleotide variants between hosts is rare.

SARS-CoV-2 Dysregulates Neutrophil Degranulation and Reduces Lymphocyte Counts

SARS-CoV-2, the virus that causes COVID-19, has given rise to one of the largest pandemics, affecting millions worldwide. High neutrophil-to-lymphocyte ratios have been identified as an important correlate to poor recovery rates in severe COVID-19 patients. However, the mechanisms underlying this clinical outcome and the reasons for its correlation to poor prognosis are unclear. Furthermore, the mechanisms involved in healthy neutrophils acquiring a SARS-CoV-2-mediated detrimental role are yet to be fully understood. In this study, we isolated circulating neutrophils from healthy donors for treatment with supernates from infected epithelial cells and direct infection with SARS-CoV-2 in vitro. Infected epithelial cells induced a dysregulated degranulation of primary granules with a decrease in myeloperoxidase (MPO), but slight increase in neutrophil elastase release. Infection of neutrophils resulted in an impairment of both MPO and elastase release, even though CD16 receptor shedding was upregulated. Importantly, SARS-CoV-2-infected neutrophils had a direct effect on peripheral blood lymphocyte counts, with decreasing numbers of CD19+ B cells, CD8+ T cells, and CD4+ T cells. Together, this study highlights the independent role of neutrophils in contributing to the aberrant immune responses observed during SARS-CoV-2 infection that may be further dysregulated in the presence of other immune cells.

The COVID-19 pandemic: a new epoch and fresh challenges for cancer patients and caregivers-a descriptive cross-sectional study

PURPOSE

Cancer patients and their caregivers are overwhelmed with features of uncertainty, fear, shock, worry, anxiety, sadness, and grief. To add on to their misery, the COVID-19 pandemic has severely afflicted the cancer care delivery. The study was conducted to observe the challenges faced by cancer patients and their caregivers and to formulate strategies for oncological setups to overcome those challenges.

METHODS

After obtaining institutional ethical clearance, a descriptive cross-sectional study was conducted to observe the challenges faced by patients and their caregivers at the level of various domains (physical, logistic, psychological, socioeconomic, and spiritual) who visited the outpatient and inpatient department of cancer pain and palliative care unit. The results were expressed in absolute numbers.

RESULTS

Major challenges encountered were suffering from physical symptoms like pain, nausea, vomiting, dyspnea (90%), postponement of cancer treatment (80%), fear of contracting COVID infection due to hospital visit (93.5%), lack of accommodation (70%), and lack of spiritual clarity and hope (50%).

CONCLUSIONS

Major challenges faced by patients were in physical and psychological domains, and those by caregivers were in socioeconomic domains and handling physical symptoms of their patients. It is imperative to recognize and be cognizant of the challenges faced by cancer patients and their caregivers. Health care setups should formulate strategies to alleviate these challenges and provide holistic care to cancer patients. These strategies will hold in good stead for future pandemics also.

A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.

Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19

Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.

Assessing the Psychological Impact of the Pandemic COVID -19 in Uninfected High-Risk Population

Purpose

To assess the impact of the COVID-19 pandemic on the psyche of uninfected people with chronic diseases in the Elduim community, White Nile State, Sudan, during the COVID -19 pandemic.

Methods

We used a generalized anxiety disorder scale (GAD -7) and a patient health questionnaire (PHQ-9) for psychological assessment. The study included two hundred thirty-four participants; all participants with a chronic disease but not infected with COVID -19 were between 24 and 65 years of age. Residents of the study area were randomly selected. Descriptive statistics and a t-test were used for associations with a p-value of 0.05 or less.

Results

This study found that anxiety rated by GAD 7 was either mild (18, 7.7%), moderate (98, 41.9%), or severe (41, 17.5%) among participants. PHQ 9-rated depression showed 22 (9.4%) mild depression, most of them in participants aged 36–44 years. Participants with kidney disease showed major depression 11 (42.31%). Factors that significantly affected anxiety scores were age 24–35 years (P =0.002), university graduates (P < 0.000), married (P < 0.000), those with diabetes and hypertension (P =0.041), and urban residents (P < 0.023). Those who had secondary education were married and smoked were significantly more likely to have major depression than those with another educational status (p < 0.05).

Conclusion

COVID 19 pandemic had a significant impact on the psyche of uninfected people with chronic diseases in Sudan, and significant associated factors were identified. Unique interventions are strongly recommended to reduce the psychological impact of the COVID 19 pandemic.

SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics

State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states-Rhode Island, Massachusetts, and Pennsylvania-that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.

Infection dynamics of COVID-19 virus under lockdown and reopening

Motivated by COVID-19, we develop and analyze a simple stochastic model for the spread of disease in human population. We track how the number of infected and critically ill people develops over time in order to estimate the demand that is imposed on the hospital system. To keep this demand under control, we consider a class of simple policies for slowing down and reopening society and we compare their efficiency in mitigating the spread of the virus from several different points of view. We find that in order to avoid overwhelming of the hospital system, a policy must impose a harsh lockdown or it must react swiftly (or both). While reacting swiftly is universally beneficial, being harsh pays off only when the country is patient about reopening and when the neighboring countries coordinate their mitigation efforts. Our work highlights the importance of acting decisively when closing down and the importance of patience and coordination between neighboring countries when reopening.

Presence of Anti-MDA5 Antibody and Its Value for the Clinical Assessment in Patients With COVID-19: A Retrospective Cohort Study

Background

Striking similarities have been found between coronavirus disease 2019 (COVID-19) and anti-melanoma differentiation-associated gene 5 (MDA5) antibody (Ab)-related dermatomyositis, implying a shared autoinflammatory aberrance. Herein, we aim to investigate whether the anti-MDA5 Ab is present in COVID-19 and correlates with the severity and adverse outcome of COVID-19 patients.

Methods and Findings

We retrospectively recruited 274 adult inpatients with COVID-19 in this study, including 48, 164, and 62 cases of deaths, severe, and non-severe patients respectively. The anti-MDA5 Ab was determined by ELISA and verified by Western Blotting, which indicated that the positive rate of anti-MDA5 Ab in COVID-19 patients was 48.2% (132/274). The clinical and laboratory features, as well as outcomes between patients with positive and negative anti-MDA5 Ab were compared and we found that the anti-MDA5 Ab positive patients tended to represent severe disease (88.6% vs 66.9%, P<0.0001). We also demonstrated that the titer of anti-MDA5 Ab was significantly elevated in the non-survivals (5.95 ± 5.16 vs 8.22 ± 6.64, P=0.030) and the positive rate was also higher than that in the survivals (23.5% vs 12.0%, P=0.012). Regarding severe COVID-19 patients, we found that high titer of anti-MDA5 Ab (≥10.0 U/mL) was more prevalent in the non-survivals (31.2% vs 14.0%, P=0.006). Moreover, a dynamic analysis of anti-MDA5 Ab was conducted at different time-points of COVID-19, which revealed that early profiling of anti-MDA5 Ab could distinguish severe patients from those with non-severe ones.

Conclusions

Anti-MDA5 Ab was prevalent in the COVID-19 patients and high titer of this antibody is correlated with severe disease and unfavorable outcomes.

Modeling the dynamics of COVID-19 pandemic with implementation of intervention strategies

The ongoing COVID-19 epidemic spread rapidly throughout India, with 34,587,822 confirmed cases and 468,980 deaths as of November 30, 2021. Major behavioral, clinical, and state interventions have implemented to mitigate the outbreak and prevent the persistence of the COVID-19 in human-to-human transmission in India and worldwide. Hence, the mathematical study of the disease transmission becomes essential to illuminate the real nature of the transmission behavior and control of the diseases. We proposed a compartmental model that stratify into nine stages of infection. The incidence data of the SRAS-CoV-2 outbreak in India was analyzed for the best fit to the epidemic curve and we estimated the parameters from the best fitted curve. Based on the estimated model parameters, we performed a short-term prediction of our model. We performed sensitivity analysis with respect to R 0 and obtained that the disease transmission rate has an impact in reducing the spread of diseases. Furthermore, considering the non-pharmaceutical and pharmaceutical intervention policies as control functions, an optimal control problem is implemented to reduce the disease fatality. To mitigate the infected individuals and to minimize the cost of the controls, an objective functional has been formulated and solved with the aid of Pontryagin's maximum principle. This study suggest that the implementation of optimal control strategy at the start of a pandemic tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended time period. Our numerical simulations exhibit that the combination of two controls is more effective when compared with the combination of single control as well as no control.

COVID-19 among Pregnant Women Delivering in a Tertiary Care Center: A Descriptive Cross-sectional Study

INTRODUCTION

Coronavirus Disease 2019 pandemic is raging across the world and has affected pregnant women as well. There is limited information regarding COVID-19 in pregnant women. The study aimed to find the prevalence of COVID-19 among all pregnant women who delivered during the study period in a tertiary care center.

