Humanity has seen numerous pandemics during its course of evolution. The list includes several incidents from the past, such as measles, Ebola, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS), etc. The latest edition to this is coronavirus disease 2019 (COVID-19), caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of August 18, 2020, COVID-19 has affected over 21 million people from 180 + countries with 0.7 million deaths across the globe. Genomic technologies have enabled us to understand the genomic constitution of pathogens, their virulence, evolution, and rate of mutation, etc. To date, more than 83,000 viral genomes have been deposited in public repositories, such as GISAID and NCBI. While we are writing this, India is the third most affected country by COVID-19, with 2.7 million cases and > 53,000 deaths. Gujarat is the 11th highest affected state with a 3.48% death rate compared to the national average of 1.91%. In this study, a total of 502 SARS-CoV-2 genomes from Gujarat were sequenced and analyzed to understand its phylogenetic distribution and variants against global and national sequences. Further variants were analyzed from diseased and recovered patients from Gujarat and the world to understand its role in pathogenesis. Among the missense mutations present in the Gujarat SARS-CoV-2 genomes, C28854T (Ser194Leu) had an allele frequency of 47.62 and 7.25% in deceased patients from the Gujarat and global datasets, respectively. In contrast, the allele frequency of 35.16 and 3.20% was observed in recovered patients from the Gujarat and global datasets, respectively. It is a deleterious mutation present in the nucleocapsid (N) gene and is significantly associated with mortality in Gujarat patients with a p-value of 0.067 and in the global dataset with a p-value of 0.000924. The other deleterious variant identified in deceased patients from Gujarat (p-value of 0.355) and the world (p-value of 2.43E-06) is G25563T, which is located in Orf3a and plays a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct clusters under the GH clade of GISAID. This study will shed light on the viral haplotype in SARS-CoV-2 samples from Gujarat, India.

Humanity has seen numerous pandemics during its course of evolution. The list includes many such as measles, Ebola, SARS, MERS, etc. Latest edition to this pandemic list is COVID-19, caused by the novel coronavirus, SARS-CoV-2. As of 4th July 2020, COVID-19 has affected over 10 million people from 170+ countries, and 5,28,364 deaths. Genomic technologies have enabled us to understand the genomic constitution of the pathogens, their virulence, evolution, rate of mutations, etc. To date, more than 60,000 virus genomes have been deposited in the public depositories like GISAID and NCBI. While we are writing this, India is the 3rd most-affected country with COVID-19 with 0.6 million cases, and >18000 deaths. Gujarat is the fourth highest affected state with , 5.44 percent death rate compared to national average of 2.8 percent. Here, 361 SARS-CoV-2 genomes from across Gujarat have been sequenced and analyzed in order to understand its phylogenetic distribution and variants against global and national sequences. Further, variants were analyzed from diseased and recovered patients from Gujarat and the World to understand its role in pathogenesis. From missense mutations, found from Gujarat SARS-CoV-2 genomes, C28854T, deleterious mutation in nucleocapsid (N) gene was found to be significantly associated with mortality in patients. The other significant deleterious variant found in diseased patients from Gujarat and the world is G25563T, which is located in Orf3a and has a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct cluster under GH clade of GISAID.

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This retrospective data analysis study aims to analyze the data collected during adjunctive homeopathy treatment of COVID-19 patients by the Homoeopathic medical officers (HMO) in Gujarat state-dedicated COVID-19 hospitals (DCH) during the first wave of the pandemic. The HMOs used the standard data collection forms/sheets to record each patient's demographic information, clinical symptoms, homoeopathic management, and outcome data. Data of all cases hospitalized with COVID-19 of any age, and both genders were included, and entries with missing values or incomplete/ incorrect information were excluded from the analysis. The outcome measure is the recovery duration, time to clinical improvement, worsening symptoms, and indicated homeopathic medicines. Data from 2581 cases analyzed showed clinical recovery time after adjunctive homeopathy as 05 days (IQR: 3-7); the Mean was 5.19 days (SD:4.62), with 80% of patients (2063 out of 2581) discharged between 0-7 days out of which more than 20.4% patients (419 out of 2063) having at least one of the comorbidities. Only 03 deaths of male patients above 50 years with comorbidities and 67 cases (2.6%) with worsening symptoms were reported. The homeopathic medicines used were <em>Arsenic album</em> in 73.0% and <em>Bryonia alba</em> in 17.6% of cases. Adjunctive Homoeopathy and standard care in COVID-19 patients had a promising role in the early relief of clinical symptoms and less progression into severity in the risk group of elderly patients with comorbidities. There were no reported adverse effects of taking the adjunctive Homoeopathy, making it a potential choice for integrated use in managing COVID-19 patients.

Objectives: Currently available information on influenza vaccination rates for Germany is limited since published evidence does not include a detailed breakdown by age and/or risk status. The aim of our study was to estimate the age-specific seasonal influenza vaccine uptake for people with underlying chronic conditions and a healthy population in Germany. Methods: We used health insurance claims data of a large German statutory sickness fund to estimate influenza vaccination coverage rates covering eight seasons (2010-2011 to 2017-2018). The population was divided into nine age groups