Epidemics最新文献

{"title":"Modelling the dynamics of SARS-CoV-2 during the first 14 days of infection","authors":"Jingsi Xu ,&nbsp;Martín López-García ,&nbsp;Thomas House ,&nbsp;Ian Hall","doi":"10.1016/j.epidem.2025.100843","DOIUrl":"10.1016/j.epidem.2025.100843","url":null,"abstract":"<div><div>Interpreting the viral mechanism of SARS-CoV-2 based on the human body level is critical for developing more efficient interventions. Due to the limitation of data, limited models consider the viral dynamics of the early phase of infection. The Human Challenge Study (Killingley et al., 2022) enables us to obtain data from inoculation to the 14th day after infection, which provides an overview of the dynamics of SARS-CoV-2 infection within the host. In the Human Challenge Study, each volunteer was inoculated with 10TCID50, approximately 55PFU, of a wild-type of virus (Killingley et al., 2022), and the data indicates that the viral load reduced below the detectable level within a day.</div><div>The simplified within-host models developed by Xu et al. (2023) explain the data from the Human Challenge Study (Killingley et al., 2022). However, they do not explain the viral decay from Day 0 to Day 1. Hence, in this paper, we aim to develop a new viral mechanism to explain this phenomenon. Based on the simplified within-host models developed by Xu et al. (2023), we consider that the virus will first go through an adjustment phase and then start to replicate. A new dose-response model is developed to evaluate the probability of infection by constructing a boundary problem. We will discuss this viral mechanism and fit the model to the data of the Human Challenge Study (Killingley et al., 2022) by adopting AMC-SMC (approximate Bayesian computation-sequential Monte Carlo). Based on the results of parameter inference, we estimate that the adjusted viral load is around 1% of the inoculated viral load.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100843"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781205","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":"Sequential Monte Carlo Squared for online inference in stochastic epidemic models","authors":"Dhorasso Temfack,&nbsp;Jason Wyse","doi":"10.1016/j.epidem.2025.100847","DOIUrl":"10.1016/j.epidem.2025.100847","url":null,"abstract":"<div><div>Effective epidemic modeling and surveillance require computationally efficient methods that can continuously update parameter estimates as new data becomes available. This paper explores the application of an online variant of Sequential Monte Carlo Squared (O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) to the stochastic Susceptible–Exposed–Infectious–Removed (SEIR) model for real-time epidemic tracking. The advantage of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> lies in its ability to update parameter estimates using a particle Metropolis–Hastings kernel by only utilizing a fixed window of recent observations. This feature enables timely parameter updates and significantly enhances computational efficiency compared to standard SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, which requires processing all past observations. First, we demonstrate the efficiency of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> on simulated epidemic data, where both the true parameter values and the observation process are known. We then make an application to a real-world COVID-19 dataset from Ireland, successfully tracking the epidemic and estimating a time-dependent reproduction number of the disease. Our results show that O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> provides accurate online estimates of both static and dynamic epidemiological parameters while substantially reducing computational cost. These findings highlight the potential of O-SMC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> for real-time epidemic monitoring and supporting adaptive public health interventions.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100847"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890108","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":"Verifying infectious disease scenario planning for geographically diverse populations","authors":"Jessica R. Conrad ,&nbsp;Paul W. Fenimore ,&nbsp;Kelly R. Moran ,&nbsp;Marisa C. Eisenberg","doi":"10.1016/j.epidem.2025.100833","DOIUrl":"10.1016/j.epidem.2025.100833","url":null,"abstract":"<div><div>In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model.</div><div>We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost <em>per capita</em> infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations.</div><div>Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100833"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501024","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":"Modeling transmission dynamics and socio-economic determinants of scarlet fever in Chengdu, China: An integrated SEIAR and machine learning approach","authors":"Tianlong Yang ,&nbsp;Xunbo Du ,&nbsp;Junfan Li ,&nbsp;Tin Zhang ,&nbsp;Yao Wang ,&nbsp;Liang Wang","doi":"10.1016/j.epidem.2025.100844","DOIUrl":"10.1016/j.epidem.2025.100844","url":null,"abstract":"<div><h3>Background</h3><div>Scarlet fever (SF) is an acute infectious disease that poses a significant public health threat; however, its transmission dynamics, particularly the impact of asymptomatic carriers and socioeconomic determinants, remain unclear.