Satisfaction levels of students toward online teaching and assessment in a medical college in North India during COVID-19 related lockdown

[...]online teaching was sparsely used in medical colleges in India. The curriculum of medical schools is prepared with the aim of developing certain competencies and skills among the medical schools. [...]it was not a temporary measure and online classes continued for the good part of the year 2020. [...]this study aimed to evaluate the perceptions of students toward online teaching and assessment module implemented in a medical college of North India and factors which affected the overall satisfaction among medical students during COVID pandemic. materials and methods Study Design and Sampling A cross-sectional observational questionnaire based survey was conducted on 600 medical students in our medical college. Variables with P < 0.25 were included in the multivariate model. [...]a multivariate model was developed to identify significant factors associated with students not being overall satisfied with online teaching.

Trends in psychiatric diagnoses by COVID-19 infection and hospitalization among patients with and without recent clinical psychiatric diagnoses in New York city from March 2020 to August 2021.

Determining emerging trends of clinical psychiatric diagnoses among patients infected with the SARS-CoV-2 virus is important to understand post-acute sequelae of SARS-CoV-2 infection or long COVID. However, published reports accounting for pre-COVID psychiatric diagnoses have usually relied on self-report rather than clinical diagnoses. Using electronic health records (EHRs) among 2,358,318 patients from the New York City (NYC) metropolitan region, this time series study examined changes in clinical psychiatric diagnoses between March 2020 and August 2021 with month as the unit of analysis. We compared trends in patients with and without recent pre-COVID clinical psychiatric diagnoses noted in the EHRs up to 3 years before the first COVID-19 test. Patients with recent clinical psychiatric diagnoses, as compared to those without, had more subsequent anxiety disorders, mood disorders, and psychosis throughout the study period. Substance use disorders were greater between March and August 2020 among patients without any recent clinical psychiatric diagnoses than those with. COVID-19 positive patients (both hospitalized and non-hospitalized) had greater post-COVID psychiatric diagnoses than COVID-19 negative patients. Among patients with recent clinical psychiatric diagnoses, psychiatric diagnoses have decreased since January 2021, regardless of COVID-19 infection/hospitalization. However, among patients without recent clinical psychiatric diagnoses, new anxiety disorders, mood disorders, and psychosis diagnoses increased between February and August 2021 among all patients (COVID-19 positive and negative). The greatest increases were anxiety disorders (378.7%) and mood disorders (269.0%) among COVID-19 positive non-hospitalized patients. New clinical psychosis diagnoses increased by 242.5% among COVID-19 negative patients. This study is the first to delineate the impact of COVID-19 on different clinical psychiatric diagnoses by pre-COVID psychiatric diagnoses and COVID-19 infections and hospitalizations across NYC, one of the hardest-hit US cities in the early pandemic. Our findings suggest the need for tailoring treatment and policies to meet the needs of individuals with pre-COVID psychiatric diagnoses.

Proteomic Approach for Comparative Analysis of the Spike Protein of SARS-CoV-2 Omicron (B.1.1.529) Variant and Other Pango Lineages.

The novel SARS-CoV-2 variant, Omicron (B.1.1.529), is being testified, and the WHO has characterized Omicron as a variant of concern due to its higher transmissibility and very contagious behavior, immunization breakthrough cases. Here, the comparative proteomic study has been conducted on spike-protein, hACE2 of five lineages (α, ß, δ, γ and Omicron. The docking was performed on spike protein- hACE-2 protein using HADDOCK, and PRODIGY was used to analyze the binding energy affinity using a reduced Haddock score. Followed by superimposition in different variant-based protein structures and calculated the esteem root mean square deviation (RMSD). This study reveals that Omicron was seen generating a monophyletic clade. Further, as α variant is the principal advanced strain after Wuhan SARS-CoV-2, and that is the reason it was showing the least likeness rate with the Omicron and connoting Omicron has developed of late with the extreme number of mutations. α variant has shown the highest binding affinity with hACE2, followed by ß strain, and followed with γ. Omicron showed a penultimate binding relationship, while the δ variant was seen as having the least binding affinity. This proteomic basis in silico analysis of variable spike proteins of variants will impart light on the development of vaccines and the identification of mutations occurring in the upcoming variants.

