ABSTRACT
Infection with the SARS-CoV-2 virus results in Covid 19, an infectious illness. Most persons who get Coronavirus will only experience mild to moderate symptoms and will get better without any special care. Some people get very sick and need medical attention. The rising mortality toll from COVID-19 underscores the importance of developing methods for early detection of the disease, which might aid in containing the epidemic and facilitating the creation of tailored mitigation strategies. Current research in chaotic dynamics indicates that coughs and other vocal sounds include lung health data that can be used for symptomatic reasons. Mel frequencies Cepstral Coefficients (MFCC) are applied to cough samples, and then the audio data from coughs is fed into a GridsearchCV model using a KNN-based classification method. Our model was developed using 217 samples from training data and 55 from testing data. Cough tests conducted on both males and females are included in the dataset. An evaluation found that the model had an accuracy of 83.3%. © 2022 IEEE.
ABSTRACT
Covid 19 is an infectious disease that is caused by infection due to SARS-CoV-2 virus. The vast majority of people infected with Corona virus will encounter mild to moderate symptoms and recover without any special treatment. In some case, some people become seriously ill and require clinical consideration. Because of the increase in number of death due to COVID-19, an techniques for the early discovery of the illness is very much needed that might assist with restricting its spread just as help in the development of targeted surrounding solutions. Coughs and other vocal sounds contain pulmonary health data that can be utilized for symptomatic purposes, and ongoing examinations in chaotic dynamics have shows a nonlinear phenomenon exists in vocal signs. Cough samples are transformed with Mel frequency Cepstral Coefficients (MFCC) and the cough audio data is fitted into a GridsearchCV model with KNN based classification algorithm. The number of training data for used for training our model is 217 and remaining 55 data were used for testing the model. The dataset contains the cough tests from both male and female. When evaluated the model could get a precision of 83.3%. © 2022 IEEE.
ABSTRACT
Background: The objective of this study was to find out the effect, course of disease and management of the COVID-19 infection in pregnant women and compare it with non-pregnant population. Method: This is a retrospective study done at Sardar Vallabhbhai Patel Institute of Medical Science and Research (SVPIMSR), Smt. NHL MMC, Ahmedabad, from April 1, 2020, to September 30, 2020. All COVID-19-positive patients were divided into two groups: group A was comprised of pregnant/postpartum and group B of non-pregnant patients. Result: A total of 709 women (Group A-205, Group B-504) were included in the study, In group A 90% of patients were asymptomatic, while in group B 48% asymptomatic patients. Group A had 5.9% and group B had 13.1% patients having comorbidities. In group A, smaller number of patients had raised inflammatory markers as compared to group B. Only 4% patients of group A showed significant changes on chest X-ray as compared to 16% in group B. Only 2.9% patients of group A required intensive care unit admission as compared to 10.31% patients of group B. Mean hospital stay of group A was 10.6 days, and that of group B was 12.1 days. Conclusion: In pregnancy, due to the physiological alterations in cardiovascular, respiratory and immune system, the pregnant women are vulnerable to infections. Although pregnancy is immunocompromised state, the severity of Coivd-19 disease is milder as compared to non-pregnant COVID-19-positive patients.
ABSTRACT
Covid-19 has once again brought into focus our limited preparedness to deal with epidemics. Most nations, across the globe, have responded with a resolve to come stronger out of this crisis and leaderships across the world have shown great commitment to protecting its people from Covid-19. Covid-19 has also taught us a few things for the future. One such learning has been that a strong shift in focus towards non-communicable diseases driving health infrastructure across the globe for the last few decades has come at neglect of communicable diseases. In that sense, therefore, the current pandemic has been a wake-up call. Organised Medicine Academic Guild (OMAG), an umbrella organization of professional associations gathered a group of health experts to develop a policy document on epidemic preparedness to limit the influence of epidemics like Covid-19.
ABSTRACT
BACKGROUND: In March 2020, a healthcare professional from a renowned private hospital, in the textile city of Bhilwara, Rajasthan, reported clustering of cases of pneumonia amongst doctors and paramedical staff suspected to be due to COVID-19. The basis of suspicion was clinico-eco-epidemiologic-radiological findings as, by that time, about 20 COVID19 cases were reported from the state of Rajasthan including a big Italian group of tourists who travelled extensively in Rajasthan, including Udaipur city. OBJECTIVES: The current study presents the field experience of the Central and the State Rapid Response Teams (RRTs) in the cluster containment at Bhilwara. Methods: The information regarding the sociodemographic profile of the cases was provided by the Senior Medical Officer In-charge. The containment strategy was modeled under 6 pillars. Google Maps was used for preparing spot map. RESULTS: Immediate public health actions of cluster containment including contact tracing, quarantine, and isolation were initiated using epidemiological approach of mapping the cluster and taking care of reservoir of infection by the District Public Health Team supported by Multidisciplinary Rapid Response Team. This was supplemented by strict enforcement of lock down in the District taking care of daily need of the community by the leadership of administration with very strong intersectoral co-ordination (locally called "ruthless containment"). CONCLUSION: The forthcoming challenge resides in re-establishment of inter-district and inter-state travel, which can become a risk of re-entry of the new cases, which needs to be taken care of, with the help of stringent administrative measures and screening at all points of entry. The team in Bhilwara needs to remain vigilant to pick up any imported cases early before local transmission establishes.