Infectious Disease Modelling最新文献

{"title":"Epidemiological indices with multiple circulating pathogen strains","authors":"Cristiano Trevisin ,&nbsp;Lorenzo Mari ,&nbsp;Marino Gatto ,&nbsp;Vittoria Colizza ,&nbsp;Andrea Rinaldo","doi":"10.1016/j.idm.2025.03.006","DOIUrl":"10.1016/j.idm.2025.03.006","url":null,"abstract":"<div><div>Epidemiological indicators (e.g. reproduction numbers and epidemicity indices) describe long- and short-term behaviour of ongoing epidemics. Their evolving values provide context for designing control measures because maintaining both indices below suitable thresholds warrants waning infection numbers. However, current models for the computation of epidemiological metrics do not consider the stratification of the pathogen into variants endowed with different infectivity and epidemiological severity. This is the case, in particular, with SARS-CoV-2 infections. Failing to account for the variety of epidemiological features of emerging variants prevents epidemiological indices from spotting the possible onset of uncontrolled growth of specific variants, thus significantly limiting the prognostic value of the indicators. Here, we expand an existing framework for the computation of spatially explicit reproduction numbers and epidemicity indices to account for arising variants. By analysing the data of the COVID-19 pandemic in Italy, we show that embedding additional layers of complexity in the mathematical descriptions of unfolding epidemics reveals new angles. In particular, we find epidemiological metrics significantly exceeding their thresholds at the emergence of new variants. Such values foresee a recrudescence in new infections that only becomes evident after emerging new variants have effectively replaced the previous active strains. The demography of the variant composition flags the presence of specific strains growing more rapidly than the total number of infections generated by all variants combined. Variant-aware epidemiological indicators thus allow to engineer better control measures tailored to the shifting patterns of severity and evolving features of infectious disease epidemics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 802-812"},"PeriodicalIF":8.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686190","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":"Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network","authors":"Yunyi Cai ,&nbsp;Weiyi Wang ,&nbsp;Lanlan Yu ,&nbsp;Ruixiao Wang ,&nbsp;Gui-Quan Sun ,&nbsp;Allisandra G. Kummer ,&nbsp;Paulo C. Ventura ,&nbsp;Jiancheng Lv ,&nbsp;Marco Ajelli ,&nbsp;Quan-Hui Liu","doi":"10.1016/j.idm.2025.03.005","DOIUrl":"10.1016/j.idm.2025.03.005","url":null,"abstract":"<div><div>In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%–88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1. In conclusion, our model-based evaluation highlights the challenges of relying on TTIs to contain an outbreak of a novel pathogen with characteristics similar to SARS-CoV-2, and that the estimated effectiveness of TTI depends on the way contact patterns are modeled, supporting the relevance of obtaining comprehensive data on human social interactions to improve preparedness.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 775-786"},"PeriodicalIF":8.8,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654522","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":"A refractory density approach to a multi-scale SEIRS epidemic model","authors":"Anton Chizhov ,&nbsp;Laurent Pujo-Menjouet ,&nbsp;Tilo Schwalger ,&nbsp;Mattia Sensi","doi":"10.1016/j.idm.2025.03.004","DOIUrl":"10.1016/j.idm.2025.03.004","url":null,"abstract":"<div><div>We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We introduce a microscopic model that describes the probability of infection for a single individual and the evolution of the disease within their body. From the individual-level description, we then present the corresponding population-level model of epidemic spreading on the mesoscopic and macroscopic scale. We conclude with numerical illustrations, taking into account either a white Gaussian noise or an escape noise to showcase the potential of our approach in producing both transient and asymptotic complex dynamics as well as finite-size fluctuations consistently across multiple scales. A comparison with the epidemiology of coronaviruses is also given to corroborate the qualitative relevance of our new approach.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 787-801"},"PeriodicalIF":8.