METHODS

This was a descriptive cross-sectional study conducted in a tertiary care center from 16th August to 15th November 2020 after obtaining ethical clearance from the Institutional Review Committee of a tertiary care center. All the women who delivered in the hospital during the study period were enrolled and they were subjected to COVID-19 Reverse Transcriptase Polymerase Chain Reaction test. A total of 667 samples were taken using convenience sampling technique. Data were analyzed using the Statistical Package for the Social Sciences version 24 software. Point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary data.

RESULTS

Among 667 pregnant women, the prevalence of COVID-19 was 47 (7.05%) (5.10-8.99 at 95% Confidence Interval). Though the majority of women were asymptomatic 40 (85.1%), 5 (10.64%) developed mild disease, 1 (2.12%) each had severe and critical COVID-19 pneumonia.

CONCLUSIONS

The prevalence of COVID-19 among pregnant women delivering in our center is similar to other studies done in similar settings. In our study, we found that the majority of women had been asymptomatic and were diagnosed on routine testing. Hence, it is important to test all pregnant women before delivery for Coronavirus Disease 2019 irrespective of the presence or absence of symptoms.

A global assessment of the impact of school closure in reducing COVID-19 spread

Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19-59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.

Clinical, Virological, Immunological, and Genomic Characterization of Asymptomatic and Symptomatic Cases With SARS-CoV-2 Infection in India

Purpose

The current global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), led to the investigation with clinical, biochemical, immunological, and genomic characterization from patients to understand the pathophysiology of viral infection.

Methods

Samples were collected from six asymptomatic and six symptomatic SARS-CoV-2-confirmed hospitalized patients in Bhubaneswar, Odisha, India. Clinical details, biochemical parameters, and treatment regimen were collected from a hospital; viral load was determined by RT-PCR; and the levels of cytokines and circulating antibodies in plasma were assessed by Bio-Plex and isotyping, respectively. In addition, whole-genome sequencing of viral strains and mutational analysis were carried out.

Results

Analysis of the biochemical parameters highlighted the increased levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), serum SGPT, serum SGOT, and ferritin in symptomatic patients. Symptomatic patients were mostly with one or more comorbidities, especially type 2 diabetes (66.6%). The virological estimation revealed that there was no significant difference in viral load of oropharyngeal (OP) samples between the two groups. On the other hand, viral load was higher in plasma and serum samples of symptomatic patients, and they develop sufficient amounts of antibodies (IgG, IgM, and IgA). The levels of seven cytokines (IL-6, IL-1α, IP-10, IL-8, IL-10, IFN-α2, IL-15) were found to be highly elevated in symptomatic patients, while three cytokines (soluble CD40L, GRO, and MDC) were remarkably higher in asymptomatic patients. The whole-genome sequence analysis revealed that the current isolates were clustered with 19B, 20A, and 20B clades; however, 11 additional changes in Orf1ab, spike, Orf3a, Orf8, and nucleocapsid proteins were acquired. The D614G mutation in spike protein is linked with higher virus replication efficiency and severe SARS-CoV-2 infection as three patients had higher viral load, and among them, two patients with this mutation passed away.

Conclusions

This is the first comprehensive study of SARS-CoV-2 patients from India. This will contribute to a better understanding of the pathophysiology of SARS-CoV-2 infection and thereby advance the implementation of effective disease control strategies.

Differential impacts of contact tracing and lockdowns on outbreak size in COVID-19 model applied to China

The COVID-19 pandemic has led to widespread attention given to the notions of "flattening the curve" during lockdowns, and successful contact tracing programs suppressing outbreaks. However a more nuanced picture of these interventions' effects on epidemic trajectories is necessary. By mathematical modeling each as reactive quarantine measures, dependent on current infection rates, with different mechanisms of action, we analytically derive distinct nonlinear effects of these interventions on final and peak outbreak size. We simultaneously fit the model to provincial reported case and aggregated quarantined contact data from China. Lockdowns compressed the outbreak in China inversely proportional to population quarantine rates, revealing their critical dependence on timing. Contact tracing had significantly less impact on final outbreak size, but did lead to peak size reduction. Our analysis suggests that altering the cumulative cases in a rapidly spreading outbreak requires sustained interventions that decrease the reproduction number close to one, otherwise some type of swift lockdown measure may be needed.

Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China

The COVID-19, the disease caused by the novel coronavirus 2019 (SARS-CoV-2), has caused graving woes across the globe since first reported in the epicenter Wuhan, Hubei, China, December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that put more than 900 million people housebound for more than two months since the lockdown of Wuhan on 23 January 2020 when other provinces in China followed suit. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns before and during the lockdown period. We quantify the synchrony of mobility patterns across provinces and within provinces. Using these mobility data, we calibrate movement flow between provinces in combination with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicenter Hubei. As such, infections can propagate further into other interconnected places both near and far, thereby necessitating synchronous lockdowns. Moreover, our data-driven modeling analysis shows that lockdowns and consequently reduced mobility lag a certain time to elicit an actual impact on slowing down the spreading and ultimately putting the epidemic under check. In spite of the vastly heterogeneous demographics and epidemiological characteristics across China, mobility data shows that massive travel restrictions have been applied consistently via a top-down approach along with high levels of compliance from the bottom up.

Can SARS-CoV-2 Infection Exacerbate Alzheimer's Disease? An Overview of Shared Risk Factors and Pathogenetic Mechanisms

The current coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2, is affecting every aspect of global society, including public healthcare systems, medical care access, and the economy. Although the respiratory tract is primarily affected by SARS-CoV-2, emerging evidence suggests that the virus may also reach the central nervous system (CNS), leading to several neurological issues. In particular, people with a diagnosis of Alzheimer's disease (AD) are a vulnerable group at high risk of contracting COVID-19, and develop more severe forms and worse outcomes, including death. Therefore, understanding shared links between COVID-19 and AD could aid the development of therapeutic strategies against both. Herein, we reviewed common risk factors and potential pathogenetic mechanisms that might contribute to the acceleration of neurodegenerative processes in AD patients infected by SARS-CoV-2.

Caspases and therapeutic potential of caspase inhibitors in moderate-severe SARS-CoV-2 infection and long COVID

BACKGROUND

COVID-19 can present with lymphopenia and extraordinary complex multiorgan pathologies that can trigger long-term sequela.

AIMS

Given that inflammasome products, like caspase-1, play a role in the pathophysiology of a number of co-morbid conditions, we investigated caspases across the spectrum of COVID-19 disease.

MATERIALS & METHODS

We assessed transcriptional states of multiple caspases and using flow cytometry, the expression of active caspase-1 in blood cells from COVID-19 patients in acute and convalescent stages of disease. Non-COVID-19 subject presenting with various comorbid conditions served as controls.

RESULTS

Single-cell RNA-seq data of immune cells from COVID-19 patients showed a distinct caspase expression pattern in T cells, neutrophils, dendritic cells, and eosinophils compared with controls. Caspase-1 was upregulated in CD4+ T-cells from hospitalized COVID-19 patients compared with unexposed controls. Post-COVID-19 patients with lingering symptoms (long-haulers) also showed upregulated caspase-1activity in CD4+ T-cells that ex vivo was attenuated with a select pan-caspase inhibitor. We observed elevated caspase-3/7levels in red blood cells from COVID-19 patients compared with controls that was reduced following caspase inhibition.

DISCUSSION

Our preliminary results suggest an exuberant caspase response in COVID-19 that may facilitate immune-related pathological processes leading to severe outcomes. Further clinical correlations of caspase expression in different stages of COVID-19 will be needed.

CONCLUSION

Pan-caspase inhibition could emerge as a therapeutic strategy to ameliorate or prevent severe COVID-19.

Biomarkers of ageing and frailty may predict COVID-19 severity

Coronavirus Disease 2019 (COVID-19) is caused by the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) - the culprit of an ongoing pandemic responsible for the loss of over 3 million lives worldwide within a year and a half. While the majority of SARS-CoV-2 infected people develop no or mild symptoms, some become severely ill and may die from COVID-19-related complications. In this review, we compile and comment on a number of biomarkers that have been identified and are expected to enhance the detection, protection and treatment of individuals at high risk of developing severe illnesses, as well as enable the monitoring of COVID-19 prognosis and responsiveness to therapeutic interventions. Consistent with the emerging notion that the majority of COVID-19 deaths occur in older and frail individuals, we researched the scientific literature and report the identification of a subset of COVID-19 biomarkers indicative of increased vulnerability to developing severe COVID-19 in older and frail patients. Mechanistically, increased frailty results from reduced disease tolerance, a phenomenon aggravated by ageing and comorbidities. While biomarkers of ageing and frailty may predict COVID-19 severity, biomarkers of disease tolerance may predict resistance to COVID-19 with socio-economic factors such as access to adequate health care remaining as major non-biomolecular influencers of COVID-19 outcomes.

