COVID-19 as the disease of concern motivates various scientists to investigate it in various perspectives. In statistical perspective, a number of statistical models are used to predict the outcome of COVID-19 cases given a number of risk factors. Accuracy of a statistical model in predicting the outcome is important to be determined. A part of supervised machine learning called deep learning is used to predict the outcome of COVID-19 given five predictors, new cases, age >= 65 years, prevalence of diabetes mellitus, female smoker, and male smoker. Big data of COVID-19 is downloaded from the website. A thousand data sets have been analyzed by neural network algorithm using library Keras.
{"title":"ACCURACY OF NEURAL NETWORK MODEL IN PREDICTING OUTCOME OF COVID 19 USING DEEP LEARNING APPROACH","authors":"K. Kuntoro","doi":"10.17654/0973514322008","DOIUrl":"https://doi.org/10.17654/0973514322008","url":null,"abstract":"COVID-19 as the disease of concern motivates various scientists to investigate it in various perspectives. In statistical perspective, a number of statistical models are used to predict the outcome of COVID-19 cases given a number of risk factors. Accuracy of a statistical model in predicting the outcome is important to be determined. A part of supervised machine learning called deep learning is used to predict the outcome of COVID-19 given five predictors, new cases, age >= 65 years, prevalence of diabetes mellitus, female smoker, and male smoker. Big data of COVID-19 is downloaded from the website. A thousand data sets have been analyzed by neural network algorithm using library Keras.","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43656874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SURVIVAL ANALYSIS OF BREAST CANCER PATIENTS OF NORTH-EAST INDIA DURING 2016-2019","authors":"S. Bhattacharjee, S. Deka","doi":"10.17654/0973514322001","DOIUrl":"https://doi.org/10.17654/0973514322001","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45342544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ESTIMATION OF PROBABILITY DENSITY FUNCTION AND INTENSITY FUNCTION OF THE SURVIVAL OF STOMACH CANCER PATIENTS USING REAL POLYNOMIALS","authors":"K. Ratheesan","doi":"10.17654/0973514322010","DOIUrl":"https://doi.org/10.17654/0973514322010","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48835236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"APPLICATION OF MULTISTATE MODEL IN ANALYZING HEAD AND NECK CANCER DATA","authors":"T. Bindu","doi":"10.17654/0973514322003","DOIUrl":"https://doi.org/10.17654/0973514322003","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48683003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"JOB SATISFACTION AND ORGANIZATIONAL COMMITMENT OF DOCTORS: A CASE STUDY OF SAUDI ARABIA","authors":"A. Almarashi, K. Khan","doi":"10.17654/0973514322002","DOIUrl":"https://doi.org/10.17654/0973514322002","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48073017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Jannet Vennila, P. Basker, K. Thenmozhi, P. Nithyakala
{"title":"ANALYZING THE IMPACT OF INFLAMMATORY BOWEL DISEASE (IBD) BY USING R-PROGRAMMING","authors":"J. Jannet Vennila, P. Basker, K. Thenmozhi, P. Nithyakala","doi":"10.17654/0973514322009","DOIUrl":"https://doi.org/10.17654/0973514322009","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41981176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose of this paper: In this article we have analyzed the relationship among clinical variables: age, sex, pathological history of interest of the deceased;medicolegal: initial cause of death, immediate cause of death, origin of death and medicolegal etiology of death, and histological or anatomopathological: anthracosis, arteriosclerosis, congestion, fatty degeneration, edema, emphysema, sclerosis, hemorrhage, inflammation, necrosis and other casual or incidental findings from the study of medicolegal autopsies chosen at random from no Covid-19 victims in pandemic times. Design/methodology/approach: For the analysis of the relationships among the different variables, parametric and non-parametric techniques have been used: t-Student, ANOVA, contingency coefficient and Kruskal-Wallis. Findings: The relationship among these variables has been significant (p-value <= 0.05): Sex-age (0.005), Sex-pathological history (0.000), Sex-immediate cause of death (0.037), Pathological history-initial cause of death (0.036), Pathological history-medicolegal etiology (0.043), Initial cause of death-immediate cause of death (0.000) and Initial cause of death-origin of death (0.000), Immediate cause of death-origin of death (0.000). Research limitations/implications: We intend to expand the study in the future.
