Epidemics最新文献

{"title":"Supporting LGBTQ+ epidemiologists in the UK during research-related travel and international collaboration","authors":"Joseph T. Hicks ,&nbsp;Bethan Cracknell Daniels ,&nbsp;Rosie Maddren ,&nbsp;Jack Doyle ,&nbsp;Travis Mager ,&nbsp;Christina J. Atchison ,&nbsp;Lucy Okell","doi":"10.1016/j.epidem.2025.100871","DOIUrl":"10.1016/j.epidem.2025.100871","url":null,"abstract":"<div><div>Conferences, fieldwork, international positions, and collaborations with international partners are beneficial to any epidemiologist, strengthening relationships with fellow scientists, policymakers, health professionals, and those affected by the studied disease. However, international working can pose unique challenges for minority groups. In the UK, LGBTQ+ scientists have a degree of legal protection against discrimination, and universities often have LGBTQ+ staff-student networks that provide support. By contrast, international work can present barriers that non-LGTBQ+ colleagues may not be aware of, such as stress when travelling to countries with anti-LGBTQ+ laws, policies, or sentiments. Homophobic, biphobic, and transphobic beliefs, policies, and actions fluctuate over time, but persist or are on the rise in many locations across the world, including high-income countries. Without institutional support, work-related travel can present a cognitive burden, threatening both physical safety and mental well-being of LGBTQ+ researchers. At Imperial College London, we have worked to address these challenges by developing resources and training for LGBTQ+ staff, students, and allies. We developed an initiative including the creation of online written resources, integration of these materials into travel safety protocols, and a partnership with a LGBTQ+ mental health organization to offer in-person training. We present our experience developing these resources, describe feedback of training participants, and discuss strategies for institutions to develop their own support resources, fostering greater equity in the research experience for individuals of all identities.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100871"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145558112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rtglm: Unifying estimation of the time-varying reproduction number, Rt, under the Generalised Linear and Additive Models","authors":"Pierre Nouvellet","doi":"10.1016/j.epidem.2025.100857","DOIUrl":"10.1016/j.epidem.2025.100857","url":null,"abstract":"<div><div>Most current methods to estimate the time-varying reproduction number (<em>R_t</em>), such as <em>EpiEstim</em>, rely on branching processes and the renewal equation. They also require subjective choices to set the level of temporal and spatial heterogeneity assumed. We propose a novel framework to estimate <em>R_t</em> based on Generalized Linear and Additive Models (GLM/GAM). By integrating the renewal equation model within GLM/GAM, the proposed framework, “<em>Rtglm</em>”, allows smooth estimation of <em>R_t</em> variations over time and space without relying on arbitrary scaling parameters. The performance of <em>Rtglm</em> was evaluated using historical datasets and simulated outbreaks. It demonstrated improved overall performance and accuracy compared to <em>EpiEstim</em>, as measured by the CRPS scores and Mean Square Errors respectively. However, when case incidence was low and <em>R_t</em> estimation relied on a smoothing term, <em>Rtglm</em> was marginally overconfident in its estimates. The method offers substantial improvement for the real-time estimation of spatio-temporal trends in <em>R_t</em>, with improved performance and lower reliance on arbitrarily set parameters. The open-source and user-friendly R package developed will also simplify user experience. Finally, the framework bridges gaps between epidemic monitoring methodologies and sets the stage for future extensions to enhance statistical inference and integrate additional epidemiological complexities, including the evaluation of intervention strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100857"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in approximate Bayesian inference for models in epidemiology","authors":"Xiahui Li,&nbsp;Fergus Chadwick,&nbsp;Ben Swallow","doi":"10.1016/j.epidem.2025.100855","DOIUrl":"10.1016/j.epidem.2025.100855","url":null,"abstract":"<div><div>Bayesian inference methods are useful in infectious diseases modeling due to their capability to propagate uncertainty, manage sparse data, incorporate latent structures, and address high-dimensional parameter spaces. However, parameter inference through assimilation of observational data in these models remains challenging. While asymptotically exact Bayesian methods offer theoretical guarantees for accurate inference, they can be computationally demanding and impractical for real-time outbreak analysis. This review synthesizes recent advances in approximate Bayesian inference methods that aim to balance inferential accuracy with scalability. We focus on four prominent families: Approximate Bayesian Computation, Bayesian Synthetic Likelihood, Integrated Nested Laplace Approximation, and Variational Inference. For each method, we evaluate its relevance to epidemiological applications, emphasizing innovations that improve both computational efficiency and inference accuracy. We also offer practical guidance on method selection across a range of modeling scenarios. Finally, we identify hybrid exact approximate inference as a promising frontier that combines methodological rigor with the scalability needed for the response to outbreaks. This review provides epidemiologists with a conceptual framework to navigate the trade-off between statistical accuracy and computational feasibility in contemporary disease modeling.