Infectious Disease Modelling最新文献

{"title":"State-space modelling for infectious disease surveillance data: Dynamic regression and covariance analysis","authors":"Christopher D. Prashad","doi":"10.1016/j.idm.2024.12.005","DOIUrl":"10.1016/j.idm.2024.12.005","url":null,"abstract":"<div><div>We analyze COVID-19 surveillance data from Ontario, Canada, using state-space modelling techniques to address key challenges in understanding disease transmission dynamics. The study applies component linear Gaussian state-space models to capture periodicity, trends, and random fluctuations in case counts. We explore the relationships between COVID-19 cases, hospitalizations, workdays, and wastewater viral loads through dynamic regression models, offering insights into how these factors influence public health outcomes. Our analysis extends to multivariate covariance estimation, utilizing a novel methodology to provide time-varying correlation estimates that account for non-stationary data. Results demonstrate the significance of incorporating environmental covariates, such as wastewater data, in improving model robustness and uncovering the complex interplay between epidemiological factors. This work highlights the limitations of simpler models and emphasizes the advantages of state-space approaches for analyzing dynamic infectious disease data. By illustrating the application of advanced modelling techniques, this study contributes to a deeper understanding of disease transmission and informs public health interventions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 591-627"},"PeriodicalIF":8.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social contact patterns and their impact on the transmission of respiratory pathogens in rural China","authors":"Yuxia Liang ,&nbsp;Qian You ,&nbsp;Qianli Wang ,&nbsp;Xiaohong Yang ,&nbsp;Guangjie Zhong ,&nbsp;Kaige Dong ,&nbsp;Zeyao Zhao ,&nbsp;Nuolan Liu ,&nbsp;Xuemei Yan ,&nbsp;Wanying Lu ,&nbsp;Cheng Peng ,&nbsp;Jiaxin Zhou ,&nbsp;Jiqun Lin ,&nbsp;Maria Litvinova ,&nbsp;Mark Jit ,&nbsp;Marco Ajelli ,&nbsp;Hongjie Yu ,&nbsp;Juanjuan Zhang","doi":"10.1016/j.idm.2024.12.006","DOIUrl":"10.1016/j.idm.2024.12.006","url":null,"abstract":"<div><h3>Introduction</h3><div>Social contact patterns significantly influence the transmission dynamics of respiratory pathogens. Previous surveys have quantified human social contact patterns, yielding heterogeneous results across different locations. However, significant gaps remain in understanding social contact patterns in rural areas of China.</div></div><div><h3>Methods</h3><div>We conducted a pioneering study to quantify social contact patterns in Anhua County, Hunan Province, China, from June to October 2021, when there were minimal coronavirus disease-related restrictions in the area. Additionally, we simulated the epidemics under different assumptions regarding the relative transmission risks of various contact types (e.g., indoor versus outdoor, and physical versus non-physical).</div></div><div><h3>Results</h3><div>Participants reported an average of 12.0 contacts per day (95% confidence interval: 11.3–12.6), with a significantly higher number of indoor contacts compared to outdoor contacts. The number of contacts was associated with various socio-demographic characteristics, including age, education level, income, household size, and travel patterns. Contact patterns were assortative by age and varied based on the type of contact (e.g., physical versus non-physical). The reproduction number, daily incidence, and infection attack rate of simulated epidemics were remarkably stable.</div></div><div><h3>Discussion</h3><div>We found many intergenerational households and contacts that pose challenges in preventing and controlling infections among the elderly in rural China. Our study also underscores the importance of integrating various types of contact pattern data into epidemiological models and provides guidance to public health authorities and other major stakeholders in preparing and responding to infectious disease threats in rural China.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 439-452"},"PeriodicalIF":8.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics and asymptotic profiles of a local-nonlocal dispersal SIR epidemic model with spatial heterogeneity","authors":"Salih Djilali ,&nbsp;Ghilmana Sarmad ,&nbsp;Abdessamad Tridane","doi":"10.1016/j.idm.2024.12.003","DOIUrl":"10.1016/j.idm.2024.12.003","url":null,"abstract":"<div><div>This research investigates a novel approach to modeling an SIR epidemic in a heterogeneous environment by imposing certain restrictions on population mobility. Our study reveals the influence of partially restricting the mobility of the infected population, who are allowed to diffuse locally and can be modeled using random dispersion. In contrast, the non-infective population, which includes susceptible and recovered individuals, has more freedom in their movements. This greater mobility