SynthesisPloS one2025
Modelling the impact of behavioural interventions during pandemics: A systematic review.
Synthesis in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
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
backgroundMany studies examined the impact of behavioural interventions on COVID-19 outcomes. We conducted a systematic review to gain insight into transmission models, following PRISMA 2020 guidelines. We included peer-reviewed studies published in English until December 31, 2022, focusing on human subjects, modelling, and examining behavioural interventions during COVID-19 using real data across diverse geographical regions.
methodsWe searched seven databases. We used descriptive analysis, network analysis for textual synthesis, and regression analysis to identify the relationship between the basic reproduction number R0 and various characteristics. From 30, 114 articles gathered, 15, 781 met the inclusion criteria. After deduplication, 7, 616 articles remained. The titles and abstracts screening reduced these to 1, 764 articles. Full-text screening reduced this to 270, and risk-of-bias assessment narrowed it to 245 articles. We employed combined criteria for risk of bias assessment, incorporating domains from ROBINS-I and principles for modeling.
resultsPrimary outcomes focused on R0, COVID-19 cases, and transmission rates. The average R0 was 3.184. The vast majority of studies (90.3%) used compartmental models, particularly SEIR models. Social distancing, mask-wearing, and lockdowns were frequently analyzed interventions. Early and strict implementation of these interventions significantly reduced transmission rates. Risk of bias assessment revealed that 62.6% of studies were of low risk, 24.1% moderate, and 9.3% high risks. Common issues included transparency, attrition bias, and confounding factors.
conclusionsThis comprehensive review highlights the importance of behavioural interventions in reducing COVID-19 transmission and areas for improving future research transparency and robustness. Our risk of bias criteria offers an important framework for future systematic reviews in modeling studies of interventions. We recommend that future studies enhance transparency in reporting and address common biases such as attrition and confounding.
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Registered trials
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