Evidence map›Paper›PMID 34108816›Full record

ArticleSafety science2021

COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness.

Costas A Varotsos, Vladimir F Krapivin, Yong Xue, Vladimir Soldatov, Tatiana Voronova

Abstract read
In one paragraph

Article in Safety science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Costas A VarotsosDepartment of Environmental Physics and Meteorology, National and Kapodistrian University of Athens, Panepistimioupolis, Bldg PHYS-V, GR-157 84 Athens, Greece.
Vladimir F KrapivinKotelnikov's Institute of Radioengineering and Electronics, Fryazino Branch, Russian Academy of Sciences, Vvedensky 1, Fryazino, Moscow Region 141190, Russian Federation.
Yong XueSchool of Environment Science and Spatial Informatics, University of Mining and Technology, Xuzhou, Jiangsu 221116, PR China.
Vladimir SoldatovKotelnikov's Institute of Radioengineering and Electronics, Fryazino Branch, Russian Academy of Sciences, Vvedensky 1, Fryazino, Moscow Region 141190, Russian Federation.
Tatiana VoronovaDepartment of Environmental Physics and Meteorology, National and Kapodistrian University of Athens, Panepistimioupolis, Bldg PHYS-V, GR-157 84 Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario.

Indexed as

Big dataCOVID-19Decision makingModelPrognosisSimulationTrendVaccination

Identifiers

PMID34108816
PMCPMC8179249

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.