Background and objective COVID-19 is a highly disseminating viral disease imparted by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), which was declared a global pandemic by the World Health Organization. In our study, we aimed to describe the clinical characteristics of the first 100 hospitalized patients of confirmed COVID-19 in a developing country.

Background and objectives Infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are rapidly spreading, posing a serious threat to the health of people worldwide, resulting in the World Health Organization officially declaring it a pandemic. There are several biochemical markers linked with predicting the severity of coronavirus disease. This study aims to identify the most effective predictive biomarker such as C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), procalcitonin (PCT), and D-dimer, among others, in predicting the clinical outcome of the disease. Materials and methods This study was conducted as a retrospective, observational, multi-centric study, including all admitted COVID-19 positive patients only. The disease outcome was followed along with the hospital course of every patient at the time of analysis. Baseline laboratory investigations of all patients were monitored both at admission and discharge. A comparative analysis was done between the survivors (n=263) and non-survivors (n=101). Statistical analysis was conducted using IBM SPSS Statistics for Windows Version 25 (Armonk, NY: IBM Corp.). Results Of 364 patients, 65.7% were in the isolation ward, and 34.3% were in the intensive care unit; 72.3% of patients survived, while 27.7% of patients died. The mean age of the study population was 52.6 ± 15.8 years with female patients significantly younger than male patients (p=0.001) and 50 to 75 years being the most common age group (p=0.121). Among the survivors versus non-survivors of COVID-19, there were significant differences in total leukocyte count (p<0.001), neutrophil count, (p<0.001), lymphocyte count (p<0.001), urea (p<0.001), serum bicarbonate (p=0.001), CRP levels (p<0.001), LDH (p=0.013), and D-dimer (p<0.001) at admission. At discharge, the laboratory values of non-surviving patients showed significant leukocytosis (p<0.001), neutrophilia (p<0.001), lymphocytopenia (p<0.001), decreased monocytes (p<0.001), elevated urea and creatinine (p<0.001), hypernatremia (p<0.001), decreased serum bicarbonate levels (p<0.001), elevated CRP level (p=0.040), LDH (p<0.001), 1 2 3 4 4 4 5

Background and Objectives: COVID-19 is a global pandemic. In our study, we aimed to utilize the hematological parameters in predicting the prognosis and mortality in COVID-19 patients. Materials and methods: A retrospective, observational study was conducted to include all the admitted patients (n = 191) having COVID-19 Polymerase chain reaction (PCR) positive, and evaluated those for prognosis and disease outcome by utilizing several biochemical and hematological markers. Results: Amongst the patients admitted in the ward versus in the intensive care unit (ICU), there were significant differences in mean hemoglobin (P = 0.003), total leukocyte count (P = 0.001), absolute neutrophil and lymphocyte counts (P < 0.001), absolute monocyte count (P = 0.019), Neutrophil-to-Lymphocyte ratio (NLR) and Lymphocyte-to-Monocyte ratio (LMR) (P < 0.001), Platelet-to-Lymphocyte ratio (PLR) and Lymphocyte-to C-reactive protein ratio (LCR) (P = 0.002), and C-reactive protein (CRP) levels (P < 0.001). Amongst the deceased patients, there was significant leukocytosis (P = 0.008), neutrophilia and lymphopenia (P < 0.001), increased NLR (P = 0.001), decreased LMR (P < 0.001), increased PLR (p = 0.017), decreased LCR (p = 0.003), and elevated CRP level (P < 0.001). A receiver operating characteristic curve obtained for the above parameters showed NLR (AUC: 0.841, PPV: 83.6%) and PLR (AUC: 0.703, PPV: 81.8%) for ICU patients, while NLR (AUC: 0.860, PPV: 91.1%) and PLR (AUC: 0.677, PPV: 87.5%) for the deceased patients had significant accuracy for predicting the disease severity of COVID-19 in comparison to survivors. Conclusion: The inflammatory markers and hematological indices are a good guide for predicting the severity and disease outcome of coronavirus disease. NLR and PLR are elevated in severe disease while LMR and LCR are inversely correlating with disease severity.

Background and objectives Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for the coronavirus disease 2019 (COVID-19) pandemic. The disease mainly affects the respiratory system of the patient, in particular, the lungs, which leads to patients presenting with acute respiratory distress syndrome and acute respiratory failure, with 5-15% of patients requiring observation in the intensive care unit (ICU) with respiratory support in the form of ventilation. This study was aimed at identifying the role of biochemical markers in the risk stratification of invasive and non-invasive ventilation of hospitalized COVID-19 patients. Materials and methods The study was conducted as a prospective, observational study of all admitted COVID-19 patients. A comparative analysis was performed of the survivors who were on invasive versus (vs) non-invasive ventilation and the non-survivors similarly. After computing the descriptive statistics, a multinomial logistic regression model was applied to obtain an unadjusted odds ratio (OR) at 95% confidence interval (CI), with Hosmer-Lemeshow (HL) goodness-of-fit test used to predict the fitness of the data. Kaplan-Meier survival curves were obtained for each of the laboratory investigations predicting survival along with the intensive care stay and invasive ventilation. A log-rank test was carried out to compare the survival distributions. Results A total of 373 included patients in the study had a mean age of 52.78 ± 15.76 years with females younger than males, and indifference amongst invasive vs non-invasively ventilated (p=0.821). Females were slightly more prone to invasive ventilation (p=0.097). Overall, 39% of the subjects did not need respiratory support, while 13% were on a ventilator, 16% on bilevel positive airway pressure/continuous positive airway pressure (BiPAP/CPAP), and 31% on supplemental oxygen therapy. Among the laboratory markers, mean hemoglobin was evidently lower in the invasive group, leukocytosis and thrombocytopenia were present in both invasively ventilated and non-surviving patients, while neutrophilia and lymphocytopenia were statistically indifferent among the mode of ventilation. Elevated urea, creatinine, and sodium were also significantly deranged laboratory markers amongst the invasively ventilated group. C-reactive protein (CRP) and lactate dehydrogenase (LDH) were elevated significantly in the invasive group, while serum ferritin was more frequently raised in the non-invasively ventilated group. Procalcitonin (PCT) was significantly associated with invasive ventilation as opposed to the non-invasive group. D-dimer was equally raised in both the groups at admission but significantly elevated in the invasive group at discharge. A multinomial regression model signified D-dimer (

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.