ArticleBMC public health2025
Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
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Who cites it
1 citing paper in PubMed.
- OSH inspector ratio to strengthen decent workplace safety and health: a cross-regional trend analysis research.Frontiers in public health · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundDisability Weights (DWs) are crucial for assessing disease burden guiding public health decision-making. For emerging health threats such as COVID-19, the absence of relevant survey data from China has led to reliance on established DW values for specific symptoms in calculating the COVID-19 disease burden. However, these values have not been updated in real-time to reflect the ongoing mutations of the virus, potentially skewing the longitudinal estimation of COVID-19's burden and compromising the accuracy of public health interventions.
methodsThis study developed a real-time estimation framework using longitudinal internet survey data to track changes in DW distributions across different populations over time. These distributions were integrated into Monte Carlo simulations to model real-time disease burden, offering robust data to support evidence-based policy decisions and optimize resource allocation.
resultsOur analysis revealed substantial variation in DW distributions across symptoms. As populations experience multiple infections and the virus evolves, the COVID-19 disease burden has converged with, and in some cases fallen below, that of influenza's. Survey data suggested an average immunity interval of approximately five months between infections. Moreover, COVID-19 has profoundly reshaped healthcare-seeking behavior and consumption patterns, with individual lifestyle factors and pre-existing health conditions contributing significantly to infection severity.
conclusionThe real-time DW estimation method proposed in this study effectively and accurately reflects the dynamic changes in the COVID-19 disease burden amidst ongoing virus mutations, providing crucial reference data for the evaluation and formulation of public health policies. Furthermore, the study provides insights into the transmission interval of COVID-19 and behavioral changes during the pandemic, offering valuable insights for the potential outbreak of future "Disease X."
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