ArticleHeliyon2023
Effectiveness analysis of multiple epidemic prevention measures in the context of COVID-19 using the SVIRD model and ensemble Kalman filter.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Compartmental modeling for pandemic data analysis: The gap between statistics and models.Heliyon · 2024Article
- Article
- Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia.PLOS global public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to accurately forecast the spread of coronavirus disease 2019 (COVID-19) is of great importance to the resumption of societal normality. Existing methods of epidemic forecasting often ignore the comprehensive analysis of multiple epidemic prevention measures. This paper aims to analyze various epidemic prevention measures through a compound framework. Here, a susceptible-vaccinated-infected-recovered-deceased (SVIRD) model is constructed to consider the effects of population mobility among origin and destination, vaccination, and positive retest populations. And we further use real-time observations to correct the model trajectory with the help of data assimilation. Seven prevention measures are used to analyze the short-term trend of active cases. The results of the synthetic scene recommended that four measures-improving the vaccination protection rate (IVPR), reducing the number of contacts per person per day (RNCP), selecting the region with less infected people as origin A (SES-O) and limiting population flow entering from A to B per day (LAIP-OD)-are the most effective in the short-term, with maximum reductions of 75%, 53%, 35% and 31%, respectively, in active cases after 150 days. The results of the real-world experiment with Hong Kong as the origin and Shenzhen as the destination indicate that when the daily vaccination rate increased from 5% to 9.5%, the number of active cases decreased by only 7.35%. The results demonstrate that reducing the number of contacts per person per day after productive life resumes is more effective than increasing vaccination rates.
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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.