Evaluation and analysis of rates of bacterial co-infections and antimicrobial usage in COVID-19 patients: a retrospective cohort analysis

Background: The coronavirus disease 2019 (COVID- 19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread over the world. Although there are minimal microbiological and antibiotic data on COVID-19, bacterial co-infections have been related to poor outcomes in respiratory viralinfections. Adequate antibiotic use in conformity withantibiotic stewardship (ABS) recommendations is necessary during the pandemic. Material and procedure: We conducted a retrospective single-center cohort analysis of 140 adulthospitalised patients (ages 17-99) with confirmed COVID-19 who were admitted between February 16, 2021, and April 22, 2021, and who were discharged onMay 6, 2021. From 140 COVID-19 participants, the following clinical data was gathered: Men made up 63.5 percent of the participants, with a median age of 63.5 years (range 17-99). Results: According to local ABS recommendations, the most commonly administered antibiotic regimen was ampicillin/sulbactam (41.5 percent) with a median length of 6 (range 1-13) days. Urine antigen testing for Legionella pneumophila and Streptococcus peumoniaewas negative in all of the patients. In critically ill patients hospitalised to intensive care units (n = 50), co-infections with Enterobacterales (34.0%) and Aspergillus fumigatus (18.0%) were discovered. Blood cultures obtained at admission had a diagnostic yield of 4.2 percent. Conclusion: While bacterial and fungal co-infections are rare in COVID-19 patients, they are widespread in critically ill individuals. More investigation into the impact of antimicrobial therapy on therapeutic success in COVID-19 patients is essential to prevent antibiotic abuse. COVID-19 management might be improved with the aid of ABS standards. It's also necessary to look at the microbiological patterns of infectious consequences in COVID-19 individuals who are severely unwell.

Assessing the adverse effects of immunization after covaxin and covishield vaccine in health care workers of India

Background: The highly contagious COVID-19 disease or Corona Virus disease is caused by a novel coronavirus. It is a pandemic affecting a large population globally concerning both health and financial aspect. Vaccination has emerged to be a vital measure to control COVID-19. However, fear of side effects is a major concern with these vaccines warranting further studies. Aim: The present study aimed to evaluate the adverse effects after vaccinations with either Covishield or Covaxin for COVID-19 infection. Methods: The study included 1160 health care professionals who received either Covaxin or Covishield for COVID-19 infection prevention. The adverse effects were noted following the first and second dose of vaccines at different time intervals of less than 24 hours, 24-48 hours, and between 3 to 7 days post-vaccination. WHO (World health organization) questionnaire was used for recording the side-effects where 720 subjects took Covishield and 440 subjects Covaxin. Results: The study results showed that most of the subjects reported side effects within 24 hours of first dose vaccination with 85% and 90% for Covishield and Covaxin respectively, and after 2nd dose were 74.16% and 89.09% respectively. The most common side effects reported after both vaccines were myalgia, chills, fever, headache, and general weakness. All adverse effects were self-limiting and subsided with no need for hospitalization. Conclusion: The present study concludes that no significant difference is seen concerning side effects with either Covaxin or Covishield in two doses and the two vaccines separately Further studies are warranted in different populations and age groups to get details on the safety data of these vaccines.

The Correlation of Respiratory System Compliance and Mortality in COVID-19 Acute Respiratory Distress Syndrome: Do Phenotypes Really Exist? (preprint)

Background: Recent literature suggests respiratory system compliance (Crs) based phenotypes exist among COVID-19 ARDS patients. We sought to determine whether these phenotypes exist and whether Crs predicts mortality. Methods: A retrospective observational cohort study of 111 COVID-19 ARDS patients admitted March 11-July 8, 2020. Crs was averaged for the first 72-hours of mechanical ventilation. Crs Results: 111 COVID-19 ARDS patients were included, 40 phenotype-H and 71 phenotype-L. Both the mean Pa O2 /Fi O2 ratio for the first 72-hours of mechanical ventilation and the Pa O2 /Fi O2 ratio hospital nadir were lower in phenotype-H than L(115[IQR87] vs 165[87], p =  0.016), (63[32] vs 75[59], p =  0.026). There were no difference in characteristics, diagnostic studies, or complications between groups. Twenty-seven (67.5%) phenotype-H patients died vs 37(52.1%) phenotype-L( p =  0.115). Multivariable regression did not reveal a mortality difference between phenotypes; however, a 2-fold mortality increase was noted in Crs  50ml/cmH 2 O when analyzing ordinal Crs groups. Moving up one group level (ex. Crs30-39.9ml/cmH 2 O to 40-49.9ml/cmH 2 O), was marginally associated with 14% lower risk of death(RR = 0.86, 95%CI 0.72, 1.01, p =  0.065). This attenuated(RR = 0.94, 95%CI 0.80, 1.11) when adjusting for pH nadir and Pa O2 /Fi O2 ratio nadir. Conclusion: We identified a spectrum of Crs in COVID-19 ARDS similar to Crs distribution in non-COVID-19 ARDS. While we identified increasing mortality as Crs decreased, there was no specific threshold marking significantly different mortality based on phenotype. We therefore would not define COVID-19 ARDS patients by phenotypes-H or L and would not stray from traditional ARDS ventilator management strategies.

A Comprehensive Analysis of COVID-19 Outbreak situation in India (preprint)

The outbreak of COVID-19 in different parts of the world is a major concern for all the administrative units of respective countries. India is also facing this very tough task for controlling the virus outbreak and has managed its growth rate through some strict measures. This study presents the current situation of coronavirus spread in India along with the impact of various measures taken for it. With the help of data sources (till 7th-8th April 2020) from various state units of India and Ministry of Health and Family Welfare, Government of India, this study presents various trends and patterns. This study answers six different research ques-tions in a comprehensive manner. It has been reported that growth rate of infected cases has been controlled with the help of National Lockdown, however some uncontrolled mass level events had negatively impacted the infected cases. With the help of exponential and polyno-mial regression modelling, the predictions of up to 75000 cases have been done by the end of April 2020. It has also been seen that there are some prominent clusters and patient nodes in the network of patients which are the major influencers for COVID-19 spread. Also, death rate case predictions have been done through multi-class classification models with an accuracy of 75%. At the end, strategies for continuation for lockdown has been discussed and presented. It appears that only essential services should be open for the citizens of India and the national lockdown should be carried on for next 2-4 weeks. This study will be useful for the Government of India and various states of India, Administrative Units of India, Frontline health workforce of India, researchers and scientists. This study will also be favorable for the administrative units of other countries to consider various aspects related to the control of COVID-19 outspread in their respective regions.

SEIR and Regression Model based COVID-19 outbreak predictions in India (preprint)

COVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals.

SEIR and Regression Model based COVID-19 outbreak predictions in India (preprint)

COVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals.

Trend Analysis and Forecasting of COVID-19 outbreak in India (preprint)

COVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holts linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.