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686191","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":"The interaction between population age structure and policy interventions on the spread of COVID-19","authors":"Hao Yin ,&nbsp;Zhu Liu ,&nbsp;Daniel M. Kammen","doi":"10.1016/j.idm.2025.03.003","DOIUrl":"10.1016/j.idm.2025.03.003","url":null,"abstract":"<div><div>COVID-19 has triggered an unprecedented public health crisis and a global economic shock. As countries and cities have transitioned away from strict pandemic restrictions, the most effective reopening strategies may vary significantly based on their demographic characteristics and social contact patterns. In this study, we employed an extended age-specific compartment model that incorporates population mobility to investigate the interaction between population age structure and various containment interventions in New York, Los Angeles, Daegu, and Nairobi – four cities with distinct age distributions that served as local epicenters of the epidemic from January 2020 to March 2021. Our results demonstrated that individual social distancing or quarantine strategies alone cannot effectively curb the spread of infection over a one-year period. However, a combined strategy, including school closure, 50 % working from home, 50 % reduction in other mobility, 10 % quarantine rate, and city lockdown interventions, can effectively suppress the infection. Furthermore, our findings revealed that social-distancing policies exhibit strong age-specific effects, and age-targeted interventions can yield significant spillover benefits. Specifically, reducing contact rates among the population under 20 can prevent 14 %, 18 %, 56 %, and 99 % of infections across all age groups in New York, Los Angeles, Daegu, and Nairobi, respectively, surpassing the effectiveness of policies exclusively targeting adults over 60 years old. In particular, to protect the elderly, it is essential to reduce contacts between the younger population and people of all age groups, especially those over 60 years old. While an older population structure may escalate fatality risk, it might also decrease infection risk. Moreover, a higher basic reproduction number amplifies the impact of an older population structure on the fatality risk of the elderly. The considerable variations in susceptibility, severity, and mobility across age groups underscore the need for targeted interventions to effectively control the spread of COVID-19 and mitigate risks in future pandemics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 758-774"},"PeriodicalIF":8.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654521","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":"Early prediction of the outbreak risk of dengue fever in Ba Ria-Vung Tau province, Vietnam: An analysis based on Google trends and statistical models","authors":"Dang Anh Tuan ,&nbsp;Pham Vu Nhat Uyen","doi":"10.1016/j.idm.2025.03.001","DOIUrl":"10.1016/j.idm.2025.03.001","url":null,"abstract":"<div><div>Dengue fever (DF), caused by the Dengue virus through the Aedes mosquito vector, is a dangerous infectious disease with the potential to become a global epidemic. Vietnam, particularly Ba Ria-Vung Tau (BRVT) province, is facing a high risk of DF. This study aims to determine the relationship between the search volume for DF on Google Trends and DF cases in BRVT province, thereby constructing a model to predict the early outbreak risk of DF locally. Using Poisson regression (adjusted by quasi-Poisson), considering the lagged effect of Google Trends Index (GTI) search volume on DF cases, and removing the autocorrelation (AC) of DF cases by using appropriate transformations, seven forecast models were surveyed based on the dataset of DF cases and GTI search volume weekly with the phrase \"sốt xuất huyết\" (dengue fever) in BRVT province from January 2019 to August 2023 (243 weeks). The model selected is the one with the lowest dispersion index. The results show that the correlation coefficient (95% confidence interval) and dispersion index of the 7 models including Basis TSR; Basis TSR + AC: Lag(Residuals,1); Basis TSR + AC: Lag(SXH,1); Basis TSR + AC: Lag(log(SXH+1),1); TSR Lag(GTI,2) + AC: Lag(log(SXH+1),2); TSR Lag(GTI,3) + AC: Lag(log(SXH+1),3); TSR Lag(GTI,0) + AC: Lag(log(SXH+1),1) are 0.71 (0.63–0.76) and 74.2; 0.79 (0.73–0.83) and 48.6; 0.89 (0.87–0.92) and 37.3; 0.98 (0.97–0.99) and 7.2; 0.96 (0.95–0.97) and 14.3; 0.93 (0.91–0.94) and 25.7; 0.98 (0.97–0.99) and 6.8, respectively. Therefore, the final model is the most suitable one selected. Testing the accuracy of the selected model using the ROC curve with the Youden criterion, the AUC (threshold 75%) is 0.982, and the AUC (threshold 95%) is 0.984, indicating the very good predictive ability of the model. In summary, the research results show the potential for applying this model in Vietnam, especially in BRVT, to enhance the effectiveness of epidemic prevention measures and protect public health.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 743-757"},"PeriodicalIF":8.