Magnitude and causes of routine immunization disruptions during COVID-19 pandemic in developing countries.

The ongoing pandemic of COVID-19 is a threat to various routine healthcare services. India's routine immunization (RI) campaign is one of largest ever known. In this review, we discuss the magnitude of disruption of RI activities due to COVID-19 pandemic, various causes of it and recommend ways to reduce the disruptions. Prominent literature databases were searched till April 30, 2021 for articles reporting disruptions of RI due to COVID-19. One study from India and numerous from outside India reported significant declines in the vaccine coverage rates during the lockdown period, which ranged from March 2020 till August 2020 in different regions of the world. Some reported disruptions for all vaccines, while a few reported sparing of birth doses. Shortage of healthcare workers due for them being diverted to patient care services and their reduced movement due to lockdowns and non-availability of public transport were prominent causes. Parents avoided RI sessions as they feared them or their children getting infected. They also faced travel restrictions, just like the healthcare workers. Children of school entry age and those from poorer socio-demographic profile appeared to miss the doses more frequently. Ministry of Health and Family Welfare, India has issued guidelines for conducting fixed and outreach RI sessions while following COVID-appropriate behavior. Promptly identifying missed out children and scheduling catch-up sessions is required to sustain the gains made over the decades by the immunization program of India.

A cluster-based model of COVID-19 transmission dynamics.

Many countries have manifested COVID-19 trajectories where extended periods of constant and low daily case rate suddenly transition to epidemic waves of considerable severity with no correspondingly drastic relaxation in preventive measures. Such solutions are outside the scope of classical epidemiological models. Here, we construct a deterministic, discrete-time, discrete-population mathematical model called cluster seeding and transmission model, which can explain these non-classical phenomena. Our key hypothesis is that with partial preventive measures in place, viral transmission occurs primarily within small, closed groups of family members and friends, which we label as clusters. Inter-cluster transmission is infrequent compared with intra-cluster transmission but it is the key to determining the course of the epidemic. If inter-cluster transmission is low enough, we see stable plateau solutions. Above a cutoff level, however, such transmission can destabilize a plateau into a huge wave even though its contribution to the population-averaged spreading rate still remains small. We call this the cryptogenic instability. We also find that stochastic effects when case counts are very low may result in a temporary and artificial suppression of an instability; we call this the critical mass effect. Both these phenomena are absent from conventional infectious disease models and militate against the successful management of the epidemic.

Reappraisal of trifluperidol against Nsp3 as a potential therapeutic for novel COVID-19: a molecular docking and dynamics study.

Novel COVID-19 is a highly infectious disease that is caused by the recently discovered SARS-CoV-2. It is a fast-spreading disease that urgently requires therapeutics. The current study employed computational regression methods to target the ADP-ribose phosphatase (ADRP) domain of Nsp3 using FDA-approved drugs. Identified leads were further investigated using molecular dynamics simulation (MDS). The screening and MDS results suggest that trifluperidol could be a novel inhibitor of the ADRP domain of Nsp3. Trifluperidol could, therefore, be used to help control the spread of COVID-19, either alone or in combination with antiviral agents.

