Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics
Abstract Background Disability 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...
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2025-06-01
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| Online Access: | https://doi.org/10.1186/s12889-025-23273-3 |
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| author | Wenxiu Chen Wei An Qun Gao Ji Bai Hua Li Song Tang Wenhui Gao Zhe Tian Yu Zhang Min Yang |
| author_facet | Wenxiu Chen Wei An Qun Gao Ji Bai Hua Li Song Tang Wenhui Gao Zhe Tian Yu Zhang Min Yang |
| author_sort | Wenxiu Chen |
| collection | DOAJ |
| description | Abstract Background Disability 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. Methods This 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. Results Our 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. Conclusion The 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." |
| format | Article |
| id | doaj-art-b8abf4de724147e58e641ed8eb33f1da |
| institution | OA Journals |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj-art-b8abf4de724147e58e641ed8eb33f1da2025-08-20T02:05:42ZengBMCBMC Public Health1471-24582025-06-0125111210.1186/s12889-025-23273-3Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristicsWenxiu Chen0Wei An1Qun Gao2Ji Bai3Hua Li4Song Tang5Wenhui Gao6Zhe Tian7Yu Zhang8Min Yang9National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of SciencesNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of SciencesBeijing Center for Disease Prevention and ControlNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of SciencesNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of SciencesNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and PreventionChaoyang District Center for Disease Prevention and Control of BeijingNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of SciencesNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of SciencesNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of SciencesAbstract Background Disability 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. Methods This 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. Results Our 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. Conclusion The 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."https://doi.org/10.1186/s12889-025-23273-3Disability WeightMonte CarloCOVID-19Emerging DiseaseTransmission Characteristics |
| spellingShingle | Wenxiu Chen Wei An Qun Gao Ji Bai Hua Li Song Tang Wenhui Gao Zhe Tian Yu Zhang Min Yang Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics BMC Public Health Disability Weight Monte Carlo COVID-19 Emerging Disease Transmission Characteristics |
| title | Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics |
| title_full | Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics |
| title_fullStr | Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics |
| title_full_unstemmed | Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics |
| title_short | Calculation of COVID-19 disease burden using Monte Carlo simulation with dynamic disability weights and analysis of transmission characteristics |
| title_sort | calculation of covid 19 disease burden using monte carlo simulation with dynamic disability weights and analysis of transmission characteristics |
| topic | Disability Weight Monte Carlo COVID-19 Emerging Disease Transmission Characteristics |
| url | https://doi.org/10.1186/s12889-025-23273-3 |
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