Epidemics最新文献

{"title":"Modeling the transmission dynamics of African swine fever virus within commercial swine barns: Quantifying the contribution of multiple transmission pathways","authors":"Aniruddha Deka ,&nbsp;Jason A. Galvis ,&nbsp;Christian Fleming ,&nbsp;Maryam Safari ,&nbsp;Chi-An Yeh ,&nbsp;Gustavo Machado","doi":"10.1016/j.epidem.2025.100828","DOIUrl":"10.1016/j.epidem.2025.100828","url":null,"abstract":"<div><div>The transmission of African swine fever virus (ASFV) within swine barns occurs through direct and indirect pathways. Identifying and quantifying the roles of ASFV dissemination within barns is crucial for developing disease control strategies. We created a stochastic transmission model to examine the ASFV dissemination dynamics through transmission routes within commercial swine barns. We consider seven transmission routes at three disease dynamics levels: within-pens, between-pens, and within-room transmission, along with the transfer of pigs between pens within rooms. We simulated ASFV spread within barns of various sizes and layouts from rooms with a median of 32 pens (IQR: 28-40), where each pen housed a median of 34 pigs (IQR: 29-36). Our model enables tracking the viral load in each pen and monitoring the disease status at the pen level. Results show that between-pen transmission pathways exhibited the highest contribution in spread, accounting for 66.76%, whereas within-pen and within-room pathways account for 26.12% and 7.12%, respectively. Nose-to-nose contact between pens was the primary dissemination route, comprising an average of 46.04%. On the other hand, aerosol transmission within pens had the lowest contribution, accounting for less than 1%. Furthermore, we show that the daily transfer of pigs between pens did not impact the spread of ASFV. On average, at the room level, the combined approach of passive daily surveillance and mortality-focused surveillance enabled ASFV detection within 18 (IQR: 16-19) days. The model allows us to monitor the viral load variation across the room over time, revealing that most of the viral load accumulates in pens closer to the exhaust fans after a month. This work significantly deepens our understanding of ASFV spread within commercial swine production farms in the U.S. and highlights the main transmission pathways that should be prioritized when implementing ASFV countermeasure actions at the room level.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100828"},"PeriodicalIF":3.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878659","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":"Modelling the interplay between responsive individual vaccination decisions and the spread of SARS-CoV-2","authors":"Karina Wallrafen-Sam ,&nbsp;Maria Garcia Quesada ,&nbsp;Benjamin A. Lopman ,&nbsp;Samuel M. Jenness","doi":"10.1016/j.epidem.2025.100831","DOIUrl":"10.1016/j.epidem.2025.100831","url":null,"abstract":"<div><h3>Background</h3><div>COVID-19 vaccine hesitancy proved to be a major barrier to higher uptake, but it is unclear whether interventions targeting hesitancy could result in substantial prevention benefits. Epidemic models that represent vaccine decision-making psychology can provide insight into the potential impact of vaccine promotion interventions in the context of the COVID-19 pandemic and future epidemics of vaccine-preventable diseases.</div></div><div><h3>Methods</h3><div>We coupled a network- and agent-based model of SARS-CoV-2 transmission with a social-psychological vaccination decision-making model in which vaccine side effects and breakthrough infections could “nudge” individuals towards vaccine resistance while spikes in COVID-19 hospitalizations could nudge them towards vaccine willingness. This model was parameterized and calibrated to represent the COVID-19 epidemic in Georgia, USA from January 2021 to August 2022. We modelled counterfactual scenarios in which increases to resistant-to-willing nudges were combined with decreases to willing-to-resistant nudges. We compared cumulative vaccine doses administered, SARS-CoV-2 incidence, and COVID-related deaths across scenarios.</div></div><div><h3>Results</h3><div>Increasing the probability of hospitalization-prompted resistant-to-willing nudges increased vaccine uptake by as much as 5.4 % and decreased SARS-CoV-2 incidence by as much as 4.0 %. In contrast, decreasing the probability of breakthrough infection-related willing-to-resistant nudges had a negligible impact on further vaccination and disease outcomes.</div></div><div><h3>Conclusions</h3><div>Vaccine promotion interventions that address community-level factors influencing decision-making may have a greater ability to avert SARS-CoV-2 infections than those targeted to individual vaccination and infection history. Additionally, reactive vaccine promotion interventions may have only limited prevention benefits in the short term, suggesting that attention should be paid to formulating interventions that accurately anticipate the case curve.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100831"},"PeriodicalIF":3.