ArticleInformation sciences2021
Inferring statistical trends of the COVID19 pandemic from current data. Where probability meets fuzziness.
Article in Information sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed.
- Data quality model for assessing public COVID-19 big datasets.The Journal of supercomputing · 2023Article
- Multi-criteria decision making of COVID-19 vaccines (in India) based on ranking interpreter technique under single valued bipolar neutrosophic environment.Expert systems with applications · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: (1) a basic tool to deal with samples that are both truncated and non independently drawn, and (2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in an intermediate domain between probability models and fuzzy sets, still maintaining probabilistic features of the employed statistics as the reference KPI of the tools. This research uses as a benchmark the daily cumulative death numbers of COVID19 in two countries, with no any ancillary data. Numerical results show: (i) the model capability of capturing the inflection point and forecasting the end-of-infection time and related outbreak size, and (ii) the out-performance of the model inference method according to conventional indicators.
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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.