</div></div><div><h3>Methods</h3><div>We developed a susceptible–exposed–infectious–asymptomatic–recovered (SEIAR) model that incorporates asymptomatic infections to estimate the time-varying reproduction number (<em>R</em>_<em>t</em>) for SF in Chengdu (2005–2019) using local epidemiological data. The model was evaluated using the coefficient of determination (<em>R</em>²), and sensitivity analysis confirmed its robustness. We further integrated Boruta, Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to systematically assess the influence of socioeconomic variables on <em>R</em>_<em>t</em>.</div></div><div><h3>Results</h3><div>Between 2005 and 2019, Chengdu reported 11,499 cases of SF, with an average incidence of 4.87 per 100,000. Two distinct seasonal peaks occurred in April–May and November–December, and incidence rates were notably lower during school holidays. The majority of cases affected children aged 3–7, with a male-to-female ratio of 1.59:1. In addition, core districts such as Wuhou and Xindu exhibited the highest incidence. The SEIAR model demonstrated strong predictive performance (overall <em>R</em>² = 0.831, <em>P</em> &lt; 0.001) and estimated a median <em>R</em>_<em>t</em> of 0.963; however, several regions exceeded this threshold, with <em>R</em>_<em>t</em> peaking approximately two months prior to incidence surges. Spatial analyses revealed significant clustering in central urban areas, and integrated socioeconomic analysis identified the one-child rate as the primary driver of <em>R</em>_<em>t</em>, followed by population density and healthcare facility density (<em>P</em> &lt; 0.01).</div></div><div><h3>Conclusion</h3><div>By integrating epidemiological data with socioeconomic factors, this study quantitatively elucidates the transmission characteristics of SF in Chengdu, providing data-driven support for monitoring and targeted intervention strategies in the absence of vaccination.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100844"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597434","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":"Characterizing potential interaction between respiratory syncytial virus and seasonal influenza in the U.S.","authors":"Jiani Chen ,&nbsp;Deven V. Gokhale ,&nbsp;Ludy Registre Carmola ,&nbsp;Liang Liu ,&nbsp;Pejman Rohani ,&nbsp;Justin Bahl","doi":"10.1016/j.epidem.2025.100850","DOIUrl":"10.1016/j.epidem.2025.100850","url":null,"abstract":"<div><div>RSV and seasonal influenza are two of the most prevalent causes of respiratory infection in the U.S. In this study, we used weekly positive case reports and genetic surveillance data to characterize the circulation of these viruses in the United States between 2011 and 2019 and a mathematical modeling approach to explore their potential interaction at a regional level. Our analyses showed that RSV and seasonal influenza co-circulate with different relative epidemic sizes and seasonal overlaps across regions and seasons. We found that RSV had a different evolutionary dynamic compared to seasonal influenza and that local persistence may play a role in underlying annual epidemics. Our analysis supports a potential competitive interaction between RSV and seasonal influenza in most regions across the United States. The multiple-pathogen modeling framework suggests that cross-immunity following infection of either virus might be one of the key drivers of viral competition. However, this finding is based on model-derived inferences and limited surveillance data; further investigation is needed to confirm its robustness and gain a better understanding of the underlying mechanisms. These findings underscore the importance of continued research into the immunological and ecological mechanisms of viral inference, which might be important for the development of more effective protective strategies against co-circulating respiratory viruses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100850"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831576","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":"Projecting the population-level impact of norovirus vaccines","authors":"Katia Koelle ,&nbsp;Brooke Lappe ,&nbsp;Benjamin A. Lopman ,&nbsp;Max S.Y. Lau ,&nbsp;Emma Viscidi ,&nbsp;Katherine B. Carlson","doi":"10.1016/j.epidem.2025.100842","DOIUrl":"10.1016/j.epidem.2025.100842","url":null,"abstract":"<div><div>Norovirus diversity has major implications for vaccine design. The number of circulating genogroups and genotypes, and the way this viral diversity interacts at the population level, will factor into how many and which genotypes should be included in an effective vaccine. Here, we develop an age-stratified, multi-strain model for norovirus to project potential population-level impacts of different vaccine formulations on genotype-specific and overall annual attack rates. Our model assumes that vaccination impacts susceptibility to infection but not infectiousness or the risk of developing disease. We parameterize