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100855"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pilot study to correlate wastewater and clinical surveillance for hepatitis A in New York state","authors":"Maxwell D. Weidmann ,&nbsp;Patrick W. Bryant ,&nbsp;Lynsey Schoultz ,&nbsp;Aishwarya Jadhav ,&nbsp;Lindsey Rickerman ,&nbsp;Dustin T. Hill ,&nbsp;Daryl M. Lamson ,&nbsp;David A. Larsen ,&nbsp;Kirsten St. George","doi":"10.1016/j.epidem.2025.100867","DOIUrl":"10.1016/j.epidem.2025.100867","url":null,"abstract":"<div><div>The COVID-19 pandemic prompted a rapid expansion of wastewater-based surveillance in New York State (NYS). Pilot studies were initiated in 2023 to assess the use of this system for the surveillance of hepatitis A virus (HAV) and other pathogens of public health interest. A known cause of outbreaks in the US associated with contaminated food products and transmission between injection drug users, HAV is present in feces for weeks before the onset of symptoms. However, the use of wastewater surveillance as an early warning system has not been assessed outside of the outbreak setting. We compare clinical HAV surveillance with quantitative testing of wastewater samples for HAV RNA from four counties in NYS between September 2022 and November 2023, a period of relatively low HAV incidence. There was a significantly higher mean concentration of HAV RNA in wastewater from sewersheds in districts with reported HAV cases, relative to those without (267 vs. 21 gene copies per microliter, p &lt; 0.05). For 91 % of HAV cases, HAV RNA was detected in the wastewater from the same county between HAV exposure onset and diagnosis, and new HAV RNA detection in wastewater occurred, on average, 41 days before case diagnosis. Our findings demonstrate that wastewater surveillance may provide early warning of case clusters at the county level in low-incidence settings and may allow for detection of otherwise missed asymptomatic or mild illness. Expansion of testing to include all sewersheds in each county may further improve the sensitivity for identifying locations for targeted HAV intervention.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100867"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The transmission dynamics of Norovirus in England: A genotype-specific modelling study","authors":"Juan F. Vesga ,&nbsp;Amy Douglas ,&nbsp;Cristina Celma ,&nbsp;Edward S. Knock ,&nbsp;Marc Baguelin ,&nbsp;W. John Edmunds","doi":"10.1016/j.epidem.2025.100875","DOIUrl":"10.1016/j.epidem.2025.100875","url":null,"abstract":"<div><h3>Background</h3><div>Norovirus is the leading cause of acute gastroenteritis cases in England and worldwide, with diverse co-circulating genotypes. Vaccine candidates targeting multiple genotypes are advancing. However, most transmission models still focus on single-strain dynamics, limiting their ability to assess the role of co-circulating strains on population burden.</div></div><div><h3>Methods</h3><div>We developed an age structured multistrain transmission model that integrates norovirus genotype diversity, waning immunity, and cross-protection within genogroups. We calibrate to case and genotyping surveillance time-series data with community-wide age structured incidence estimates and cross-sectional seroprevalence among English children to capture the transmission dynamics of the main co-circulating norovirus strains in England. Using a calibrated model, we examine the case of an emerging GII.4 variant under different scenarios of transmissibility.</div></div><div><h3>Results</h3><div>We found that on average the current GII.4 strain has an R₀ of 4.8 (CrI 4.5 – 5.01). We estimate the average number of lifetime norovirus episodes per person to be 5.2 (CrI 95 % 4.5 – 6.3) in the absence of new pandemic strains, with 66 % of children in England experiencing at least one symptomatic episode by the age of four. Our sensitivity analysis and model selection suggests that cross-protection within genogroups (between strains of the same genogroup), is very limited at conferring protection. Importantly, our modelling suggests that a potential emerging variant would cause a larger first epidemic season and return to baseline levels with an increase in relative contribution of GII.4. If such variant was more transmissible, the size of the initial peak could almost double the current average epidemic peak.