ArticleJournal of applied statistics2023
A latent space model and Hotelling's T
Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Editorial to the special issue: statistical perspectives on analytics for COVID-19 data.Journal of applied statistics · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the COVID-19 coronavirus pandemic, potential patients that suffer from different symptoms can be diagnosed with COVID-19. At the early stages of the pandemic, patients were mainly diagnosed with fever and respiratory symptoms. Recently, patients with new symptoms, such as gastrointestinal or loss of senses, are also diagnosed with COVID-19. Monitoring these symptoms can help the healthcare system to be aware of new symptoms that can be related to the COVID-19 coronavirus. This article focuses on monitoring the behavior of COVID-19 symptoms over time. In this regard, a Latent space model (LSM) and a Generalized linear model (GLM) are introduced to model the networks of symptoms. We apply Hotelling's T2 control chart to the estimated parameters of the LSM and GLM, to identify significant changes and detect anomalies in the networks. The performance of the proposed methods is evaluated using simulation and calculating average run length (ARL). Then, dynamic networks are generated from a COVID-19 epidemic survey dataset.
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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.