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631905","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":"A graph-theoretic framework for integrating mobility data into mathematical epidemic models","authors":"Razvan G. Romanescu","doi":"10.1016/j.idm.2025.02.008","DOIUrl":"10.1016/j.idm.2025.02.008","url":null,"abstract":"<div><div>Advances in modeling the spread of infectious diseases have allowed modellers to relax the homogeneous mixing assumption of traditional compartmental models. The recently introduced synthetic network model, which is an SIRS type model based on a non-linear transmission rate, effectively decouples the underlying population network structure from the epidemiological parameters of disease, and has been shown to produce superior fits to multi-wave epidemics. However, inference from case counts alone is generally problematic due to the partial unidentifiability between probability of person to person transmission and the average number of contacts per individual. An alternate source of data that can inform the network alone has the potential to improve overall modeling results. Aggregate cell phone mobility data, which record daily numbers of visits to points of interest, provide a proxy for the number of contacts that people establish during their visits. In this paper, we link the contact rate from an epidemic model to the total number of contacts formed in the population. Inferring the latter from Google Community Mobility Reports data, we develop an integrated epidemic model whose transmission adapts to population mobility. This model is illustrated on the first four waves of the COVID-19 pandemic.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 716-730"},"PeriodicalIF":8.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488588","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":"Modeling hepatitis B-related deaths in China to achieve the WHO's impact target","authors":"Wenjun Liu ,&nbsp;Renjie Liu ,&nbsp;Peng Li ,&nbsp;Ruyi Xia ,&nbsp;Zhuoru Zou ,&nbsp;Lei Zhang ,&nbsp;Mingwang Shen ,&nbsp;Guihua Zhuang","doi":"10.1016/j.idm.2025.02.010","DOIUrl":"10.1016/j.idm.2025.02.010","url":null,"abstract":"<div><h3>Background</h3><div>The World Health Organization (WHO) targets a 65% reduction in hepatitis B-related deaths by 2030 compared to 2015 to eliminate viral hepatitis as a major public health threat. It is unknown whether and how China can achieve this target despite significant intervention achievements. We aimed to predict the hepatitis B-related deaths in China and identify key developments needed to achieve the target.</div></div><div><h3>Methods</h3><div>An age- and time-dependent dynamic hepatitis B virus (HBV) transmission compartmental model was developed to predict the trend of hepatitis B-related deaths under base-case and subsequent scenarios from 2015 to 2040. In base-case scenario, we assumed the diagnosis and treatment (D&amp;T) rate would reach 72% in 2030, as proposed by WHO. Subsequent scenarios were set based on the results of base-case and one-way sensitivity analysis.</div></div><div><h3>Results</h3><div>Compared with 2015, hepatitis B-related deaths would be reduced by 23.89% in 2030 and 51.79% in 2040, respectively, and the WHO's impact target of 65% reduction would not be achieved until 2038 at the earliest under base-case scenario. HBV clearance rate and current treatment effectiveness were the most sensitive parameters that significantly influenced the decline of hepatitis B-related deaths from 2015 to 2040. In the subsequent scenario, when D&amp;T rate improving to 90% by 2030, with the current treatment effectiveness and HBV clearance rate being optimized from 2016, the WHO's impact target would be achieved in 2038. Increasing the clearance rate further from 2% to 2.8% during 2016–2030 linearly, the impact target would be achieved on time.</div></div><div><h3>Conclusions</h3><div>It is difficult for China to achieve the WHO's impact target of 65% reduction in hepatitis B-related deaths by 2030 even we assumed the D&amp;T rate would reach 72% in 2030 and beyond. A comprehensive scale-up of available strategies, especially innovative drugs and technologies will ensure that China achieves the target on schedule.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 731-742"},"PeriodicalIF":8.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526587","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":"Prediction of monthly occurrence number of scrub typhus in Ganzhou City, China, based on SARIMA and BPNN models","authors":"Renfa Huang ,&nbsp;Kailun Pan ,&nbsp;Qingfeng Cai ,&nbsp;Fen Lin ,&nbsp;Hua Xue ,&nbsp;Mingpeng Li ,&nbsp;Yong Liao","doi":"10.1016/j.idm.2025.02.009","DOIUrl":"10.1016/j.idm.2025.02.009","url":null,"abstract":"<div><div>Scrub typhus poses a serious public health risk globally. Forecasting the occurrence of the disease is essential for policymakers to develop prevention and control strategies. This study investigated the application of modelling techniques to predict the occurrence of scrub typhus and establishes an early warning system aimed at providing a foundational reference for its effective prevention and control. In this study, the monthly occurrence of scrub typhus in Ganzhou City from January 2008 to December 2022 was utilized as the training set for the first part of the