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895672","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":"Accounting for the geometry of the respiratory tract in viral infections","authors":"Thomas Williams ,&nbsp;James M. McCaw ,&nbsp;James M. Osborne","doi":"10.1016/j.epidem.2025.100829","DOIUrl":"10.1016/j.epidem.2025.100829","url":null,"abstract":"<div><div>Increasingly, experimentalists and modellers alike have come to recognise the important role of spatial structure in infection dynamics. Almost invariably, spatial computational models of viral infections — as with <em>in vitro</em> experimental systems — represent the tissue as wide and flat, which is often assumed to be representative of the entire affected tissue within the host. However, this assumption fails to take into account the distinctive geometry of the respiratory tract in the context of viral infections. The respiratory tract is characterised by a tubular, branching structure, and moreover is spatially heterogeneous: deeper regions of the lung are composed of far narrower airways and are associated with more severe infection. Here, we extend a typical multicellular model of viral dynamics to account for two essential features of the geometry of the respiratory tract: the tubular structure of airways, and the branching process between airway generations. We show that, with this more realistic tissue geometry, the dynamics of infection are substantially changed compared to standard computational and experimental approaches, and that the resulting model is equipped to tackle important biological phenomena that do not arise in a flat host tissue, including viral lineage dynamics, and heterogeneity in immune responses to infection in different regions of the respiratory tree. Our findings suggest aspects of viral dynamics which current <em>in vitro</em> systems may be insufficient to describe, and points to several features of respiratory infections which can be experimentally assessed.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100829"},"PeriodicalIF":3.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907492","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":"Estimating social contact rates for the COVID-19 pandemic using Google mobility and pre-pandemic contact surveys","authors":"Em Prestige ,&nbsp;Pietro Coletti ,&nbsp;Jantien Backer ,&nbsp;Nicholas G. Davies ,&nbsp;W. John Edmunds ,&nbsp;Christopher I. Jarvis","doi":"10.1016/j.epidem.2025.100830","DOIUrl":"10.1016/j.epidem.2025.100830","url":null,"abstract":"<div><div>During the COVID-19 pandemic, aggregated mobility data was frequently used to estimate changing social contact rates. By taking pre-pandemic contact matrices, and transforming these using pandemic-era mobility data, infectious disease modellers attempted to predict the effect of large-scale behavioural changes on contact rates. This study explores the most accurate method for this transformation, using pandemic-era contact surveys as ground truth. We compared four methods for scaling synthetic contact matrices: two using fitted regression models and two using “naïve” mobility or mobility squared models. The regression models were fitted using the CoMix contact survey and Google mobility data from the UK over March 2020 – March 2021. The four models were then used to scale synthetic contact matrices—a representation of pre-pandemic behaviour—using mobility data from the UK, Belgium and the Netherlands to predict the number of contacts expected in “work” and “other” settings for a given mobility level. We then compared partial reproduction numbers estimated from the four models with those calculated directly from CoMix contact matrices across the three countries. The accuracy of each model was assessed using root mean squared error. The fitted regression models had substantially more accurate predictions than the naïve models, even when models were applied to out-of-sample data from the UK, Belgium and the Netherlands. Across all countries investigated, the linear fitted regression model was the most accurate and the naïve model using mobility alone was the least accurate. When attempting to estimate social contact rates during a pandemic without the resources available to conduct contact surveys, using a model fitted to data from another pandemic context is likely to be an improvement over using a “naïve” model based on mobility data alone. If a naïve model is to be used, mobility squared may be a better predictor of contact rates than mobility per se.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100830"},"PeriodicalIF":3.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877350","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":"Detection of surges of SARS-Cov-2 using nonparametric Hawkes models","authors":"Sophie Phillips ,&nbsp;George Mohler ,&nbsp;Frederic Schoenberg","doi":"10.1016/j.epidem.2025.100824","DOIUrl":"10.1016/j.epidem.2025.100824","url":null,"abstract":"<div><div>Hawkes point process models have been shown to forecast the number of daily new cases of epidemic diseases, including SARS-CoV-2 (Covid-19), with high accuracy. Here, we explore how accurately Hawkes models forecast surges of Covid-19 in the United States. We use Hawkes models to estimate the effective reproduction rate <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> and transmission density parameters for Covid-19 case counts in each of the 50 United States, then forecast <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> in future weeks with simple exponential smoothing. A classifier based on <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>&gt;</mo><mi>x</mi></mrow></math></span> is applied to predict upcoming surges in cases each week from August 2020 to December 2021, using only data available up to that week. At false alarm rates below 5%, the forecasts based on <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> are correct more often than forecasts based on smoothing the raw case count data, achieving a maximum accuracy of 90% with <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>&gt;</mo><mn>1</mn><mo>.