ArticleImmunity, inflammation and disease2024
HIV deathrate prediction by Gaidai multivariate risks assessment method.
Article in Immunity, inflammation and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- HIV deathrate prediction by Gaidai multivariate risks assessment method.Immunity, inflammation and disease · 2024Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHIV is a contagious disease with reportedly high transmissibility, being spread worldwide, with certain mortality, allegedly presenting a burden to public health worldwide. The main objective of this study was to determine excessive HIV death risks at any time within any region or country of interest. STUDY
designCurrent study presents a novel multivariate public health system bio-risk assessment approach that is particularly applicable to environmental multi-regional, biological, and public health systems, being observed over a representative period of time, yielding reliable long-term HIV deathrate assessment. Hence, the development of a new bio-statistical approach, that is, population-based, multicenter, and medical survey-based. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. Firstly, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case, - not to mention challenges with system dimensionality higher than 2D.
methodsExisting bio-statistical methods that process spatiotemporal clinical observations of multinational bio-processes often do not have the advantage of efficiently dealing with high regional dimensionalities and complex nonlinear inter-correlations between different national raw datasets. Hence, this study advocates the direct application of the novel bio-statistical Gaidai method to a raw unfiltered clinical data set.
resultsThis investigation described the successful application of a novel bio-risk assessment approach, yielding reliable long-term HIV mortality risk assessments.
conclusionsThe suggested risk assessment methodology may be utilized in various public bio and public health clinical applications based on available raw patient survey datasets.
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