ArticlePloS one2025
Bayesian spatio-temporal disease mapping of COVID-19 cases in Bangladesh.
Article in PloS one, 2025. 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
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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, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 is a highly transmittable respiratory illness induced by SARS-CoV-2, a novel coronavirus. The spatio-temporal analysis considers interactions between space and time is essential for understanding the virus's transmission pattern and developing efficient mitigation strategies.
objectiveThis study explicitly examines how meteorological, demographic, and vaccination with all doses of risk factors are interrelated with COVID-19's complex evolution and dynamics in 64 Bangladeshi districts over space and time.
methodsThe study employed Bayesian spatio-temporal Poisson modeling to determine the most suitable model, including linear trend, analysis of variance (ANOVA), separable models, and Poisson temporal model for spatiotemporal effects. The study employed the Deviance Information Criterion (DIC) and Watanabe-Akaike information criterion (WAIC) for model selection. The Markov Chain Monte Carlo approach also provided information regarding both prior and posterior realizations.
resultsThe results of our study indicate that the spatio-temporal Poisson ANOVA model outperformed all other models when considering various criteria for model selection and validation. This finding underscores the significant relationship between spatial and temporal variations and the number of cases. Additionally, our analysis reveals that maximum temperature does not appear to have a significant association with infected cases. On the other hand, factors such as humidity (%), population density, urban population, aging index, literacy rate (%), households with internet users (%), and complete vaccination coverage all play vital roles in correlating with the number of affected cases in Bangladesh.
conclusionsThe research has demonstrated that demographic, meteorological, and vaccination variables possess significant potential to be associated with COVID-19-affected cases in Bangladesh. These data show that there are interconnections between space and time, which shows how important it is to use integrated modeling in pandemic management. An assessment of the risks particular to an area allows government agencies and communities to concentrate their efforts to mitigate those risks.
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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.