the baseline model (without vaccination) based on literature estimates and the ability to recover observed epidemiological patterns. We then simulate this model under seven different potential vaccine formulations, initially assuming only pediatric vaccination. While we find that increases in coverage result in declines in annual norovirus attack rates for all formulations considered, we also find that vaccine formulations that include genotype GII.4 would be most effective at lowering overall norovirus attack rates. Inclusion of additional genotypes in a vaccine would further lower attack rates but more incrementally, with the addition of GI.3, GII.2, GII.3, and GII.6 together having a similar impact to that of GII.4 alone on reducing overall norovirus incidence. We further find that transient dynamics are expected for 10-20 years following roll-out with any pediatric vaccine. During this time, there may be unanticipated changes in genotype circulation patterns, although long-term increases in non-vaccine genotype attack rates above baseline levels are not expected. Finally, we anticipate that annual vaccination of older-aged individuals with a GII.4-containing vaccine can, under certain conditions but not others, provide appreciable direct benefits to individuals in this age group beyond what pediatric vaccination affords. Together, our results indicate that there is a clear population-level benefit of primary pediatric vaccination with a GII.4-inclusive norovirus vaccine plus incremental value of other genotypes, with additional direct benefits of annual vaccination to older adults provided that vaccination results in a considerable (multi-month) duration of broadly protective immunity to infection. More empirical studies are needed to validate the structure of the model and refine its parameterization, both of which affect projections of vaccine impact.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100842"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672569","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":"Work absenteeism across economic activity sectors and its association with COVID-19-like illness prevalence in the Netherlands, 2020–2023","authors":"Hester Korthals Altes ,&nbsp;Jan Van De Kassteele ,&nbsp;Bram Wisse ,&nbsp;Maria Xiridou ,&nbsp;Albert Jan Van Hoek ,&nbsp;Jacco Wallinga","doi":"10.1016/j.epidem.2025.100841","DOIUrl":"10.1016/j.epidem.2025.100841","url":null,"abstract":"<div><div>The monitoring of work absenteeism can inform pandemic decision making, besides the surveillance of disease end-points like mortality and intensive care bed occupancy. For instance, high disease prevalence accompanied by elevated levels of absenteeism in the healthcare sector will increase the strain on the health care system, and may necessitate adaptation of the control measures. This highlights the need to assess the association between COVID-19 disease prevalence and absenteeism in relevant economic sectors. We initiated the comprehensive monitoring and analysis of work absenteeism and developed an autoregressive time series model which combined COVID-19 prevalence as measured through syndromic surveillance, with absenteeism across various economic activity sectors in the Netherlands. The analysis was updated regularly and shared with policy makers. Overall, prevalence of COVID-19-like illnesses was the most important contributor to variation in absenteeism over the period November 2020-May 2023, with absenteeism rates varying markedly between activity sectors. Of the sectors well-covered by the absenteeism database, the Education and Logistics sectors showed the greatest contribution of a seasonal pattern independent of COVID-19 to absenteeism.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100841"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572646","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":"Epidemiology and environmental risks of antibiotic resistant Enterobacterales isolates in different aquatic matrices from North-Western Romania","authors":"Anca Farkas ,&nbsp;Rahela Carpa ,&nbsp;Edina Szekeres ,&nbsp;Adela Teban-Man ,&nbsp;Cristian Coman ,&nbsp;Anca Butiuc-Keul","doi":"10.1016/j.epidem.2025.100852","DOIUrl":"10.1016/j.epidem.2025.100852","url":null,"abstract":"<div><div>The most menacing sources of environmental contamination with antibiotic resistant bacteria are effluents derived from anthropic activities. Even when wastewater treatment processes are implemented, conventional methods are not able to completely retain the antibiotic resistance determinants. We propose an antibiotic resistance risk assessment, incorporating the characterisation of ARB, ARGs and MGEs in different environmental compartments.</div><div>Antibiotic susceptibility testing of 678 <em>Enterobacterales</em> isolates revealed an increased degree of intrinsic resistance to erythromycin (77.9 %), high level of resistance to ampicillin (39.7 %), low frequency of carbapenem resistance (2.36 %), and a percentage of 34.4 % MDR strains. The most frequent resistance determinants were <em>bla</em>_{<em>TEM-1</em>} (26.5 %) and <em>tetA</em> (8.26 %), while the <em>intI1</em> gene was found in 7.37 % of isolates. Resistant <em>Enterobacterales</em> from aquatic matrices with different degrees of contamination were identified as <em>Citrobacter</em> spp. (n = 46), <em>Enterobacter</em> spp. (n = 35), <em>Klebsiella</em> spp. (n = 54) and <em>Escherichia coli</em> (n = 107). A strong statistical correlation was observed between the presence of <em>intI1</em> and the ARG index (0.768) in resistant <em>Enterobacter</em> spp.