can be modeled using nonlocal dispersion. Our approach is valid for a class of nonlocal dispersion kernels. For the analysis, we first establish the well-posedness of the solution, ensuring the existence, uniqueness, and positivity of this solution. Additionally, we identify the basic reproduction number R₀ with its threshold role. Specifically, when R₀ &lt; 1, we prove the global asymptotic stability of the disease-free steady state. Conversely, when R₀ &gt; 1, we demonstrate the corresponding semiflow of the model is uniformly persistent and establish behavior at endemic steady state. Lastly, we examine the asymptotic profiles of the positive steady state as the rate at which susceptible or infected individuals disperse tends to zero or infinity. Our findings reveal that when the movement of infected individuals is restricted, the infection concentrates in specific locations that may be described as the infected preferred spots.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 387-409"},"PeriodicalIF":8.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lockdown policy in pandemics: Enforcement, adherence, and effectiveness in the case of COVID-19","authors":"Yu Shi ,&nbsp;Canyao Liu ,&nbsp;Linjia Wu ,&nbsp;Han Wu ,&nbsp;Kevin Han ,&nbsp;Daming Li ,&nbsp;Sheridan B. Green ,&nbsp;Kunal Sangani","doi":"10.1016/j.idm.2024.11.002","DOIUrl":"10.1016/j.idm.2024.11.002","url":null,"abstract":"","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 493-504"},"PeriodicalIF":8.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the effectiveness of vaccination campaigns: Insights from unvaccinated mortality data","authors":"Lixin Lin ,&nbsp;Haydar Demirhan ,&nbsp;Lewi Stone","doi":"10.1016/j.idm.2024.12.004","DOIUrl":"10.1016/j.idm.2024.12.004","url":null,"abstract":"<div><div>This paper examines a recently developed statistical approach for evaluating the effectiveness of vaccination campaigns in terms of deaths averted. The statistical approach makes predictions by comparing death rates in the vaccinated and unvaccinated populations. The statistical approach is preferred for its simplicity and straightforwardness, especially when compared to the difficulties involved when fitting the many parameters of a dynamic SIRD-type model, which may even be an impossible task.</div><div>We compared the estimated number of deaths averted by the statistical approach to the “ground truth” number of deaths averted in a relatively simple scheme (e.g., constant vaccination, constant <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span>, pure SIR dynamics, no age stratification) through mathematical analysis, and quantified the difference and degree of underestimation. The results indicate that the statistical approach consistently produces conservative estimates and will always underestimate the number of deaths averted by the direct effect of vaccination, and thus obviously the combined total effect (direct and indirect effect).</div><div>For high <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values (e.g. <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub><mo>≥</mo></mrow></math></span> 8), the underestimation is relatively small as long as the vaccination level (<span><math><mrow><mi>v</mi></mrow></math></span>) remains below the herd immunity vaccination threshold. However, for low <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values (e.g. <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub><mo>≤</mo></mrow></math></span> 1.5), the statistical approach significantly underestimates the number of deaths averted by vaccination, with the underestimation greater than 20%. Applying an approximate correction to the statistical approach, however, can improve the accuracy of estimates for low <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> and low <span><math><mrow><mi>v</mi></mrow></math></span>.</div><div>In conclusion, the statistical approach can provide reasonable estimates in scenarios involving high <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values and low <span><math><mrow><mi>v</mi></mrow></math></span>, such as during the Omicron variant epidemic in Australia. For low <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values and low <span><math><mrow><mi>v</mi></mrow></math></span>, applying an approximate correction to the statistical approach can lead to more accurate estimates, although there are caveats even for this. These results suggest that the statistical method needs to be used with caution.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 365-373"},"PeriodicalIF":8.