Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network.

The outbreak of COVID-19 has engulfed the entire world since the end of 2019, causing tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability to accurately predict the spread of disease as the arrangements for the essential supply of medical items largely depend on prior predictions. The objective of the study is to train a reliable model for predicting the spread of Coronavirus. The prediction capabilities of various powerful models such as the Autoregression Model (AR), Global Autoregression (GAR), Stacked-LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), Facebook Prophet (FBProphet), and Residual Recurrent Neural Network (Res-RNN) were taken into consideration for predicting COVID-19 using the historical data of daily confirmed cases along with Twitter data. The COVID-19 prediction results attained from these models were not up to the mark. To enhance the prediction results, a novel model is proposed that utilizes the power of Res-RNN with some modifications. Gated Recurrent Unit (GRU) and LSTM units are also introduced in the model to handle the long-term dependencies. Neural Networks being data-hungry, a merged layer was added before the linear layer to combine tweet volume as additional features to reach data augmentation. The residual links are used to handle the overfitting problem. The proposed model RNN Convolutional Residual Network (RNNCON-Res) showcases dominating capability in country-level prediction 20 days ahead with respect to existing State-Of-The-Art (SOTA) methods. Sufficient experimentation was performed to analyze the prediction capability of different models. It was found that the proposed model RNNCON-Res has achieved 91% accuracy, which is better than all other existing models.

Efficacy of add-on Ayurveda and Yoga intervention in health care workers of tertiary care hospital during COVID-19: Randomized controlled trial.

BACKGROUND: The present study aimed to evaluate the safety and prophylactic efficacy of add-on Comprehensive Ayurveda and mindfulness-based Yoga (CAY) regimen to standard care among HealthCare Workers (HCWs) against COVID-19. MATERIALS AND METHODS: This prospective single-blind (outcome assessor-blinded) RCT was conducted in tertiary care hospital in Delhi during July 2020-April 2021. HCWs of both sexes were randomized to add-on CAY intervention or control group. The primary outcomes were the incidence of confirmed COVID-19 positive cases and influenza-like illness events (ILI). Secondary outcomes were anxiety (GAD-7), depression (PHQ-9), and quality of life (SF-36) at the end of 12 weeks. RESULTS: Three hundred fifty-six participants (181 in intervention and 175 in the control group) were randomized. With the modified intention to treat approach, we analyzed 309 participants. The mean age for the intervention and control group was 39.3 ± 10.1 and 36.6 ± 10 years, respectively. Incidence of COVID-19 event was higher in control group compared to CAY group (16 of 164 [9.8%] vs. 11 of 145 [7.6%]; P = 0.50). The incidence of ILI events was also higher in the control group as compared to the CAY group (14 of 164 [8.5%] vs 9 of 145 [6.2%]). The health change domain of the SF-36 questionnaire showed statistically significant improvement in the CAY group as compared to the control group (P < 0.01). CONCLUSION: Incidence of COVID-19 and ILI events was lower in the CAY group compared with the contr ol group, though the difference is not statistically significant.

Prevalence of psychological outcomes and its associated factors in healthcare personnel working during COVID-19 outbreak in India.

Background: Care of COVID-19 patients has been shown to affect the mental health of healthcare personnel (HCP), however, there is little data reflecting psychological health of HCP in India. Aims: The present study was undertaken to assess the prevalence of psychological outcomes and its association with various sociodemographic and occupational factors among the HCP in India. Methodology: A cross-sectional, online survey, using snowball sampling method was conducted between June 1, 2020, and June 22, 2020. The HCP working in COVID-19 designated hospitals across India were invited to participate. Patient Health Questionnaire-4 and 19-item stress-related questionnaire were used to evaluate symptoms of overall anxiety, depression, COVID-19 infection specific anxiety, exhaustion, and workload. Results: In this cross-sectional study with 2334 HCP from 27 states and 7 union territories of India; 17.9% of participants had depression, 18.7% had overall anxiety, 26.5% had exhaustion, 30.3% reported heavy workload, and 25.4% had COVID-19 infection-specific anxiety, respectively. The HCP working in states with higher caseload was a common risk factor for overall anxiety (odds ratio [OR], 1.7; P < 0.001), depression (OR, 1.6; P < 0.001), COVID-19 infection-specific anxiety (OR, 2.5; P < 0.001), exhaustion (OR, 3.1; P < 0.001), and heavy workload (OR, 2.6; P < 0.001). Nurses were more at risk for depression (OR, 2.2; P < 0.001), anxiety specific to COVID-19 infection (OR, 1.3; P = 0.034), and heavy workload (OR, 2.9; P < 0.001); while doctors were more at risk for overall anxiety (OR, 2.0; P = 0.001) and exhaustion (OR, 3.1; P < 0.001). Conclusions: Frontline workers, specifically nurses and doctors, and those working in states with high COVID-19 caseload are more at risk for adverse psychological outcomes. The relatively less prevalence compared with other countries, is perhaps a reflection of measures undertaken, including early lockdown, ensuring better all-round preparedness and social norms.