analysis, while the data from January 2008 to December 2019 served as the training set for the second part. Based¹ on these data, the SARIMA model, the BPNN model, and the combined SARIMA-BPNN model were developed and validated using data from January to December 2023. The most effective model was then selected to predict the number of occurrences of scrub typhus for the years 2024 and 2025, respectively. The root mean square error (RMSE) and mean absolute error (MAE) of the BPNN (3-9-1) model, developed using data from January 2008 to December 2022, were 8.472 and 6.4, respectively. In contrast, the RMSE and MAE of the combined SARIMA-BPNN (1-9-1) model, constructed using data from January 2008 to December 2019, were 19.361 and 16.178, respectively. In addition, the BPNN (3-9-1) model predicted 284 cases of scrub typhus in Ganzhou City for 2024, and 163 cases for 2025. The BPNN (3-9-1) model demonstrated strong applicability in predicting the monthly occurrence of scrub typhus. Furthermore, incorporating three years of data on the occurrence of new crown outbreaks when developing a predictive model for infectious diseases can substantially enhance prediction accuracy.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 691-701"},"PeriodicalIF":8.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464262","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":"Controlling endemic foot-and-mouth disease: Vaccination is more important than movement bans. A simulation study in the Republic of Turkey","authors":"Glen Guyver-Fletcher ,&nbsp;Erin E. Gorsich ,&nbsp;Chris Jewell ,&nbsp;Michael J. Tildesley","doi":"10.1016/j.idm.2025.02.006","DOIUrl":"10.1016/j.idm.2025.02.006","url":null,"abstract":"","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 702-715"},"PeriodicalIF":8.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473986","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":"Diphtheria transmission dynamics – Unveiling generation time and reproduction numbers from the 2022–2023 outbreak in Kano state, Nigeria","authors":"Raoul Kamadjeu ,&nbsp;Oyeladun Okunromade ,&nbsp;Bola Biliaminu Lawal ,&nbsp;Muzammil Gadanya ,&nbsp;Salma Ali Suwaid ,&nbsp;Eduardo Celades Blanco ,&nbsp;Ifedayo Adetifa ,&nbsp;Elizabeth A. Kelvin","doi":"10.1016/j.idm.2025.02.007","DOIUrl":"10.1016/j.idm.2025.02.007","url":null,"abstract":"<div><h3>Background</h3><div>Diphtheria, caused by Corynebacterium diphtheriae, remains a serious public health threat in areas with low vaccination coverage, despite global declines due to widespread immunization and improved clinical management. A major outbreak in Nigeria from 2022 to 2023 underscored the persistent risk in regions with inadequate vaccination. This study aims to assess the transmission dynamics of diphtheria in Kano State, the epicenter of the outbreak, by estimating key epidemiological parameters, including the generation time (GT), approximated in our study by serial interval, and effective reproduction number (Rₜ).</div></div><div><h3>Methods</h3><div>We analyzed diphtheria case-based data from Kano State, Nigeria, collected between August 18, 2022, and November 29, 2023. Generation time was approximated using serial intervals in confirmed cases within the same geographical areas. The effective reproduction number (Rₜ) was calculated using four methods: Maximum Likelihood Estimation (MLE), Exponential Growth (EG), Sequential Bayesian (SB), and Time-Dependent (TD), focusing on the period of maximum exponential growth. A sensitivity analysis was conducted to quantify the impact of uncertainties in the GT derived from our data on the estimation of Rₜ.</div></div><div><h3>Results</h3><div>Over the 469-day outbreak period, 13,899 diphtheria cases were reported, with complete data available for 9406 cases. The estimated mean generation time was 2.8 days (SD = 3.48 days), with 97% of cases having a GT of less than 21 days. The Rₜ estimates varied across methods, with the TD method producing the highest reproduction number of 2.21 during the peak growth period. Sensitivity analysis showed that Rₜ estimates increased with longer generation times. The models, except for the SB method, demonstrated a generally strong fit with the outbreak exponential growth period.</div></div><div><h3>Conclusion</h3><div>The ongoing diphtheria outbreak in Nigeria highlights the critical threat posed by declining vaccination coverage. This study provides valuable insights into the transmission dynamics of diphtheria during a prolonged and widespread outbreak, enhancing our understanding of disease spread in this context. While certain limitations may influence the interpretation of our estimates, the findings offer valuable information for future diphtheria outbreak preparedness and response in the African context.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 680-690"},"PeriodicalIF":8.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421046","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}