</mo><mn>39</mn></mrow></math></span>. The optimal decision boundary uses a combination of <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> and observed data.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100824"},"PeriodicalIF":3.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830099","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 effect of COVID-19 vaccination on change in contact and implications for transmission","authors":"Carol Y. Liu ,&nbsp;Aaron Siegler ,&nbsp;Patrick Sullivan ,&nbsp;Samuel M. Jenness ,&nbsp;Stefan Flasche ,&nbsp;Benjamin Lopman ,&nbsp;Kristin Nelson","doi":"10.1016/j.epidem.2025.100827","DOIUrl":"10.1016/j.epidem.2025.100827","url":null,"abstract":"<div><h3>Background</h3><div>Monitoring human behavior as epidemic intelligence can critically complement traditional surveillance systems during epidemics. Retrospective analysis of novel behavioral data streams initiated during the COVID-19 pandemic help illustrate their utility. During the pandemic, behavior changed rapidly and was increasingly influenced by individual choice in response to changes such as newly available vaccines. Vaccines provided substantial protection against severe disease and deaths; however, their effect on behavior is understudied and it is unclear if vaccine effects against infection fully offset relaxation of social distancing behaviors.</div></div><div><h3>Methods &amp; results</h3><div>We analyzed data from a longitudinal cohort sampled from U.S. households that measured contact rates, risk mitigation and COVID-19 vaccination status between August 2020-April 2022. Contact rates universally increased across survey rounds among all sociodemographic groups, but unvaccinated individuals had persistently higher contact rates. Using a multilevel generalized linear mixed effects model, we found that individuals who newly completed a primary vaccine series had an additional increase of 1.93 (95 % CI: 0.27–3.59) contacts compared to individuals who remained unvaccinated. Using observed contact rates to estimate transmission, we found that observed increases in contact rates were not fully offset by vaccine protection against infection, but transmission was still maintained below levels without distancing and vaccination despite clusters of individuals with high contact and no vaccination.</div></div><div><h3>Conclusion</h3><div>We estimated changes in contact rates following vaccination and inferred the joint effect of changes in vaccination and contacts on population-level transmission, finding that observed increases in contact rates were not fully offset by vaccine effects. Our work highlights the potential utility of ongoing longitudinal monitoring of contact patterns during epidemics.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100827"},"PeriodicalIF":3.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880911","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":"Optimizing influenza vaccine allocation by age using cost-effectiveness analysis: A comparison of 6720 vaccination program scenarios in children and adults in Belgium","authors":"Regina Manansala ,&nbsp;Joke Bilcke ,&nbsp;Lander Willem ,&nbsp;Niel Hens ,&nbsp;Philippe Beutels","doi":"10.1016/j.epidem.2025.100826","DOIUrl":"10.1016/j.epidem.2025.100826","url":null,"abstract":"<div><h3>Background</h3><div>Many European countries prioritize groups for annual influenza vaccination based on risk of severe disease and death. This has resulted in relatively high influenza vaccination coverage in older adults in Belgium. However, coverage is much lower in younger adults and negligible in children. Children and young adults are known to play a major role in the transmission dynamics of influenza. Thus, an important policy question is how influenza vaccines can be optimally allocated across age groups, taking indirect effects into account.</div></div><div><h3>Methods</h3><div>We adapted a dynamic transmission model to reproduce influenza seasonality in Belgium comparing 6720 mutually exclusive vaccination options, including current practice. Vaccination options were defined by different combinations of coverage level changes in nine age groups. We performed an economic evaluation comparing all options from a healthcare payer perspective. Quality-adjusted life-years (QALYs) were the primary health outcome. We expressed parametric uncertainty using the Incremental Net Monetary Benefits (INMB) approach.</div></div><div><h3>Results</h3><div>Of all the vaccination options considered, over 90 % dominated the current Belgian vaccination strategy in terms of cost-effectiveness. Children were estimated to contribute a substantial indirect protective effect to the overall population. The most cost-effective program increases vaccination coverage rates for children to 90 %, 50–64 years old to 48 %, and 65–74 years old to 75 %.</div></div><div><h3>Discussion</h3><div>Overall QALY gains can be maximized in seasonal influenza vaccination programs at acceptable costs by achieving high vaccination coverage in childhood age groups. Programmatic and ethical concerns towards such an implementation in the Belgian context need to be separately considered.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100826"},"PeriodicalIF":3.