Epidemics最新文献

{"title":"A method to estimate the serial interval distribution under partially-sampled data","authors":"Kurnia Susvitasari,&nbsp;Paul Tupper,&nbsp;Jessica E. Stockdale,&nbsp;Caroline Colijn","doi":"10.1016/j.epidem.2023.100733","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100733","url":null,"abstract":"<div><p>The serial interval of an infectious disease is an important variable in epidemiology. It is defined as the period of time between the symptom onset times of the infector and infectee in a direct transmission pair. Under partially sampled data, purported infector–infectee pairs may actually be separated by one or more unsampled cases in between. Misunderstanding such pairs as direct transmissions will result in overestimating the length of serial intervals. On the other hand, two cases that are infected by an unseen third case (known as coprimary transmission) may be classified as a direct transmission pair, leading to an underestimation of the serial interval. Here, we introduce a method to jointly estimate the distribution of serial intervals factoring in these two sources of error. We simultaneously estimate the distribution of the number of unsampled intermediate cases between purported infector–infectee pairs, as well as the fraction of such pairs that are coprimary. We also extend our method to situations where each infectee has multiple possible infectors, and show how to factor this additional source of uncertainty into our estimates. We assess our method’s performance on simulated data sets and find that our method provides consistent and robust estimates. We also apply our method to data from real-life outbreaks of four infectious diseases and compare our results with published results. With similar accuracy, our method of estimating serial interval distribution provides unique advantages, allowing its application in settings of low sampling rates and large population sizes, such as widespread community transmission tracked by routine public health surveillance.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100733"},"PeriodicalIF":3.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000695/pdfft?md5=458166bd7330f9768060ac0ef73cebd5&pid=1-s2.0-S1755436523000695-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138490665","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":"Variation in pneumococcal invasiveness metrics is driven by serotype carriage duration and initial risk of disease","authors":"Benjamin J. Metcalf ,&nbsp;Kristofer Wollein Waldetoft ,&nbsp;Bernard W. Beall ,&nbsp;Sam P. Brown","doi":"10.1016/j.epidem.2023.100731","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100731","url":null,"abstract":"<div><p><em>Streptococcus pneumoniae</em> is an opportunistic pathogen that, while usually carried asymptomatically, can cause severe invasive diseases like meningitis and bacteremic pneumonia. A central goal in <em>S. pneumoniae</em> public health management is to identify which serotypes (immunologically distinct strains) pose the most risk of invasive disease. The most common invasiveness metrics use cross-sectional data (<em>i.e.</em>, invasive odds ratios (IOR)), or longitudinal data (<em>i.e.</em>, attack rates (AR)). To assess the reliability of these metrics we developed an epidemiological model of carriage and invasive disease. Our mathematical analyses illustrate qualitative failures with the IOR metric (<em>e.g.</em>, IOR can decline with increasing invasiveness parameters). Fitting the model to both longitudinal and cross-sectional data, our analysis supports previous work indicating that invasion risk is maximal at or near time of colonization. This pattern of early invasive disease risk leads to substantial (up to 5-fold) biases when estimating underlying differences in invasiveness from IOR metrics, due to the impact of carriage duration on IOR. Together, these results raise serious concerns with the IOR metric as a basis for public health decision-making and lend support for multiple alternate metrics including AR.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100731"},"PeriodicalIF":3.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000671/pdfft?md5=30db509b0692cd94b01b5d29e76719cd&pid=1-s2.0-S1755436523000671-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138467683","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 variations of SIkJalpha model for COVID-19 forecasting and scenario projections","authors":"Ajitesh Srivastava","doi":"10.1016/j.epidem.2023.100729","DOIUrl":"10.1016/j.epidem.2023.100729","url":null,"abstract":"<div><p>We proposed the SIkJalpha model at the beginning of the COVID-19 pandemic (early 2020). Since then, as the pandemic evolved, more complexities were added to capture crucial factors and variables that can assist with projecting desired future scenarios. Throughout the pandemic, multi-model collaborative efforts have been organized to predict short-term outcomes (cases, deaths, and hospitalizations) of COVID-19 and long-term scenario projections. We have been participating in five such efforts. This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic. Specifically, we show that the SIkJalpha model is an approximation of a class of epidemiological models. We demonstrate how the model can be used to incorporate various complexities, including under-reporting, multiple variants, waning of immunity, and contact rates, and to generate probabilistic outputs.