{"title":"STATISTICAL STUDY OF MEDICOLEGAL AUTOPSIAS","authors":"E. M. Pérez","doi":"10.17654/0973514322006","DOIUrl":"https://doi.org/10.17654/0973514322006","url":null,"abstract":"Purpose of this paper: In this article we have analyzed the relationship among clinical variables: age, sex, pathological history of interest of the deceased;medicolegal: initial cause of death, immediate cause of death, origin of death and medicolegal etiology of death, and histological or anatomopathological: anthracosis, arteriosclerosis, congestion, fatty degeneration, edema, emphysema, sclerosis, hemorrhage, inflammation, necrosis and other casual or incidental findings from the study of medicolegal autopsies chosen at random from no Covid-19 victims in pandemic times. Design/methodology/approach: For the analysis of the relationships among the different variables, parametric and non-parametric techniques have been used: t-Student, ANOVA, contingency coefficient and Kruskal-Wallis. Findings: The relationship among these variables has been significant (p-value <= 0.05): Sex-age (0.005), Sex-pathological history (0.000), Sex-immediate cause of death (0.037), Pathological history-initial cause of death (0.036), Pathological history-medicolegal etiology (0.043), Initial cause of death-immediate cause of death (0.000) and Initial cause of death-origin of death (0.000), Immediate cause of death-origin of death (0.000). Research limitations/implications: We intend to expand the study in the future.","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43855101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the mean basic reproduction number (R-0 = 4.26) for SARS-CoV-2, the global and individual values of the effective reproduction number (R) were determined for the ten leading countries in terms of vaccination against the novel coronavirus infection as of December 16-21, 2021. The calculation of R made it possible to distinguish two clusters of countries for this indicator: (i) in the world as a whole, as well as seven countries (India, USA, Indonesia, Germany, Mexico, Russia, Pakistan) had R > 1, which does not allow us to discuss about the development of herd immunity;(ii) three countries (China, Brazil, Japan) have reached R < 1, which allows us to discuss about the prerequisites for the development of herd immunity. The relationship between the effective reproduction number and the share of population vaccinated is discussed. The herd immunity threshold to SARS-CoV-2 is determined at the level of 76.5%, which is achieved at R-0 = 4.26 and R = 1.
{"title":"EFFECTIVE REPRODUCTION NUMBER AS INDICATOR OF HERD IMMUNITY TO SARS-CoV-2","authors":"A. B. Kiladze","doi":"10.17654/0973514322007","DOIUrl":"https://doi.org/10.17654/0973514322007","url":null,"abstract":"Based on the mean basic reproduction number (R-0 = 4.26) for SARS-CoV-2, the global and individual values of the effective reproduction number (R) were determined for the ten leading countries in terms of vaccination against the novel coronavirus infection as of December 16-21, 2021. The calculation of R made it possible to distinguish two clusters of countries for this indicator: (i) in the world as a whole, as well as seven countries (India, USA, Indonesia, Germany, Mexico, Russia, Pakistan) had R > 1, which does not allow us to discuss about the development of herd immunity;(ii) three countries (China, Brazil, Japan) have reached R < 1, which allows us to discuss about the prerequisites for the development of herd immunity. The relationship between the effective reproduction number and the share of population vaccinated is discussed. The herd immunity threshold to SARS-CoV-2 is determined at the level of 76.5%, which is achieved at R-0 = 4.26 and R = 1.","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43141757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"BIOINFORMATICS OF SEXUAL DIMORPHISM OF HISTOENZYMATIC ACTIVITY IN THE SKIN GLANDS OF LABORATORY RATS","authors":"A. B. Kiladze, N. K. Dzhemukhadze","doi":"10.17654/bs018030429","DOIUrl":"https://doi.org/10.17654/bs018030429","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42184454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"NON-PARAMETRIC STATISTICAL INFERENCE FOR THE SURVIVAL EXPERIMENTS","authors":"M. Ramadurai, M. A. Basha","doi":"10.17654/bs018030379","DOIUrl":"https://doi.org/10.17654/bs018030379","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67835621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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