Structure-based design and synthesis of copper(ii) complexes as antivirus drug candidates targeting SARS CoV-2 and HIV

This paper describes the structure-based design and synthesis of two novel square-planar trans-N2O2 Cu(ii) complexes [Cu(L1)2] (1) and [Cu(L2)2] (2) of 2-((Z)-(4-methoxyphenylimino)methyl)-4,6-dichlorophenol (L1H) and 2-((Z)-(2,4-dibromophenylimino)methyl)-4-bromophenol (L2H) as potential inhibitors against the main protease of the SARS-CoV-2 and HIV viruses.

Synthesis and characterization of novel copper(ii) complexes as potential drug candidates against SARS-CoV-2 main protease

Two novel copper(ii) Schiff base complexes, [Cu(L1)2] (1) and [Cu(L2)(CH3OH)(Cl)] (2) of [(Z)-(5-chloro-2-((3,5-dichloro-2-hydroxybenzylidene)amino)phenyl)(phenyl)methanone (L1H) and (Z)-(2((5-bromo-2-hydroxybenzylidene)amino-5-chlorophenyl)(phenyl)methanone)(L2H)], have been designed, synthesized and characterized.

Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modelling study of the City of Natal

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic hit almost all cities in Brazil in early 2020 and lasted for several months. Despite the effort of local state and municipal governments, an inhomogeneous nationwide response resulted in a death toll amongst the highest recorded globally. To evaluate the impact of the nonpharmaceutical governmental interventions applied by different cities-such as the closure of schools and businesses in general-in the evolution and epidemic spread of SARS-CoV-2, we constructed a full-sized agent-based epidemiological model adjusted to the singularities of particular cities. The model incorporates detailed demographic information, mobility networks segregated by economic segments, and restricting bills enacted during the pandemic period. As a case study, we analyzed the early response of the City of Natal-a midsized state capital-to the pandemic. Although our results indicate that the government response could be improved, the restrictive mobility acts saved many lives. The simulations show that a detailed analysis of alternative scenarios can inform policymakers about the most relevant measures for similar pandemic surges and help develop future response protocols.

Examining the correlation between the weather conditions and COVID-19 pandemic in Galicia

In late 2019 and early 2020, a new acute respiratory infection was detected. On 31 December 2019, Chinese health authorities notified an outbreak of pneumonia cases of unknown ethology in Wuhan city (Hubei Province, China) and began to spread rapidly throughout the world. Several scientists believe that the diseases may have originated from Bungarus multicinctus, a highly venomous snake traded in the Wuhan wet market, where meat from wild animals is sold. In this work, we pretend to analyze the influence of weather conditions in the transmission of COVID-19 in Galicia. Precisely, we examine the correlation between weather conditions considering temperature and humidity and epidemiological variables such as active cases, recovered, and deceased. In order to study the correlation between weather conditions and transmission of COVID-19, we employ a generalization of the correlation coefficient of Pearson, r, applied to fuzzy sets. This tool generalizes classical set theory and allows modeling situations with uncertainty.

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

The ongoing COVID-19 pandemic has caused unprecedented public health concern in Bangladesh. This study investigated the role of Non-Pharmaceutical Interventions on COVID-19 transmission and post-lockdown scenarios of 64 administrative districts and the country as a whole based on the spatiotemporal variations of effective reproduction number (R t) of COVID-19 incidences. The daily confirmed COVID-19 data of Bangladesh and its administrative districts from March 8, 2020, to March 10, 2021, were used to estimate R t. This study finds that the maximum value of R t reached 4.15 (3.43, 4.97, 95% CI) in late March 2020, which remained above 1 afterwards in most of the districts. Containment measures are moderately effective in reducing transmission by 24.03%. The R t was established below 1 from early December 2020 for overall Bangladesh and a gradual increase of R t above 1 has been seen from early February 2021. The basic reproduction number (R 0) in Bangladesh probably varied around 2.02 (1.33–3.28, 95% CI). This study finds a significant positive correlation (r = 0.75) between population density and COVID-19 incidence and explaining 56% variation in Bangladesh. The findings of this study are expected to support the policymakers to adopt appropriate measures for curbing the COVID-19 transmission effectively.

What can we learn from COVID-19 data by using epidemic models with unidentified infectious cases?