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100729"},"PeriodicalIF":3.8,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000658/pdfft?md5=929f79386e57f7e3861ecdc50ce83ff4&pid=1-s2.0-S1755436523000658-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048293","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":"Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021","authors":"Moses C. Kiti ,&nbsp;Obianuju G. Aguolu ,&nbsp;Alana Zelaya ,&nbsp;Holin Y. Chen ,&nbsp;Noureen Ahmed ,&nbsp;Jonathan Batross ,&nbsp;Carol Y. Liu ,&nbsp;Kristin N. Nelson ,&nbsp;Samuel M. Jenness ,&nbsp;Alessia Melegaro ,&nbsp;Faruque Ahmed ,&nbsp;Fauzia Malik ,&nbsp;Saad B. Omer ,&nbsp;Ben A. Lopman","doi":"10.1016/j.epidem.2023.100727","DOIUrl":"10.1016/j.epidem.2023.100727","url":null,"abstract":"<div><p>Non-pharmaceutical interventions minimize social contacts, hence the spread of respiratory pathogens such as influenza and SARS-CoV-2. Globally, there is a paucity of social contact data from the workforce. In this study, we quantified two-day contact patterns among USA employees. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic self-kept diaries. Data were collected over 4 rounds from 2020 to 2021 during the COVID-19 pandemic. Mean (standard deviation) contacts reported by 1456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April–June 2020), 2 (November 2020–January 2021), 3 (June–August 2021), and 4 (November–December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2–4. We then modeled SARS-CoV-2 transmission at home, work, and community settings. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained exceptionally low in work settings. To accurately parameterize models of infection transmission and control, we need empirical social contact data that capture human mixing behavior across time.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100727"},"PeriodicalIF":3.8,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000634/pdfft?md5=74164db0f3515598af291f136f59e369&pid=1-s2.0-S1755436523000634-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211560","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 utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States","authors":"Nicholas G. Reich ,&nbsp;Yijin Wang ,&nbsp;Meagan Burns ,&nbsp;Rosa Ergas ,&nbsp;Estee Y. Cramer ,&nbsp;Evan L. Ray","doi":"10.1016/j.epidem.2023.100728","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100728","url":null,"abstract":"<div><p>Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100728"},"PeriodicalIF":3.8,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000646/pdfft?md5=501f8c5a916c41a194955789cddb3e5e&pid=1-s2.0-S1755436523000646-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109127541","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":"Estimation of waning vaccine effectiveness from population-level surveillance data in multi-variant epidemics","authors":"Hiroaki Murayama ,&nbsp;Akira Endo ,&nbsp;Shouto Yonekura","doi":"10.1016/j.epidem.2023.100726","DOIUrl":"10.1016/j.epidem.2023.100726","url":null,"abstract":"<div><p>Monitoring time-varying vaccine effectiveness (e.g., due to waning of immunity and the emergence of novel variants) provides crucial information for outbreak control. Existing studies of time-varying vaccine effectiveness have used individual-level data, most importantly dates of vaccination and variant classification, which are often not available in a timely manner or from a wide range of population groups. We present a novel Bayesian framework for estimating the waning of variant-specific vaccine effectiveness in the presence of multi-variant circulation from population-level surveillance data. Applications to simulated outbreaks and the COVID-19 epidemic in Japan are also presented. Our results show that variant-specific waning vaccine effectiveness estimated from population-level surveillance data could approximately reproduce the estimates from previous test-negative design studies, allowing for rapid, if crude, assessment of the epidemic situation before fine-scale studies are made available.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100726"},"PeriodicalIF":3.8,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000622/pdfft?md5=ea726add6890caef681ab98cd068361d&pid=1-s2.0-S1755436523000622-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523072","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":"Modelling flock heterogeneity in the transmission of peste des petits ruminants virus and its impact on the effectiveness of vaccination for eradication","authors":"Bethan Savagar ,&nbsp;Bryony A. Jones ,&nbsp;Mark Arnold ,&nbsp;Martin Walker ,&nbsp;Guillaume Fournié","doi":"10.1016/j.epidem.2023.100725","DOIUrl":"10.1016/j.epidem.2023.100725","url":null,"abstract":"<div><p>Peste des petits ruminants (PPR) is an acute infectious disease of small ruminants targeted for global eradication by 2030. The Global Strategy for Control and Eradication (GSCE) recommends mass vaccination targeting 70% coverage of small ruminant populations in PPR-endemic regions. These small ruminant populations are diverse with heterogeneous mixing patterns that may influence PPR virus (PPRV) transmission dynamics. This paper evaluates the impact of heterogeneous mixing on (i) PPRV transmission and (ii) the likelihood of different vaccination strategies achieving PPRV elimination, including the GSCE recommended strategy. We develop models simulating heterogeneous transmission between hosts, including a metapopulation model of PPRV transmission between villages in lowland Ethiopia fitted to serological data. Our results demonstrate that although heterogeneous mixing