</div><div>Distinct clustering of strains was not observed across different environmental matrices, especially in those directly impacted by human-derived bacteria. Also, distribution of ARB patterns and diversity of ARGs was stable from the taxonomic perspective. Dendrogram analysis based on ERIC-PCR profiles confirmed the presence of strains with identical DNA fingerprints in non-related aquatic ecosystems. The epidemiology of resistant <em>Citrobacter, Enterobacter, Klebsiella</em> and <em>Escherichia</em> isolates confirmed an extensive migration and environmental dispersion of strains with human health significance, particularly important for water resources.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100852"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911852","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":"Learning associations of COVID-19 hospitalizations with wastewater viral signals by Markov modulated models","authors":"K. Ken Peng ,&nbsp;Charmaine B. Dean ,&nbsp;X. Joan Hu ,&nbsp;Robert Delatolla","doi":"10.1016/j.epidem.2025.100840","DOIUrl":"10.1016/j.epidem.2025.100840","url":null,"abstract":"<div><div>Recent research highlights a strong correlation between COVID-19 hospitalizations and wastewater viral signals. Increases in wastewater viral signals may be early warnings of increases in hospital admissions. That indicates a promising opportunity to assess and predict the burden of infectious diseases and has driven the widespread adoption and development of wastewater monitoring tools by public health organizations. Previous studies utilize distributed lag models to explore associations of COVID-19 hospitalizations with lagged SARS-CoV-2 wastewater viral signals. However, the conventional distributed lag models assume the duration time of the lag to be fixed, which is not always plausible. This paper presents Markov-modulated models with distributed lasting time, treating the duration of the lag as a random variable defined by a hidden process. We evaluate exposure effects over the duration time and estimate the distribution of the lasting time using the wastewater data and COVID-19 hospitalization records from Ottawa, Canada during June 2020 to November 2022. The different COVID-19 pandemic waves are accommodated in the statistical learning. Moreover, two strategies for comparing the associations over different time intervals are exemplified using the Ottawa data. Of note, the proposed Markov modulated models, an extension of distributed lag models, are potentially applicable to many different problems where the lag time is not fixed.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100840"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666047","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":"Explaining the stable coexistence of drug-resistant and -susceptible pathogens: the resistance acquisition purifying selection model","authors":"Pleuni S. Pennings","doi":"10.1016/j.epidem.2025.100848","DOIUrl":"10.1016/j.epidem.2025.100848","url":null,"abstract":"<div><div>Drug resistance is a problem in many pathogens. While overall, levels of resistance have risen in recent decades, there are many examples where after an initial rise, levels of resistance have stabilized. The stable coexistence of resistance and susceptibility has proven hard to explain – in most evolutionary models, either resistance or susceptibility ultimately “wins” and takes over the population. Here, we show that a simple model, mathematically akin to mutation-selection balance theory, can explain several key observations about drug resistance: (1) the stable coexistence of resistant and susceptible strains (2) at levels that depend on population-level drug usage and (3) with resistance often due to many different strains (resistance is present on many different genetic backgrounds). The model is applicable to resistance due to both mutations and horizontal gene transfer (HGT). It predicts that new resistant strains should continuously appear (through mutation or HGT and positive selection within treated hosts) and disappear (due to a fitness cost of resistance). The result is that while resistance is stable, <em>which</em> strains carry resistance is constantly changing. We used data from a longitudinal genomic study on <em>E. coli</em> in Norway to test this prediction for resistance to five different drugs and found that, consistent with the model, most resistant strains indeed disappear quickly after they appear in the dataset. Having a model that explains the dynamics of drug resistance will allow us to plan science-backed interventions to reduce the burden of drug resistance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100848"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907266","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}