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11719296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning model meets community-based surveillance of acute flaccid paralysis","authors":"Gelan Ayana ,&nbsp;Kokeb Dese ,&nbsp;Hundessa Daba Nemomssa ,&nbsp;Hamdia Murad ,&nbsp;Efrem Wakjira ,&nbsp;Gashaw Demlew ,&nbsp;Dessalew Yohannes ,&nbsp;Ketema Lemma Abdi ,&nbsp;Elbetel Taye ,&nbsp;Filimona Bisrat ,&nbsp;Tenager Tadesse ,&nbsp;Legesse Kidanne ,&nbsp;Se-woon Choe ,&nbsp;Netsanet Workneh Gidi ,&nbsp;Bontu Habtamu ,&nbsp;Jude Kong","doi":"10.1016/j.idm.2024.12.002","DOIUrl":"10.1016/j.idm.2024.12.002","url":null,"abstract":"<div><div>Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist. This work proposes a simple deep learning model for AFP surveillance, leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones. The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset. The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch, achieving superior accuracy, F1-score, precision, recall, and area under the receiver operating characteristic curve (AUC). It emerged as the optimal model, demonstrating the highest average AUC of 0.870 ± 0.01. Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches (<em>P</em> &lt; 0.001). By bridging community reporting with health system response, this study offers a scalable solution for enhancing AFP surveillance in low-resource settings. The study is limited in terms of the quality of image data collected, necessitating future work on improving data quality. The establishment of a dedicated platform that facilitates data storage, analysis, and future learning can strengthen data quality. Nonetheless, this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images, with substantial implications for addressing global health challenges and disease eradication strategies.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 353-364"},"PeriodicalIF":8.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic","authors":"Wei-Hua Hu ,&nbsp;Hui-Min Sun ,&nbsp;Yong-Yue Wei ,&nbsp;Yuan-Tao Hao","doi":"10.1016/j.idm.2024.12.001","DOIUrl":"10.1016/j.idm.2024.12.001","url":null,"abstract":"<div><div>An early warning model for infectious diseases is a crucial tool for timely monitoring, prevention, and control of disease outbreaks. The integration of diverse multi-source data using big data and artificial intelligence techniques has emerged as a key approach in advancing these early warning models. This paper presents a comprehensive review of widely utilized early warning models for infectious diseases around the globe. Unlike previous review studies, this review encompasses newly developed approaches such as the combined model and Hawkes model after the COVID-19 pandemic, providing a thorough evaluation of their current application status and development prospects for the first time. These models not only rely on conventional surveillance data but also incorporate information from various sources. We aim to provide valuable insights for enhancing global infectious disease surveillance and early warning systems, as well as informing future research in this field, by summarizing the underlying modeling concepts, algorithms, and application scenarios of each model.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 410-422"},"PeriodicalIF":8.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11731462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative assessment of airborne infection risk tools in enclosed spaces: Implications for disease control","authors":"Amar Aganovic ,&nbsp;Giorgio Buonanno ,&nbsp;Guangyu Cao ,&nbsp;Christian Delmaar ,&nbsp;Jarek Kurnitski ,&nbsp;Alex Mikszewski ,&nbsp;Lidia Morawska ,&nbsp;Lucie C. Vermeulen ,&nbsp;Pawel Wargocki","doi":"10.1016/j.idm.2024.11.003","DOIUrl":"10.1016/j.idm.2024.11.003","url":null,"abstract":"<div><div>The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the importance of understanding transmission modes and implementing effective mitigation strategies. Recognizing airborne transmission as a primary route has reshaped public health measures, emphasizing the need to optimize indoor environments to reduce risks. Numerous tools have emerged to assess airborne infection risks in enclosed spaces, providing valuable resources for public health authorities, researchers, and the general public.</div><div>However, comparing the outputs of these tools is challenging because of variations in assumptions, mathematical models, and data sources. We conducted a comprehensive review, comparing digital airborne infection risk calculators using standardized building-specific input parameters. These tools generally produce similar and consistent outputs with identical inputs. Variations mainly stem from model selection and the handling of unsteady viral load conditions. Differences in source term calculations, including particle emission concentrations and respiratory activity, also contribute to disparities. These differences are minor compared to the inherent uncertainties in risk assessment. Consistency in results increases with higher ventilation rates, showing a robust trend across models. However, inconsistencies arose in the inclusion of face masks, often due to the lack of detailed efficiency values. Despite some differences, the overall consistency underscores the value of these tools in public health strategy and infectious disease control.