COVID-19 IMPACT ON INDIAN FOOD AND HOSPITALITY SECTOR WITH SPECIFIC REFERENCE TO POTATO CROP

Disruption through the current epidemic is appropriate for all sectors of the economy. In any case, the collision of the coronavirus epidemic on the agribusiness, Indian food, and housing industry experienced to a large extent. In addition, it is acknowledged that changes in the presence and price flooding of basic staple vegetables such as potatoes can have many serious consequences for the friendliness sector, including the café and online food conference stages. Recalling the immaturity of these issues, in the current examination we chose to keep potato as a busy item as it is considered the most staple vegetable worldwide. The day-to-day information of potato performance and cost was first measured in the date between January 2018 and sixteenth April 2020, utilizing information on the market from Agmark (the government's alleged webpage for vegetable costs and appearances). it was done. During the next one and a half years during the epidemic, Price was trying to determine the form and pattern of conduct. The time order ARIMA model was adjustedfor this reason. Azadpur mandi is being called the biggest market of Asia. Scheduling range has recommended pressure in the potato production network to change the two costs and appearances in the coming half year. This investigation shows that the cost of potato will increase by 58.7% by October 2020 and the contrast between October 2019 and appearance will be 3.11% less than a year ago.

Impact of COVID-19 pandemic on sleep in children and adolescents: a systematic review and meta-analysis.

OBJECTIVE: This systematic review and meta-analysis were conducted to study the prevalence and pattern of sleep disturbances in children and adolescents during the COVID-19 pandemic. METHODS: MEDLINE, EMBASE, and Web of Science were searched for original studies describing sleep abnormalities in children and adolescents with or without pre-existing neurobehavioral disorders during the COVID-19 pandemic. The pooled estimates for various sleep abnormalities were calculated using a random-effect model. RESULTS: Of 371 articles screened, 16 studies were included. Among these, five studies were in preschool children, two were in children with pre-existing neurobehavioral disorders and the remaining were in school going children and adolescents. The outcome measures used for sleep were markedly heterogeneous across the studies. The pooled prevalence of any sleep disturbance in children during the pandemic was 54%(95%CI:50-57%). Interestingly, the prevalence in pre-school children was lower than pre-pandemic times (RR = 0.87; 95% CI:0.58-1.30) but this was not statistically significant. The pooled prevalence of children not meeting sleep recommendation was 49% (95%CI: 39-58%). CONCLUSION: The prevalence of sleep problems in children and adolescents during the COVID-19 pandemic is alarming. Pre-school children had a trend towards relatively fewer sleep disturbances due to home confinement measures in comparison with pre-pandemic times. Sleep duration recommendations were not met in nearly half of healthy children. However, these conclusions need to be seen in light of limited literature on the topic, few included studies done in heterogenous populations, and dubious quality of inferences drawn from these studies which were predominantly online surveys. PROSPERO REGISTRATION ID: CRD42020213788.

A new approach to the dynamic modeling of an infectious disease

In this work we propose a delay differential equation as a lumped parameter or compartmental infectious disease model featuring high descriptive and predictive capability, extremely high adaptability and low computational requirement. Whereas the model has been developed in the context of COVID-19, it is general enough to be applicable with such changes as necessary to other diseases as well. Our fundamental modeling philosophy consists of a decoupling of public health intervention effects, immune response effects and intrinsic infection properties into separate terms. All parameters in the model are directly related to the disease and its management;we can measure or calculate their values a priori basis our knowledge of the phenomena involved, instead of having to extrapolate them from solution curves. Our model can accurately predict the effects of applying or withdrawing interventions, individually or in combination, and can quickly accommodate any newly released information regarding, for example, the infection properties and the immune response to an emerging infectious disease. After demonstrating that the baseline model can successfully explain the COVID-19 case trajectories observed all over the world, we systematically show how the model can be expanded to account for heterogeneous transmissibility, detailed contact tracing drives, mass testing endeavours and immune responses featuring different combinations of temporary sterilizing immunity, severity-reducing immunity and antibody dependent enhancement. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Modelling of Natural Phenomena is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Impact of COVID-19 Pandemic on Labour Supply, Wages and Gross Value Added in India

