ABSTRACT
SARS-CoV-2 appeared in December 2019 in Wuhan, China. The Guinean Government has taken important measures since the notification of the first case on March 12, 2020, in particular the follow-up of the recovered. The objective of this study was to describe the health and socio-economic problems faced by those who recovered from COVID-19 in Guinea. This was a descriptive cross-sectional study by simple random sampling in the five communes of Conakry and the regions of Kindia, Labe, and Kankan. Up to 330 COVID-19 survivors responded to the survey, 99% of whom were from the urban area. The male gender represented 70.3%, and the 19-38 age group was the most represented (61.82%). Pupils, students/ teachers, health personnel, and academics respectively represented 10.91%, 17.58%, and 62.73%. In this study, 70% were married against 28.18% single, and 8.79% moved after leaving the CTEPI. There is a statically significant link between stigma and job loss with a p-value of 0.002. Stigma was strongly associated with community residence, change in income, and post Covid-19 stress (P <0.05). The cured people who live in the communes of Ratoma, Matam, and Matoto are more in the process of being stigmatized than the others, with respectively 27.6%, 23.4%, and 19.1% (p = 0.001). These results show the need to support COVID-19 survivors from health, psychological and socio-economic perspectives. Copyright © 2022, Institute of Medico-legal Publication. All rights reserved.
ABSTRACT
Most evolutionary-oriented deep generative models do not explicitly consider the underlying evolutionary dynamics of biological sequences as it is performed within the Bayesian phylogenetic inference framework. In this study, we propose a method for a deep variational Bayesian generative model (EvoVGM) that jointly approximates the true posterior of local evolutionary parameters and generates sequence alignments. Moreover, it is instantiated and tuned for continuous-Time Markov chain substitution models such as JC69, K80 and GTR. We train the model via a low-variance stochastic estimator and a gradient ascent algorithm. Here, we analyze the consistency and effectiveness of EvoVGM on synthetic sequence alignments simulated with several evolutionary scenarios and different sizes. Finally, we highlight the robustness of a fine-Tuned EvoVGM model using a sequence alignment of gene S of coronaviruses. © 2022 Owner/Author.