The COVID-19 outbreak, which started in late December 2019 and rapidly spread around the world, has been accompanied by an unprecedented release of data on reported cases. Our objective is to offer a fresh look at these data by coupling a phenomenological description to the epidemiological dynamics. We use a phenomenological model to describe and regularize the reported cases data. This phenomenological model is combined with an epidemic model having a time-dependent transmission rate. The time-dependent rate of transmission involves changes in social interactions between people as well as changes in host-pathogen interactions. Our method is applied to cumulative data of reported cases for eight different geographic areas. In the eight geographic areas considered, successive epidemic waves are matched with a phenomenological model and are connected to each other. We find a single epidemic model that coincides with the best fit to the data of the phenomenological model. By reconstructing the transmission rate from the data, we can understand the contributions of the changes in social interactions (contacts between individuals) on the one hand and the contributions of the epidemiological dynamics on the other hand. Our study provides a new method to compute the instantaneous reproduction number that turns out to stay below 3.5 from the early beginning of the epidemic. We deduce from the comparison of several instantaneous reproduction numbers that the social effects are the most important factor in understanding the epidemic wave dynamics for COVID-19. The instantaneous reproduction number stays below 3.5, which implies that it is sufficient to vaccinate 71% of the population in each state or country considered in our study. Therefore, assuming the vaccines will remain efficient against the new variants and adjusting for higher confidence, it is sufficient to vaccinate 75-80% to eliminate COVID-19 in each state or country.

Role of cardiovascular computed tomography parameters and lungs findings in predicting severe COVID-19 patients: a single-centre retrospective study

Background

Results

Conclusions

Epidemiological Characteristics of Hospitalized Patients with Moderate versus Severe COVID-19 Infection: A Retrospective Cohort Single Centre Study

COVID-19 has a devastating impact worldwide. Recognizing factors that cause its progression is important for the utilization of appropriate resources and improving clinical outcomes. In this study, we aimed to identify the epidemiological and clinical characteristics of patients who were hospitalized with moderate versus severe COVID-19 illness. A single-center, retrospective cohort study was conducted between 3 March and 9 September 2020. Following the CDC guidelines, a two-category variable for COVID-19 severity (moderate versus severe) based on length of stay, need for intensive care or mechanical ventilation and mortality was developed. Data including demographic, clinical characteristics, laboratory parameters, therapeutic interventions and clinical outcomes were assessed using descriptive and inferential analysis. A total of 1002 patients were included, the majority were male (n = 646, 64.5%), Omani citizen (n = 770, 76.8%) and with an average age of 54.2 years. At the bivariate level, patients classified as severe were older (Mean = 55.2, SD = 16) than the moderate patients (Mean = 51.5, SD = 15.8). Diabetes mellitus was the only significant comorbidity potential factor that was more prevalent in severe patients than moderate (n = 321, 46.6%; versus n = 178, 42.4%; p < 0.001). Under the laboratory factors; total white cell count (WBC), C-reactive protein (CRP), Lactate dehydrogenase (LDH), D-dimer and corrected calcium were significant. All selected clinical characteristics and therapeutics were significant. At the multivariate level, under demographic factors, only nationality was significant and no significant comorbidity was identified. Three clinical factors were identified, including; sepsis, Acute respiratory disease syndrome (ARDS) and requirement of non-invasive ventilation (NIV). CRP and steroids were also identified under laboratory and therapeutic factors, respectively. Overall, our study identified only five factors from a total of eighteen proposed due to their significant values (p < 0.05) from the bivariate analysis. There are noticeable differences in levels of COVID-19 severity among nationalities. All the selected clinical and therapeutic factors were significant, implying that they should be a key priority when assessing severity in hospitalized COVID-19 patients. An elevated level of CRP may be a valuable early marker in predicting the progression in non-severe patients with COVID-19. Early recognition and intervention of these factors could ease the management of hospitalized COVID-19 patients and reduce case fatalities as well medical expenditure.

Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods

Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may be interested in estimating the distribution of time from onset of disease symptoms to death and only have data on individuals who have died. This may be the case, for example, at the beginning of an epidemic. Right truncation causes the distribution of times to event in the sample to be biased towards shorter times compared to the population distribution, and appropriate statistical methods should be used to account for this bias. This article is a review of such methods, particularly in the context of an infectious disease epidemic, like COVID-19. We consider methods for estimating the marginal time-to-event distribution, and compare their efficiencies. (Non-)identifiability of the distribution is an important issue with right-truncated data, particularly at the beginning of an epidemic, and this is discussed in detail. We also review methods for estimating the effects of covariates on the time to event. An illustration of the application of many of these methods is provided, using data on individuals who had died with coronavirus disease by 5 April 2020.