</div></div><div><h3>Conclusions</h3><div>This approach addresses key limitations of single-strain frameworks and offers a more comprehensive understanding of norovirus dynamics, improving the capacity to assess the potential population-level effects of upcoming multivalent vaccine strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100875"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimized geo-hierarchical ensemble model to forecast hospitalizations from respiratory viruses in the United States","authors":"Shaochong Xu ,&nbsp;Hongru Du ,&nbsp;Ensheng Dong ,&nbsp;Xianglong Wang ,&nbsp;Liyue Zhang ,&nbsp;Lauren M. Gardner","doi":"10.1016/j.epidem.2025.100869","DOIUrl":"10.1016/j.epidem.2025.100869","url":null,"abstract":"<div><div>Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-term forecasting of COVID-19, among other respiratory viruses, however, there is a need to further improve these frameworks. Here, we propose the generalized Optimized Geo-Hierarchical Ensemble Model (OGEM), a novel forecasting machine learning framework to forecast state-level hospitalizations of influenza, COVID-19, and RSV in the U.S. independently. This framework is multi-resolution: it integrates state, regionally-trained, and nationally-trained models through an ensemble layer and applies various optimization methods to parameterize the model weights and enhance overall predictive accuracy. This proposed framework builds on existing forecasting literature by 1) employing an ensemble of three spatially hierarchical models with state-level forecasts as the output; 2) incorporating four distinct weight optimization methods to generate the ensemble; 3) utilizing clustering methods to dynamically identify multi-state regions as a function of short-term and long-term hospitalization trends for the regionally-trained model; and 4) providing a generalized framework to forecast the expected near-term hospitalizations from Influenza, RSV and COVID-19. Results demonstrate OGEM is a robust framework with relatively high performance. Extensive experimentation using historical data highlights the predictive power of our framework compared to existing ensemble approaches. Its robust performance underscores the framework's effectiveness and potential for improving and broadening infectious disease forecasting.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100869"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145573965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random Forest of epidemiological models for Influenza forecasting","authors":"Majd Al Aawar,&nbsp;Ajitesh Srivastava","doi":"10.1016/j.epidem.2025.100862","DOIUrl":"10.1016/j.epidem.2025.100862","url":null,"abstract":"<div><div>Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so hospitals can be better prepared for an influx of patients. Many forecasting methods have been used in real-time during the Influenza seasons and submitted to the CDC for public communication. We hypothesize that we can improve forecasting by using multiple mechanistic models to produce potential trajectories and use machine learning to learn how to combine those trajectories into an improved forecast. We propose a Tree Ensemble model design that utilizes the individual predictors of our baseline model SIkJalpha to improve its performance. Each predictor is generated by changing a set of hyperparameters. We compare our prospective forecasts deployed for the FluSight challenge (seasons ending in 2022, 2023, and 2024) to all the other submitted approaches. Our approach is fully automated and does not require any manual tuning. Our submissions remained in the top 33% of the models in all seasons. We demonstrate that our Random Forest-based approach is able to improve upon the forecasts of the individual predictors in terms of mean absolute error, coverage, and weighted interval score. Our method retrospectively outperformed all other models in terms of the mean absolute error and the weighted interval score based on the mean across all weekly submissions of the 2021–22 season.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100862"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the impact of non-pharmaceutical interventions (NPIs) on post-pandemic Respiratory Syncytial Virus (RSV) hospitalisations and seasonality in Wales, UK","authors":"Gabriella Santiago ,&nbsp;Carla White ,&nbsp;Brendan Collins ,&nbsp;Simon Cottrell ,&nbsp;Chris Williams ,&nbsp;Biagio Lucini ,&nbsp;Mike B. Gravenor","doi":"10.1016/j.epidem.2025.100860","DOIUrl":"10.1016/j.epidem.2025.100860","url":null,"abstract":"<div><h3>Introduction:</h3><div>Respiratory Syncytial Virus (RSV) is a single-stranded RNA virus and a major cause of hospitalisations in paediatric and geriatric populations. In the Northern Hemisphere, the RSV season is typically between October and March. Following the introduction of Non-pharmaceutical Interventions (NPIs), in response to the COVID-19 pandemic, disruptions in seasonality have been observed.</div></div><div><h3>Methods:</h3><div>We used an age-structured, deterministic SE₂I₂R model with time-dependent contact rates to study RSV hospitalisations and seasonality in the context of specific NPIs in Wales. The transmission process was linked to a clinical events model, to allow comparison to paediatric admissions data from Public Health Wales. The model was calibrated using Welsh demographics, social contact surveys and a severity index of Welsh NPI impact.