Cure Fraction Model for the Estimation of Long-term Survivors of HIV/AIDS Patients under Antiretroviral Therapy

The primary aim of this research is to estimate the proportion of long-term survivors among HIV/AIDS patients receiving Antiretroviral therapy (ART). A cure fraction model has been used to accomplish the same. Although, cure fraction models are extensively being used in oncology for modeling survival time data with long-term survivors, but there are minimal research that considers this model in HIV/AIDS set up. Here, we have defined survival time under the purview of CD4 cell counts. CD4 is considered to be disease marker for HIV/AIDS patients. Bayesian Analysis of the various mixture and non-mixture cure fraction models under exponential, generalized exponential. Raleigh, Weibull, Exponentiated Weibull distributions are exemplified using a real data set. Effect of prognostic factors like baseline CD4, age, sex, medication on cure fraction are studied. The MLE’s are obtained using Gibbs sampling techniques with MCMC method in Open BUGS package. Convergence diagnostic (like trace plots, density plots & MC errors) are used to detect in any unexpected anomalies in MCMC output. The DIC (Deviance information criterion) has been utilized to compare the efficiency of different models. A real-life data set from the ART center of RML hospital, Delhi, India are taken for this study

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-

Abstracts of 1st International Conference on Machine Intelligence and System Sciences

This book contains the abstracts of the papers presented at the International Conference on Machine Intelligence and System Sciences (MISS-2021) Organized by the Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea, held on 1–2 November 2021. This conference was intended to enable researchers to build connections between different digital technologies based on Machine Intelligence, Image Processing, and the Internet of Things (IoT). Conference Title: 1st International Conference on Machine Intelligence and System SciencesConference Acronym: MISS-2021Conference Date: 1–2 November 2021Conference Location: Techno College of Engineering Agartala, Tripura(w), IndiaConference Organizer: Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea