The pandemic of COVID-19 has afflicted every individual and has initiated a cascade of directly or indirectly involved events in precipitating mental health issues. The human species is a wanderer and hunter-gatherer by nature, and physical social distancing and nationwide lockdown have confined an individual to physical isolation. The present review article was conceived to address psychosocial and other issues and their aetiology related to the current pandemic of COVID-19. The elderly age group has most suffered the wrath of SARS-CoV-2, and social isolation as a preventive measure may further induce mental health issues. Animal model studies have demonstrated an inappropriate interacting endogenous neurotransmitter milieu of dopamine, serotonin, glutamate, and opioids, induced by social isolation that could probably lead to observable phenomena of deviant psychosocial behavior. Conflicting and manipulated information related to COVID-19 on social media has also been recognized as a global threat. Psychological stress during the current pandemic in frontline health care workers, migrant workers, children, and adolescents is also a serious concern. Mental health issues in the current situation could also be induced by being quarantined, uncertainty in business, jobs, economy, hampered academic activities, increased screen time on social media, and domestic violence incidences. The gravity of mental health issues associated with the pandemic of COVID-19 should be identified at the earliest. Mental health organization dedicated to current and future pandemics should be established along with Government policies addressing psychological issues to prevent and treat mental health issues need to be developed.   References World Health Organization (WHO) Coronavirus Disease (COVID-19) Dashboard. 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Objectives: The present study is aimed at estimating patient flow dynamical parameters and requirement of hospital beds. Secondly, the effects of age and gender on parameters were evaluated. Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from 29 Feb to 19 May 2020 for two hazards – ‘Hazard 1’ was hospital discharge and ‘Hazard 2’ was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission . The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis. Results: The Kaplan Meier estimates of lengths of hospital stay (Median =10 days, IQR =10 days) and median survival rate ( more than 60 days due to large amount of censored data) were obtained. The Cox Model for ‘Hazard 1’ showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox Model 2 showed no significant difference of gender on survival rate. The case fatality rate 8.1 % , recovery rate 78.8% , mortality rate 0.10 per 100 person--days and hospital admission rate 0.35 per 105 person-days were estimated.Conclusion : The study estimates hospital bed requirement based on patient flow dynamic parameters. Furthermore, study concludes that average length of hospital stay were similar for patients of both genders and all age groups.

Objectives: The present study is aimed at estimating patient flow dynamic parameters and requirement for hospital beds. Second, the effects of age and gender on parameters were evaluated.Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from February 29 through May 19, 2020, for two hazards: Hazard 1 was hospital discharge, and Hazard 2 was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission. The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis.Results: The Kaplan Meier estimates of lengths of hospital stay (median = 10 days, IQR = 5–15 days) and median survival rate (more than 60 days due to a large amount of censored data) were obtained. The Cox model for Hazard 1 showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox model 2 showed no significant difference of age and gender on survival rate. The case fatality rate of 8.1%, recovery rate of 78.8%, mortality rate of 0.10 per 100 person-days, and hospital admission rate of 0.35 per 100,000 person-days were estimated.Conclusion: The study estimates hospital bed requirements based on median length of hospital stay and hospital admission rate. Furthermore, the study concludes there are no effects of age and gender on average length of hospital stay and no effects of age and gender on survival time in above-60 age groups.

Abstract The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centerpiece in evidence based disease management. Numerous approaches that use mathematical modeling have been used to predict the outcome of the pandemic, including data driven models, empirical and hybrid models. This study was aimed at prediction COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Retrieving real time data from the Johns Hopkins dashboard from 11 Mar 2020 to 25 Jun 2020 (N = 107 time points) to fit the model. The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths respectively with minimum Akaike Informaton Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 Jun 2020 to 05 Jul 2020 showed a trend toward continuous increment. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) of ARIMA(1,3,2) model was 21137 and 166330 respectively. Similarly, PredRMSE and BaseRMSE of ARIMA(3,3,1) model was 668.7 and 5431 respectively. We propose that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimization and evidence based decision making for a subsequent state of affairs.

Background: The triage of coronavirus-19 patients into various strata based on some prognostic indicator might prove a utilitarian strategy in the management of epidemic. The goal of health-care facilities is optimization of the use of medical resources. The present study aimed to develop a predictor model of mortality risk from routine hematologic parameters. Patients and Methods: In this retrospective case–control study, seventy survivors (n = 47) and nonsurvivors (n = 23) were enrolled who were laboratory-confirmed coronavirus disease 2019 (COVID-19) cases from SMS Medical College, Jaipur (Rajasthan, India). The clinical and routine blood profile of the survivors and nonsurvivors was recorded. A logistic regression model was fitted with step-wise method to the above dataset with dependent variable such as survivor or nonsurvivor and independent variables such as age, sex, symptoms, random blood glucose, and complete blood count. The best model was selected on the basis of Akaike information criterion. Results: It was observed that differential neutrophil count (%) and random blood sugar (RBS in mg/dL) are the statistically significant regressors (P < 0.05). The performance metrics of the model with 5-fold cross-validation showed area under the receiver operating characteristic curve, sensitivity, specificity, and validation accuracy to be 0.95, 90%, 92%, and 70%, respectively. The cutoff probability comes out at 0.30 for the outcome (nonsurvivor as success). Conclusion: The study concludes that differential neutrophil count and RBS levels can be used as early screening tools of mortality risk in COVID-19 patients and they assist in further patient management.