ABSTRACT
Objective: The aim of this study was to observe the secondary infection rate and transmission dynamics of COVID-19 among household contacts, and their associations with various factors across four dimensions of interaction. Methods: This was a case-ascertained study among unvaccinated household contacts of a laboratory-confirmed COVID-19 case in New Delhi between December 2020 and July 2021. For this study, 99 index cases and their 316 household contacts were interviewed and sampled (blood and oro-nasal swab) on days 1, 7, 14, and 28. Results: The secondary infection rate among unvaccinated household contacts was 44.6% (95% confidence interval (CI) 39.1-50.1). The predictors of secondary infection among individual contact levels were: being female (odds ratio (OR) 2.13), increasing age (OR 1.01), symptoms at baseline (OR 3.39), and symptoms during follow-up (OR 3.18). Among index cases, age of the primary case (OR 1.03) and symptoms during follow-up (OR 6.29) were significantly associated with secondary infection. Among household-level and contact patterns, having more rooms (OR 4.44) and taking care of the index case (OR 2.02) were significantly associated with secondary infection. Conclusion: A high secondary infection rate highlights the need to adopt strict measures and advocate COVID-19-appropriate behaviors. A targeted approach for higher-risk household contacts would efficiently limit infections among susceptible contacts.
ABSTRACT
COVID-19 is arguably the biggest health crisis the world has faced in the 21st century. Therefore, two of the polyherbal formulations, Infuza and Kulzam were assessed for the prevention of COVID-19 infection as a repurposed medication. Four hundred seven high-risk subjects were recruited in the present open-label randomized controlled clinical trial for eligibility. After assessment for eligibility, remaining 251 subjects were randomized to the test and control groups. Further, 52 high-risk subjects in Infuza, 51 in Kulzam, 51 in Infuza & Kulzam and 53 in control group completed the 14 days of intervention/assessment. The phenotyping of lymphocytes at baseline (0 day) and after 14 days of treatment was carried out by flow cytometry assays. A total of 15.09% high-risk subjects in control group turned positive as compared to only 7.69% in Infuza, 3.92% in Kulzam and 1.96% in Infuza & Kulzam groups. The rate of conversion to COVID-19 infection in Infuza & Kulzam group was minimal and statistically significant as compared to control group (p0.017). No significant changes in phenotype of lymphocytes (T, B, NK cells), absolute lymphocyte count and cytokine levels were found in study groups. However, there was a decreasing trend of hs-CRP level in high-risk subjects after intervention of polyherbal formulations for 14 days. The combination of Infuza and Kulzam may synergistically prevent COVID-19 infection in high-risk subjects of COVID-19.
Subject(s)
COVID-19 Drug Treatment , COVID-19 , COVID-19/prevention & control , Humans , SARS-CoV-2 , Treatment OutcomeABSTRACT
PURPOSE: Identifying asymptomatic SARS-COV-2 carriage is one of the crucial factors in controlling the COVID 19 pandemic. The relationship between the asymptomatic viral carriage and the rate of seroconversion needs better understanding. The present study was conducted to identify the asymptomatic COVID-19 infection and seropositivity in high-risk contacts in the southern district of Delhi, India. METHODS: Following the screening of 6961 subjects, a total of 407 asymptomatic high-risk subjects were selected. Demographic data, socioeconomic status, and history of COVID-19 related symptoms in the last 4 months were recorded. Blood samples and Nasopharyngeal/oropharyngeal swabs were collected for the detection of SARS-COV-2 RNA and anti-SARS-COV-2 antibodies. RESULTS: 55 asymptomatic high-risk subjects (13.5%) tested positive for SARS-COV-2 infection and among them, 70.9% remained asymptomatic throughout their course of infection. The seropositivity among the subjects was 28.9% (n â= â118) and was found significantly higher among lower-middle socioeconomic strata (p â= â0.01). The antibody levels were significantly higher (p â= â0.033) in individuals with a previous history of COVID-19 like symptoms as compared to the subjects, who had no such history. Asymptomatic healthcare workers showed a significantly increased rate of SARS-COV-2 infection (p â= â0.004) and seropositivity (p â= â0.005) as compared to the non-healthcare workers. Subjects, who were exposed to infection at their workplace (non-hospital setting) had the least RT-PCR positivity rate (p â= â0.03). CONCLUSIONS: A large proportion of SARS-COV-2 infection remains completely asymptomatic. The rate of asymptomatic carriage and seropositivity is significantly higher in healthcare workers as compared to the general population. The level of SARS-COV-2 antibodies is directly related to the appearance of symptoms. These observations may contribute to redefining COVID 19 screening, infection control, and professional health practice strategies.
Subject(s)
COVID-19 , Antibodies, Viral , COVID-19/diagnosis , COVID-19/epidemiology , Health Personnel , Humans , RNA, Viral , SARS-CoV-2ABSTRACT
The Coronavirus disease (Covid-19) has been declared a pandemic by World Health Organisation (WHO) and till date caused 585,727 numbers of deaths all over the world. The only way to minimize the number of death is to quarantine the patients tested Corona positive. The quick spread of this disease can be reduced by automatic screening to cover the lack of radiologists. Though the researchers already have done extremely well to design pioneering deep learning models for the screening of Covid-19, most of them results in low accuracy rate. In addition, over-fitting problem increases difficulties for those models to learn on existing Covid-19 datasets. In this paper, an automated Covid-19 screening model is designed to identify the patients suffering from this disease by using their chest X-ray images. The model classifies the images in three categories - Covid-19 positive, other pneumonia infection and no infection. Three learning schemes such as CNN, VGG-16 and ResNet-50 are separately used to learn the model. A standard Covid-19 radiography dataset from the repository of Kaggle is used to get the chest X-ray images. The performance of the model with all the three learning schemes has been evaluated and it shows VGG-16 performed better as compared to CNN and ResNet-50. The model with VGG-16 gives the accuracy of 97.67%, precision of 96.65%, recall of 96.54% and F1 score of 96.59%. The performance evaluation also shows that our model outperforms two existing models to screen the Covid-19.