</div><div>We also compared some of the model's efforts to conduct retrospective assessments against reported transmission events by assuming input parameters to the models so that the calculated risk would closely fit the original outbreak infection rate. Thus, validating these models against past outbreaks remains challenging because of the lack of essential input information from observed events. This comparative analysis demonstrates the importance of transparent data sources and justifiable model assumptions to enhance the reliability and precision of risk assessments.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 338-352"},"PeriodicalIF":8.8,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of wastewater percent positive for assessing epidemic trends - A case study of COVID-19 in Shangrao, China","authors":"Jing Wang ,&nbsp;Haifeng Zhou ,&nbsp;Wentao Song ,&nbsp;Lingzhen Xu ,&nbsp;Yaoying Zheng ,&nbsp;Chen You ,&nbsp;Xiangyou Zhang ,&nbsp;Yeshan Peng ,&nbsp;Xiaolan Wang ,&nbsp;Tianmu Chen","doi":"10.1016/j.idm.2024.11.001","DOIUrl":"10.1016/j.idm.2024.11.001","url":null,"abstract":"<div><h3>Objective</h3><div>This study aims to assess the feasibility of evaluating the COVID-19 epidemic trend through monitoring the positive percentage of SARS-CoV-19 RNA in wastewater.</div></div><div><h3>Method</h3><div>The study collected data from January to August 2023, including the number of reported cases, the positive ratio of nucleic acid samples in sentinel hospitals, the incidence rate of influenza-like symptoms in students, and the positive ratio of wastewater samples in different counties and districts in Shangrao City. Wastewater samples were obtained through grabbing and laboratory testing was completed within 24 h. The data were then normalized using Z-score normalization and analyzed for lag time and correlation using the xcorr function and Spearman correlation coefficient.</div></div><div><h3>Results</h3><div>A total of 2797 wastewater samples were collected. The wastewater monitoring study, based on sampling point distribution, was divided into two phases. Wuyuan County consistently showed high levels of positive ratio in wastewater samples in both phases, reaching peak values of 91.67% and 100% respectively. The lag time analysis results indicated that the peak positive ratio in all wastewater samples in Shangrao City appeared around 2 weeks later compared to the other three indicators. The correlation analysis revealed a strong linear correlation across all four types of data, with Spearman correlation coefficients ranging from 0.783 to 0.977, all of which were statistically significant.</div></div><div><h3>Conclusion</h3><div>The positive ratio of all wastewater samples in Shangrao City accurately reflected the COVID-19 epidemic trend from January to August 2023. This study confirmed the lag effect of wastewater percent positive and its strong correlation with the reported incidence rate and the positive ratio of nucleic acid samples in sentinel hospitals, supporting the use of wastewater percent positive monitoring as a supplementary tool for infectious disease surveillance in the regions with limited resources.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 325-337"},"PeriodicalIF":8.8,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavioural Change Piecewise Constant Spatial Epidemic Models","authors":"Chinmoy Roy Rahul ,&nbsp;Rob Deardon","doi":"10.1016/j.idm.2024.10.006","DOIUrl":"10.1016/j.idm.2024.10.006","url":null,"abstract":"<div><div>Human behaviour significantly affects the dynamics of infectious disease transmission as people adjust their behavior in response to outbreak intensity, thereby impacting disease spread and control efforts. In recent years, there have been efforts to incorporate behavioural change into spatio-temporal individual-level models within a Bayesian MCMC framework. In this past work, parametric spatial risk functions were employed, depending on strong underlying assumptions regarding disease transmission mechanisms within the population. However, selecting appropriate parametric functions can be challenging in real-world scenarios, and incorrect assumptions may lead to erroneous conclusions. As an alternative, non-parametric approaches offer greater flexibility. The goal of this study is to investigate the utilization of semi-parametric spatial models for infectious disease transmission, integrating an “alarm function” to account for behavioural change based on infection prevalence over time within a Bayesian MCMC framework. In this paper, we discuss findings from both simulated and real-life epidemics, focusing on constant piecewise distance functions with fixed change points. We also demonstrate the selection of the change points using the Deviance Information Criteria (DIC).</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 302-324"},"PeriodicalIF":8.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}