ArticleMethodsX2024
Analyzing COVID-19 outbreaks: A methodological approach with Gaussian pulse models.
Article in MethodsX, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This method introduces a methodological approach for comprehensively analyzing COVID-19 outbreaks using Gaussian pulse models to assess transmission rates. Our methodology is designed to provide an in-depth understanding of the intricate dynamics underlying the spread of COVID-19. By incorporating Gaussian pulse models into our approach, we capture temporal and spatial outbreaks' characteristics with high precision. This method provides a detailed overview of our methodological approach, underscoring its potential to revolutionize our comprehension of COVID-19 outbreak dynamics.•Our methodology involves the application of Gaussian pulse models to the transmission rate estimation.•Parameter estimation in epidemic modeling involves adjusting key factors like transmission rate, and effective reproduction number to best match observed data.•We use Microsoft Excel's Solver add-in with the GRG algorithm to find the best parameter values, improving the data fit.
Indexed as
Identifiers
What OpenQuestion holds
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.