SARS-CoV-2 Seroprevalence in Aden, Yemen: A population-based study

Background

In Yemen, initial surveillance of coronavirus disease 2019 (COVID-19) focused primarily on patients with symptoms or severe disease. The full spectrum of the disease remains unclear. To the best of the authors’ knowledge, this is the first seroprevalence study performed in Yemen.

Methods

This cross-sectional investigation included 2001 participants from all age groups from four districts in Aden, southern Yemen. A multi-stage sampling method was used. Data were collected using a well-structured questionnaire, and blood samples were taken. Healgen COVID-19 IgG/IgM Rapid Diagnostic Test (RDT) Cassettes were used in all participants. All positive RDTs and 14% of negative RDTs underwent enzyme-linked immunosorbent assay (ELISA) testing (WANTAI SARS-CoV-2 Ab ELISA Kit) for confirmation.

Results

In total, 549 of 2001 participants were RDT positive and confirmed by ELISA, giving a prevalence of COVID-19 of 27.4%. The prevalence of immunoglobulin G was 25%. The prevalence of asymptomatic COVID-19 in the entire study group was 7.9%. The highest prevalence was observed in Al-Mansurah district (33.4%). Regarding sociodemographic factors, the prevalence of COVID-19 was significantly higher among females, housewives and subjects with a history of contact with a COVID-19 patient: 32%, 31% and 39%, respectively.

Conclusion

This study found high prevalence of COVID-19 in the study population. Household transmission was common.

Changing Disparities in Coronavirus Disease 2019 (COVID-19) Burden in the Ethnically Homogeneous Population of Hong Kong Through Pandemic Waves: An Observational Study

BACKGROUND

Disparities were marked in previous pandemics, usually with higher attack rates reported for those in lower socioeconomic positions and for ethnic minorities.

METHODS

We examined characteristics of laboratory-confirmed coronavirus disease 2019 (COVID-19) cases in Hong Kong, assessed associations between incidence and population-level characteristics at the level of small geographic areas, and evaluated relations between socioeconomics and work-from-home (WFH) arrangements.

RESULTS

The largest source of COVID-19 importations switched from students studying overseas in the second wave to foreign domestic helpers in the third. The local cases were mostly individuals not in formal employment (retirees and homemakers) and production workers who were unable to WFH. For every 10% increase in the proportion of population employed as executives or professionals in a given geographic region, there was an 84% (95% confidence interval [CI], 1-97%) reduction in the incidence of COVID-19 during the third wave. In contrast, in the first 2 waves, the same was associated with 3.69 times (95% CI, 1.02-13.33) higher incidence. Executives and professionals were more likely to implement WFH and experienced frequent changes in WFH practice compared with production workers.

CONCLUSIONS

Consistent findings on the reversed socioeconomic patterning of COVID-19 burden between infection waves in Hong Kong in both individual- and population-level analyses indicated that risks of infections may be related to occupations involving high exposure frequency and WFH flexibility. Contextual determinants should be taken into account in policy planning aiming at mitigating such disparities.

Synthesis, crystal structure, computational study and anti-virus effect of mixed ligand copper (II) complex with ONS donor Schiff base and 1, 10-phenanthroline

This work deals with the synthesis, crystal structure, computational study and antiviral potential of mixed ligand copper(II) complex [Cu(L)(phen)](1), (where, H₂L = (Z)-N'-((E)-2-hydroxy-3,5-diiodobenzylidene)-N,N-dimethylcarbamohydrazonothioic acid, phen = 1,10-phenanthroline). The Schiff base ligand (H₂L) is coordinated with Cu(II) ion in O, N, S-tridentate mode. The copper complex (1) crystallized in the monoclinic system of the space group P21/c with eight molecules in the unit cell and reveals a square pyramidal geometry. Furthermore, we also perform quantum chemical calculations to get insights into the structure-property relationship and functional properties of ligand (H₂L) and its copper (II) complex [Cu(L)(phen)](1). Complex [Cu(L)(phen)](1) was also virtually designed in-silico evaluation by Swiss-ADME. Additionally, inspiring by recent developments to find a potential inhibitor for the COVID-19 virus, we have also performed molecular docking study of ligand and its copper complex (1) to see if our compounds shows an affinity for the main protease (M^pro) of COVID-19 spike protein (PDB ID: 7C8U). Interestingly, the results are found quite encouraging where the binding affinity and inhibition constant were found to be -7.14 kcal/mol and 5.82 μM for ligand (H₂L) and -6.18 kcal/mol and 0.76 μM for complex [Cu(L)(phen)](1) with M^pro protein. This binding affinity is reasonably well as compared to recently known antiviral drugs. For instance, the binding affinity of ligand and complex was found to be better than docking results of chloroquine (-6.293 kcal/mol), hydroxychloroquine (-5.573 kcal/mol) and remdesivir (-6.352 kcal/mol) with M^pro protein. The present study may offer the technological solutions and potential inhibition to the COVID-19 virus in the ongoing and future challenges of the global community. In the framework of synthesis and characterization of mixed ligand copper (II) complex; the major conclusions can be drawn as follow.