This article estimates the first-order supply shock through labour supply reduction associated with the containment measures taken by the Government of India to control COVID-19 spread. We provide the estimates for Lockdown 1.0 and Lockdown 2.0, from 25 March to 3 May 2020, when india had the highest stringency measures in the world. To get an extensive impact of the COVID-19 pandemic on the labour market, we carry out an in-depth analysis of labour supply shocks by employment status, industry level and occupation. The workers impacted are those who work in a non-essential industry and are not able to work from home. To identify jobs that cannot be done from home, we use a novel approach and construct an occupation-based Remote Labour Index (RLI) for India. Using the PLFS (2017?2018) we find that 116.18 million (25% of the total employed) and 78.93 million (17% of total employed) workers were affected during Lockdown 1.0 and Lockdown 2.0, respectively. The expected monthly wage and income loss to workers is estimated to be Rs. 864.5 billion (2017?2018 prices). Further, the reduction of Gross Value Added (2012?2012 prices) is estimated at 14% compared to a no-COVID scenario.JEL Classification Codes: E01, J21, J22, J24, J33, J38

Impact of reproduction number on the multiwave spreading dynamics of COVID-19 with temporary immunity: A mathematical model.

OBJECTIVES: The recent discoveries of phylogenetically confirmed COVID-19 reinfection cases worldwide, together with studies suggesting that antibody titres decrease over time, raise the question of what course the epidemic trajectories may take if immunity were really to be temporary in a significant fraction of the population. The objective of this study is to obtain an answer for this important question. METHODS: We construct a ground-up delay differential equation model tailored to incorporate different types of immune response. We considered two immune responses: (a) short-lived immunity of all types, and (b) short-lived sterilizing immunity with durable severity-reducing immunity. RESULTS: Multiple wave solutions to the model are manifest for intermediate values of the reproduction number R; interestingly, for sufficiently low as well as sufficiently high R, we find conventional single-wave solutions despite temporary immunity. CONCLUSIONS: The versatility of our model, and its very modest demands on computational resources, ensure that a set of disease trajectories can be computed virtually on the same day that a new and relevant immune response study is released. Our work can also be used to analyse the disease dynamics after a vaccine is certified for use and information regarding its immune response becomes available.

Coronavirus disease (COVID-19): A systematic review and meta-analysis to evaluate the impact of various comorbidities on serious events.

BACKGROUND AND AIMS: Currently there is limited knowledge on medical comorbidities and COVID-19; we conducted a systematic review and meta-analysis to evaluate the impact of various morbidities on serious events in COVID 19. METHODS: PubMed, Cochrane Central Register of Clinical Trials were searched on April 28, 2020, to extract published articles that reported the outcomes of COVID-19 patients. The search terms were "coronavirus" and "clinical characteristics". ICU admission, mechanical ventilation, ARDS, Pneumonia, death was considered serious events. The comorbidities assessed in the study were Hypertension (HTN), Diabetes mellitus (DM), Cardiovascular diseases (CVD), Chronic obstructive pulmonary disease (COPD) and Chronic Kidney disease (CKD). Subsequently, comparisons between comorbidity patient group and the non-comorbidity patient groups, in terms of serious events were made using the pooled estimates of odd's ratio (OR) RESULTS: We identified 688 published results and 16 studies with 3994 patients were included in the systematic review. Serious events were seen in 526(13.16%) patients. Presence of hypertension with OR 2.95, diabetes mellitus with OR 3.07, Cardio vascular disease with OR 4.58, COPD with OR 6.66 and Chronic kidney disease with OR 5.32 had significant association in patients with COVID 19 on having serious events. Presence of diabetes mellitus (OR 2.78)) had a significant impact on death in COVID 19 patients with a p-value 0.004. CONCLUSIONS: Presence of medical comorbidities in COVID-19 leads to higher risk of developing serious events i.e. ICU admission, mechanical intubation and mortality. The presence of Diabetes mellitus has a significant impact on mortality rate in COVID-19 patients.