PloS one · 2025
Modelling the impact of behavioural interventions during pandemics: A systematic review.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Gebretekle Tsega Kahsay, Albers Casper
Аннотация
BACKGROUND: Many 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.
METHODS: We 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.
RESULTS: Primary 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.
CONCLUSIONS: This 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.
METHODS: We 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.
RESULTS: Primary 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.
CONCLUSIONS: This 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.