"Five Keys to Safer Food" and COVID-19

On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) and, up to 18:37 a.m. on 9 December 2021, it has produced 268,440,530 cases and 5,299,511 deaths. This disease, in some patients, included pneumonia and shortness of breath, being transmitted through droplets and aerosols. To date, there is no scientific literature to justify transmission directly from foods. In this review, we applied the precautionary principle for the home and the food industry using the known "Five Keys to Safer Food" manual developed by the World Health Organization (WHO) and extended punctually in its core information from five keys, in the light of new COVID-19 evidence, to guarantee a possible food safety tool.

Estimating the strength of selection for new SARS-CoV-2 variants

Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present two models for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals that uncertainty is large very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.

Estimating the transmission advantage of the D614G mutant strain of SARS-CoV-2, December 2019 to June 2020

IntroductionThe SARS-CoV-2 lineages carrying the amino acid change D614G have become the dominant variants in the global COVID-19 pandemic. By June 2021, all the emerging variants of concern carried the D614G mutation. The rapid spread of the G614 mutant suggests that it may have a transmission advantage over the D614 wildtype.AimOur objective was to estimate the transmission advantage of D614G by integrating phylogenetic and epidemiological analysis.MethodsWe assume that the mutation D614G was the only site of interest which characterised the two cocirculating virus strains by June 2020, but their differential transmissibility might be attributable to a combination of D614G and other mutations. We define the fitness of G614 as the ratio of the basic reproduction number of the strain with G614 to the strain with D614 and applied an epidemiological framework for fitness inference to analyse SARS-CoV-2 surveillance and sequence data.ResultsUsing this framework, we estimated that the G614 mutant is 31% (95% credible interval: 28-34) more transmissible than the D614 wildtype. Therefore, interventions that were previously effective in containing or mitigating the D614 wildtype (e.g. in China, Vietnam and Thailand) may be less effective against the G614 mutant.ConclusionOur framework can be readily integrated into current SARS-CoV-2 surveillance to monitor the emergence and fitness of mutant strains such that pandemic surveillance, disease control and development of treatment and vaccines can be adjusted dynamically.

Age-related mortality in 61,993 confirmed COVID-19 cases over three epidemic waves in Aragon, Spain. Implications for vaccination programmes

BACKGROUND

Risk for severe COVID-19 increases with age. Different vaccination strategies are currently being considered, including those aimed at slowing down transmission and those aimed at providing direct protection to those most at risk.

METHODS

The objectives of the current study were i) to assess age-related incidence and survival between PCR-diagnosed COVID-19 cases (n = 61,993) in the Autonomous Community of Aragon from March to November 2020, and ii) to characterize age differences regarding the course of the disease in hospitalized patients in a tertiary university hospital.

RESULTS

We found a similar incidence of COVID-19 in individuals between 10 and 79 years. Incidence increased in those over 80 years possibly because of the elevated transmission within the nursing homes. We observed a profound disparity among age groups; case fatality rates (CFRs) were near 0 in cases younger than 39 years throughout different waves. In contrast, there was an age-dependent and progressive increase in the CFRs, especially during the first pandemic wave. SARS-CoV-2 infection caused a more severe and rapid progression in older patients. The elderly required faster hospitalization, presented more serious symptoms on admission, and had a worse clinical course. Hospitalized older individuals, even without comorbidities, had an increased mortality risk directly associated with their age. Lastly, the existence of comorbidities dramatically increased the CFRs in the elderly, especially in males.

CONCLUSION

The elevated incidence of COVID-19 and the vulnerability of the elderly call for their prioritization in vaccination and targeted prevention measures specifically focused on this aged population.