ArticleDialogues in health2023
Influenza-type epidemic risks by spatio-temporal Gaidai-Yakimov method.
Article in Dialogues in health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- HIV deathrate prediction by Gaidai multivariate risks assessment method.Immunity, inflammation and disease · 2024Article
- Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method.Analytical science advances · 2024Article
- Gaidai reliability method for long-term coronavirus modelling.F1000Research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Global public health was recently hampered by reported widespread spread of new coronavirus illness, although morbidity and fatality rates were low. Future coronavirus infection rates may be accurately predicted over a long-time horizon, using novel bio-reliability approach, being especially well suitable for environmental multi-regional health and biological systems. The high regional dimensionality along with cross-correlations between various regional datasets being challenging for conventional statistical tools to manage. Methods: To assess future risks of epidemiological outbreak in any province of interest, novel spatio-temporal technique has been proposed. In a multicenter, population-based environment, assess raw clinical data using state-of-the-art, cutting-edge statistical methodologies. Results: Authors have developed novel reliable long-term risk assessment methodology for future coronavirus infection outbreaks. Conclusions: Based on national clinical patient monitoring raw dataset, it is concluded that although underlying data set data quality is questionable, the proposed method may be still applied.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.