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Predictors of COVID-19 Case Fatality Rate in Highly Populated Developed Countries During the Emergence of the Delta Variant
Issue:
Volume 6, Issue 4, December 2021
Pages:
72-75
Received:
20 September 2021
Accepted:
8 October 2021
Published:
15 October 2021
Abstract: By September 2021 the outbreak of the COVID-19 caused 228.19 million confirmed cases and 4.7 million deaths globally. Mortality measures are frequently used to estimate the severity of a pandemic. Among them is the Case Fatality Rate (CFR). Some mathematical models were developed to estimate the impact of specific factors on the disease’s mortality. These models were developed before the COVID-19 vaccines were administrated, and therefore did not consider the vaccines influence on COVID-19 fatality. Moreover, some other factors associated with COVID-19 mortality such as diabetes and cardiovascular mortality were not included in these models. This study offers a mathematical model with some potential predictors of COVID-19 CFR during the fourth pandemic wave caused by the Delta variant. To evaluate these predictors, demographic and clinical information for 10 highly populated developed countries was retrieved from a real-time available website. Demographic data included population density, percent of population above age 65, GDP per capita, and percent of smoking. Clinical data included diabetes prevalence, cardiovascular death rate, percent of fully vaccinated population, and CFR. Single linear regressions were conducted to assess the association of each potential predictor with CFR. A backward multiple linear regression was conducted to identify the most parsimonious combination of the independent variables of this study predicting CFR. The model developed in this study suggests that percent of population above age 65, and cardiovascular death rate have a positive effect on CFR, i.e., they are associated with increased COVID-19 fatality rate during the fourth wave. In addition, GDP per capita has a negative effect on CFR, i.e. – higher GDP per capita is associated with lower fatality rate during COVID-19 fourth wave. Moreover, single linear regressions show a strong negative association between percent of fully vaccinated people in each country and CFR. This model sheds light on several potential demographic and clinical factors which may predict CFR in highly populated developed countries during the emergence of the Delta variant. Vaccination in accordance with the recommendations is recommended to reduce COVID-19 mortality.
Abstract: By September 2021 the outbreak of the COVID-19 caused 228.19 million confirmed cases and 4.7 million deaths globally. Mortality measures are frequently used to estimate the severity of a pandemic. Among them is the Case Fatality Rate (CFR). Some mathematical models were developed to estimate the impact of specific factors on the disease’s mortality...
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Factors Affecting Anti-retroviral Therapy Adherence Among HIV Positive Children Attending Wollega University Referral Hospital, ART, Nekemte, West Ethiopia, 2019
Edosa Amente Gutema,
Gemechu Kejela,
Zelalem Keba Babure
Issue:
Volume 6, Issue 4, December 2021
Pages:
76-83
Received:
8 November 2021
Accepted:
15 December 2021
Published:
24 December 2021
Abstract: Since the introduction of Antiretroviral Treatments, morbidity and mortality due to HIV/AIDS have been significantly reduced. Through successful prevention of mother-to-child transmission programs, developed countries face few new cases of infant HIV infection annually; however, as a result of successful ART use, children are surviving into adolescence and struggling with many adherence challenges associated with long-term therapy. This study aims to assess factors affecting child antiretroviral treatment adherence at WollegaUniversity Medical Center anti-retroviral therapy clinic. To assess factors that affect child ART adherence among HIV positive children attending Wollega University Referral hospital ART clinic, Nekemte, West Ethiopia, 2019. Cross sectional study design was conducted from March to May/2019 among HIV positive children on ART who have follow-up at Wollega University medical anti-retroviral therapy clinic. Data was collected by interviewing of the care givers of the child using a structured questionnaire. The collected data wascleaned, coded, and analyzed by manual and calculator, and the results found wascompared with findings in the area and aboard, then appropriate conclusions and recommendations wasgiven. Among the 80 study participants, 30 (37.5%) took medications other than ARD. Out of this, 20 (25%) of them took one other tablet per day and the rest were taking two to four other tablets per day. The study showed that the majority (96.3%) of the children had a near perfect (>95%) adherence rate. There were limited researches done in the study area on adherence rate and no research was found describing the national adherence rate.
Abstract: Since the introduction of Antiretroviral Treatments, morbidity and mortality due to HIV/AIDS have been significantly reduced. Through successful prevention of mother-to-child transmission programs, developed countries face few new cases of infant HIV infection annually; however, as a result of successful ART use, children are surviving into adolesc...
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Determining Disease Using Machine Learning Algorithm in Medical Image Processing: A Gentle Review
Issue:
Volume 6, Issue 4, December 2021
Pages:
84-88
Received:
15 March 2021
Accepted:
16 December 2021
Published:
29 December 2021
Abstract: Machine learning plays a very vital role in computer science. It is a part of artificial intelligence, which provides many advantages like automated cars, speech recognition, medical fields, efficient web search, etc. Machine learning algorithms are used in analysis of digital images like X-Ray, Ultrasound, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) for finding diseases. There are several diseases like brain tumor, Diabetes, liver cancer, heart disease etc. use medical modalities like MRI Image, CT scan image. Basically these images use by many research for analysis and characterization, which is useful for doctor to detect cancer or specific disease in early stage then necessary action take place on right time and also take very less cost for patients. Medical Images uses Machine Learning (ML) algorithms to develop predictive model, which plays a very important role for detection of different diseases such as heart attack, diabetes, liver, dengue and skin diseases. This review paper gives attention towards analysis and detection of diseases using machine learning algorithms in medical image processing. The paper focus on supervised learning algorithm applies on medical image for detection of particular disease. The best result can be found by applying deep learning or convolutional neural network (CNN) on medical images.
Abstract: Machine learning plays a very vital role in computer science. It is a part of artificial intelligence, which provides many advantages like automated cars, speech recognition, medical fields, efficient web search, etc. Machine learning algorithms are used in analysis of digital images like X-Ray, Ultrasound, Computed Tomography (CT), and Magnetic Re...
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