of small ruminant populations increases the instability of PPRV transmission—increasing the chance of fadeout in the absence of intervention—a vaccination coverage of 70% may be insufficient to achieve elimination if high-risk populations are not targeted. Transmission may persist despite very high vaccination coverage (&gt;90% small ruminants) if vaccination is biased towards more accessible but lower-risk populations such as sedentary small ruminant flocks. These results highlight the importance of characterizing small ruminant mobility patterns and identifying high-risk populations for vaccination and support a move towards targeted, risk-based vaccination programmes in the next phase of the PPRV eradication programme. Our modelling approach also illustrates a general framework for incorporating heterogeneous mixing patterns into models of directly transmitted infectious diseases where detailed contact data are limited. This study improves understanding of PPRV transmission and elimination in heterogeneous small ruminant populations and should be used to inform and optimize the design of PPRV vaccination programmes.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100725"},"PeriodicalIF":3.8,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000610/pdfft?md5=5095bc68ce3f1a1597706b90994e1832&pid=1-s2.0-S1755436523000610-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71487939","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":"Mathematical methods for scaling from within-host to population-scale in infectious disease systems","authors":"James W.G. Doran ,&nbsp;Robin N. Thompson ,&nbsp;Christian A. Yates ,&nbsp;Ruth Bowness","doi":"10.1016/j.epidem.2023.100724","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100724","url":null,"abstract":"<div><p>Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100724"},"PeriodicalIF":3.8,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000609/pdfft?md5=42f11d0050552382ff9757df7e1a40db&pid=1-s2.0-S1755436523000609-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134654324","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":"Modelling outbreak response impact in human vaccine-preventable diseases: A systematic review of differences in practices between collaboration types before COVID-19","authors":"James M. Azam ,&nbsp;Xiaoxi Pang ,&nbsp;Elisha B. Are ,&nbsp;Juliet R.C. Pulliam ,&nbsp;Matthew J. Ferrari","doi":"10.1016/j.epidem.2023.100720","DOIUrl":"10.1016/j.epidem.2023.100720","url":null,"abstract":"<div><h3>Background:</h3><p>Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination.</p></div><div><h3>Methods:</h3><p>We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices.</p></div><div><h3>Results:</h3><p>Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013–2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation.</p></div><div><h3>Conclusion:</h3><p>The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100720"},"PeriodicalIF":3.8,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000567/pdfft?md5=3026204558e1fdbce504ec75af88ca46&pid=1-s2.0-S1755436523000567-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72015916","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":"Trypanosoma cruzi infection in dogs along the US-Mexico border: R0 changes with vector species composition","authors":"Luis Fernando Chaves ,&nbsp;Alyssa C. Meyers ,&nbsp;Carolyn L. Hodo ,&nbsp;John P. Sanders ,&nbsp;Rachel Curtis-Robles ,&nbsp;Gabriel L. Hamer ,&nbsp;Sarah A. Hamer","doi":"10.1016/j.epidem.2023.100723","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100723","url":null,"abstract":"<div><p>Infection with <em>Trypanosoma cruzi,</em> etiological agent of Chagas disease, is common in US government working dogs along the US-Mexico border. This 3145 km long border comprises four states: Texas (TX), New Mexico (NM), Arizona (AZ) and California (CA) with diverse ecosystems and several triatomine (a.k.a., kissing bug) species, primary vectors of <em>T. cruzi</em> in this region. The kissing bug (Heteroptera: Reduviidae) community ranging from CA to TX includes <em>Triatoma protracta</em> (Uhler), <em>Triatoma recurva</em> (Stål) and <em>Triatoma rubida</em> (Uhler) and becomes dominated by <em>Triatoma gerstaeckeri</em> Stål in TX. Here, we ask if <em>T. cruzi</em> infection dynamics in dogs varies along this border region, potentially reflecting changes in vector species and their vectorial capacity. Using reversible catalytic models of infection, where seropositivity can be lost, we estimated an <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> (Estimate ± S.E.) of 1.192 ± 0.084 for TX and NM. In contrast, seropositivity decayed to zero as dogs aged in AZ and CA. These results suggest that dogs are likely infected by <em>T. cruzi</em> during their training in western TX, with a force of infection large enough for keeping <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> above 1, i.e., the disease endemically established, in TX and NM. In AZ and CA, a lower force of infection, probably associated with different vector species communities and associated vectorial capacity and/or different lineages of <em>T. cruzi</em>, results in dogs decreasing their seropositivity with age.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100723"},"PeriodicalIF":3.8,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000592/pdfft?md5=57f445b1b533dc6f207553a8bb34d8fe&pid=1-s2.0-S1755436523000592-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92065947","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}