ArticleStatistics in medicine2021
A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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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
7 citing papers in PubMed.
- Explicit modeling of antibody levels for infectious disease simulations in the context of SARS-CoV-2.iScience · 2023Article
- The coming Omicron waves and factors affecting its spread after China reopening borders.BMC medical informatics and decision making · 2023Article
- Modelling the COVID-19 epidemic and the vaccination campaign in Italy by the SUIHTER model.Infectious Disease Modelling · 2022Article
- Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends.Journal of clinical medicine · 2022Article
- A general framework for quantifying uncertainty at scale.Communications engineering · 2022Article
- A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification.Statistics in medicine · 2021Article
- Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.International journal of forecastingArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
For the analysis of COVID-19 pandemic data, we propose Bayesian multinomial and Dirichlet-multinomial autoregressive models for time-series of counts of patients in mutually exclusive and exhaustive observational categories, defined according to the severity of the patient status and the required treatment. Categories include hospitalized in regular wards (H) and in intensive care units (ICU), together with deceased (D) and recovered (R). These models explicitly formulate assumptions on the transition probabilities between these categories across time, thanks to a flexible formulation based on parameters that a priori follow normal distributions, possibly truncated to incorporate specific hypotheses having an epidemiological interpretation. The posterior distribution of model parameters and the transition matrices are estimated by a Markov chain Monte Carlo algorithm that also provides predictions and allows us to compute the reproduction number
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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.