ArticleConcurrency and computation : practice & experience2022
Evaluating COVID-19 risk under the estimation of population mean using two attributes.
Article in Concurrency and computation : practice & experience, 2022. 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.
- Article
- Evaluating COVID-19 risk under the estimation of population mean using two attributes.Concurrency and computation : practice & experience · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The virus of COVID-19 has affected humans physically, mentally, and economically all over the world. Each country's development level, resources, and immunization to have coping capacity against this covid19 differ country-wise. Thus, understanding socioeconomic vulnerability and coping capacity among countries under different health systems can be crucial. Contrasting most articles on COVID-19, this article focuses on evaluating and estimating the COVID-19 risk and lack of coping capacity in 190 countries. This present study suggests an exponential estimator using two auxiliary attributes. Theoretically, the mean square error expressions are derived and compared with some existing estimators. These findings were supported by using the real data of INFORM COVID-19 risk.
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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.