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833472","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":"Machine learning approaches for real-time ZIP code and county-level estimation of state-wide infectious disease hospitalizations using local health system data","authors":"Tanvir Ahammed ,&nbsp;Md Sakhawat Hossain ,&nbsp;Christopher McMahan ,&nbsp;Lior Rennert","doi":"10.1016/j.epidem.2025.100823","DOIUrl":"10.1016/j.epidem.2025.100823","url":null,"abstract":"<div><div>The lack of conventional methods of estimating real-time infectious disease burden in granular regions inhibits timely and efficient public health response. Comprehensive data sources (e.g., state health department data) typically needed for such estimation are often limited due to 1) substantial delays in data reporting and 2) lack of geographic granularity in data provided to researchers. Leveraging real-time local health system data presents an opportunity to overcome these challenges. This study evaluates the effectiveness of machine learning and statistical approaches using local health system data to estimate current and previous COVID-19 hospitalizations in South Carolina. Random Forest models demonstrated consistently higher average median percent agreement accuracy compared to generalized linear mixed models for current weekly hospitalizations across 123 ZIP codes (72.29 %, IQR: 63.20–75.62 %) and 28 counties (76.43 %, IQR: 70.33–81.16 %) with sufficient health system coverage. To account for underrepresented populations in health systems, we combined Random Forest models with Classification and Regression Trees (CART) for imputation. The average median percent agreement was 61.02 % (IQR: 51.17–72.29 %) for all ZIP codes and 72.64 % (IQR: 66.13–77.69 %) for all counties. Median percent agreement for cumulative hospitalizations over the previous 6 months was 80.98 % (IQR: 68.99–89.66 %) for all ZIP codes and 81.17 % (IQR: 68.55–91.33 %) for all counties. These findings emphasize the effectiveness of utilizing real-time health system data to estimate infectious disease burden. Moreover, the methodologies developed in this study can be adapted to estimate hospitalizations for other diseases, offering a valuable tool for public health officials to respond swiftly and effectively to various health crises.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100823"},"PeriodicalIF":3.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815115","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":"Analysis insights to support the use of wastewater and environmental surveillance data for infectious diseases and pandemic preparedness","authors":"KM O’Reilly ,&nbsp;MJ Wade ,&nbsp;K. Farkas ,&nbsp;F. Amman ,&nbsp;A. Lison ,&nbsp;JD Munday ,&nbsp;J. Bingham ,&nbsp;ZE Mthombothi ,&nbsp;Z. Fang ,&nbsp;CS Brown ,&nbsp;RR Kao ,&nbsp;L. Danon","doi":"10.1016/j.epidem.2025.100825","DOIUrl":"10.1016/j.epidem.2025.100825","url":null,"abstract":"<div><div>Wastewater-based epidemiology is the detection of pathogens from sewage systems and the interpretation of these data to improve public health. Its use has increased in scope since 2020, when it was demonstrated that SARS-CoV-2 RNA could be successfully extracted from the wastewater of affected populations. In this <em>Perspective</em> we provide an overview of recent advances in pathogen detection within wastewater, propose a framework for identifying the utility of wastewater sampling for pathogen detection and suggest areas where analytics require development. Ensuring that both data collection and analysis are tailored towards key questions at different stages of an epidemic will improve the inference made. For analyses to be useful we require methods to determine the absence of infection, early detection of infection, reliably estimate epidemic trajectories and prevalence, and detect novel variants without reliance on consensus sequences. This research area has included many innovations that have improved the interpretation of collected data and we are optimistic that innovation will continue in the future.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100825"},"PeriodicalIF":3.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737878","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":"Does spatial information improve forecasting of influenza-like illness?","authors":"Gabrielle Thivierge ,&nbsp;Aaron Rumack ,&nbsp;F. William Townes","doi":"10.1016/j.epidem.2025.100820","DOIUrl":"10.1016/j.epidem.2025.100820","url":null,"abstract":"<div><div>Seasonal influenza forecasting is critical for public health and individual decision making. We investigate whether the inclusion of data about influenza activity in neighboring states can improve point predictions and distribution forecasting of influenza-like illness (ILI) in each US state using statistical regression models. Using CDC FluView ILI data from 2010–2019, we forecast weekly ILI in each US state with quantile, linear, and Poisson autoregressive models fit using different combinations of ILI data from the target state, neighboring states, and the US population-weighted average. Scoring with root mean squared error and weighted interval score indicated that the covariate sets including neighbors and/or the US weighted average ILI showed slightly higher accuracy than models fit only using lagged ILI in the target state, on average. Additionally, the improvement in performance when including neighbors was similar to the improvement when including the US average instead, suggesting the proximity of the neighboring states is not the driver of the slight increase in accuracy. There is also clear within-season and between-season variability in the effect of spatial information on prediction accuracy.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100820"},"PeriodicalIF":3.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714961","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}