</div></div><div><h3>Results:</h3><div>Admissions data revealed three out-of-season outbreaks (Autumn 2020, Autumn 2021 and Summer 2022). A surge of admissions in Winter 2022-23 and Winter 2023-24 were forecasted, with peak timings correctly predicted, despite a more protracted outbreak observed in the data. Approximately, 90% of RSV admissions in Wales from 2016-22 were in infants under 1 year old; with the greatest shift in admissions age-structure in 2-4 year olds (quintupling in 2021). The model predicted a rapid return to pre-pandemic patterns after disruptions.</div></div><div><h3>Discussion/Conclusions:</h3><div>Out-of-season peaks chiefly coincided with NPI relaxation. The post-pandemic response of RSV, in terms of timings, magnitude and age-structure shift, were all broadly consistent with simple interruptions in population exposure during the pandemic and the build up of immune naïve cohorts. Our model forms the basis of medium-term projections for paediatric RSV admissions in Wales.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100860"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of household physical distancing and its timing on the transmission of SARS-CoV-2: Insights from a household transmission evaluation study","authors":"Pietro Coletti ,&nbsp;Niel Hens ,&nbsp;Christel Faes ,&nbsp;Huong Q. McLean ,&nbsp;Edward A. Belongia ,&nbsp;Melissa Rolfes ,&nbsp;Alexandra Mellis ,&nbsp;Carrie Reed ,&nbsp;Jessica Biddle ,&nbsp;Ahra Kim ,&nbsp;Yuwei Zhu ,&nbsp;H. Keipp Talbot ,&nbsp;Carlos G. Grijalva","doi":"10.1016/j.epidem.2025.100868","DOIUrl":"10.1016/j.epidem.2025.100868","url":null,"abstract":"<div><h3>Background</h3><div>Studies on SARS-CoV-2 household transmission often assume random mixing, overlooking detailed contact patterns and the timing of physical distancing.</div></div><div><h3>Methods</h3><div>To address this, we examined interactions within 280 households, including 280 index cases and 544 members, enrolled from April 2020 to April 2021 in Nashville, Tennessee, and central Wisconsin. Eligible households were enrolled within 7 days of index case symptom onset if at least one member was initially asymptomatic. Participants were monitored for 14 days, with symptoms and respiratory specimens collected daily, and contact data retrospectively assessed at three time points: the day before index case symptom onset, the day before enrollment, and 14 days post-enrollment. We fitted Exponential Random Graph Models to the contact pattern to identify drivers of household contact. We used the fitted household models to inform a two-level mixing model to account for community infection risk, and we calibrated it to the infection data. We then used the calibrated model to study different implementation of physical distancing.</div></div><div><h3>Results</h3><div>Contact patterns showed a significant reduction in physical interactions after infection awareness, particularly avoidance of index cases, with a 77% reduction in contact density (95% CI [65%-84%], <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>). Simulations from the two-level mixing model indicated that initiating contact reductions at symptom onset could lower secondary infections by over 25% in households of 4-5 members.</div></div><div><h3>Conclusions</h3><div>These results demonstrate how behavior changes following infection awareness reduce transmission. Implementing physical distancing earlier, at symptom onset, could further limit secondary infections and enhance household transmission control.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100868"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wastewater-based surveillance for influenza and respiratory syncytial virus: Insights from a 21-month study in Oklahoma","authors":"Gargi Deshpande ,&nbsp;Bijay Rimal ,&nbsp;Kristen Shelton ,&nbsp;Jason Vogel ,&nbsp;Bradley Stevenson ,&nbsp;Katrin Gaardbo Kuhn","doi":"10.1016/j.epidem.2025.100861","DOIUrl":"10.1016/j.epidem.2025.100861","url":null,"abstract":"<div><div>Upper respiratory infections caused by viruses such as respiratory syncytial virus (RSV) and influenza are major health concerns globally. Traditional surveillance methods of these viruses rely on clinical data, which can miss mild or asymptomatic cases, leading to gaps in understanding of their epidemiology. Wastewater-based surveillance (WBS) offers an alternative monitoring approach, providing real-time, population-representative data infection levels. This study aimed to evaluate the value of WBS for monitoring influenza A and B and RSV in Oklahoma from August 2022 to May 2024. Wastewater samples were collected weekly from 18 treatment plants statewide, and viral RNA was quantified using RT-qPCR. We compared wastewater data with reported influenza hospitalizations and RSV test positivity. We found significant seasonality in clinical outcomes as well as wastewater concentrations for influenza A and RSV. Our results also showed comparatively high wastewater concentrations during times when influenza hospitalizations and RSV test positivity were at their seasonal highs. Our study demonstrates the potential for WBS to offer timely insights into respiratory virus trends, particularly for underserved communities. This method complements traditional surveillance, offering a broader understanding of viral transmission and supporting public health interventions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100861"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}