Epidemics最新文献

{"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}

{"title":"Onset of infectiousness explains differences in transmissibility across Mycobacterium tuberculosis lineages","authors":"Etthel M. Windels ,&nbsp;Cecilia Valenzuela Agüí ,&nbsp;Bouke C. de Jong ,&nbsp;Conor J. Meehan ,&nbsp;Chloé Loiseau ,&nbsp;Galo A. Goig ,&nbsp;Michaela Zwyer ,&nbsp;Sonia Borrell ,&nbsp;Daniela Brites ,&nbsp;Sebastien Gagneux ,&nbsp;Tanja Stadler","doi":"10.1016/j.epidem.2025.100821","DOIUrl":"10.1016/j.epidem.2025.100821","url":null,"abstract":"<div><div><em>Mycobacterium tuberculosis</em> complex (MTBC) lineages show substantial variability in virulence, but the epidemiological consequences of this variability have not been studied in detail. Here, we aimed for a lineage-specific epidemiological characterization by applying phylodynamic models to genomic data from different countries, representing the most abundant MTBC lineages. Our results suggest that all lineages are associated with similar durations and levels of infectiousness, resulting in similar reproductive numbers. However, L1 and L6 are associated with a delayed onset of infectiousness, leading to longer periods between subsequent transmission events. Together, our findings highlight the role of MTBC genetic diversity in tuberculosis disease progression and transmission.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"51 ","pages":"Article 100821"},"PeriodicalIF":3.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674697","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":"Collaborative forecasting of influenza-like illness in Italy: The Influcast experience","authors":"Stefania Fiandrino ,&nbsp;Andrea Bizzotto ,&nbsp;Giorgio Guzzetta ,&nbsp;Stefano Merler ,&nbsp;Federico Baldo ,&nbsp;Eugenio Valdano ,&nbsp;Alberto Mateo Urdiales ,&nbsp;Antonino Bella ,&nbsp;Francesco Celino ,&nbsp;Lorenzo Zino ,&nbsp;Alessandro Rizzo ,&nbsp;Yuhan Li ,&nbsp;Nicola Perra ,&nbsp;Corrado Gioannini ,&nbsp;Paolo Milano ,&nbsp;Daniela Paolotti ,&nbsp;Marco Quaggiotto ,&nbsp;Luca Rossi ,&nbsp;Ivan Vismara ,&nbsp;Alessandro Vespignani ,&nbsp;Nicolò Gozzi","doi":"10.1016/j.epidem.2025.100819","DOIUrl":"10.1016/j.epidem.2025.100819","url":null,"abstract":"<div><div>Collaborative hubs that integrate multiple teams to generate ensemble projections and forecasts for shared targets are now regarded as state-of-the-art in epidemic predictive modeling. In this paper, we introduce Influcast, Italy’s first epidemic forecasting hub for influenza-like illness. During the 2023/2024 winter season, Influcast provided 20 rounds of forecasts, involving five teams and eight models to predict influenza-like illness incidence up to four weeks in advance at the national and regional administrative level. The individual forecasts were synthesized into an ensemble and benchmarked against a baseline model. Across all models, the ensemble most frequently ranks among the top performers at the national level considering different metrics and forecasting rounds. Additionally, the ensemble outperforms the baseline and most individual models across all regions. Despite a decline in absolute performance over longer horizons, the ensemble model outperformed the baseline in all considered horizons. These findings show the importance of multimodel forecasting hubs in producing reliable short-term influenza-like illnesses forecasts that can inform public health preparedness and mitigation strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100819"},"PeriodicalIF":3.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data","authors":"Austin G. Meyer ,&nbsp;Fred Lu ,&nbsp;Leonardo Clemente ,&nbsp;Mauricio Santillana","doi":"10.1016/j.epidem.2025.100816","DOIUrl":"10.1016/j.epidem.2025.100816","url":null,"abstract":"<div><div>Accurate, real-time forecasts of influenza hospitalizations would facilitate prospective resource allocation and public health preparedness. State-of-the-art machine learning methods are a promising approach to produce such forecasts, but they require extensive historical data to be properly trained. Unfortunately, data on influenza hospitalizations, for the 50 states in the United States, are only available since the beginning of 2020. In addition, the data are far from perfect as they were under-reported for several months before health systems began consistently submitting their data. To address these issues, we propose a transfer learning approach. We extend the currently available two-season dataset for state-level influenza hospitalizations by an additional ten seasons. Our method leverages influenza-like illness (ILI) data to infer historical estimates of influenza hospitalizations. This data augmentation enables the implementation of advanced machine learning techniques, multi-horizon training, and an ensemble of models to improve hospitalization forecasts. We evaluated the performance of our machine learning approaches by prospectively producing forecasts for future weeks and submitting them in real time to the Centers for Disease Control and Prevention FluSight challenges during two seasons: 2022–2023 and 2023–2024. Our methodology demonstrated good accuracy and reliability, achieving a fourth place finish (among 20 participating teams) in the 2022–23 and a second place finish (among 20 participating teams) in the 2023–24 CDC FluSight challenges. Our findings highlight the utility of data augmentation and knowledge transfer in the application of machine learning models to public health surveillance where only limited historical data is available.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100816"},"PeriodicalIF":3.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464330","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 COVID-19 in the North American region with a metapopulation network and Kalman filter","authors":"Matteo Perini ,&nbsp;Teresa K. Yamana ,&nbsp;Marta Galanti ,&nbsp;Jiyeon Suh ,&nbsp;Roselyn Kaondera-Shava ,&nbsp;Jeffrey Shaman","doi":"10.1016/j.epidem.2025.100818","DOIUrl":"10.1016/j.epidem.2025.100818","url":null,"abstract":"<div><h3>Background</h3><div>Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions.</div></div><div><h3>Methods</h3><div>We developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis.</div></div><div><h3>Results</h3><div>This model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries.</div></div><div><h3>Conclusions</h3><div>The metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100818"},"PeriodicalIF":3.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076069","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":"Estimating nosocomial transmission of micro-organisms in hospital settings using patient records and culture data","authors":"Jaime Cascante Vega ,&nbsp;Rami Yaari ,&nbsp;Tal Robin ,&nbsp;Lingsheng Wen ,&nbsp;Jason Zucker ,&nbsp;Anne-Catrin Uhlemann ,&nbsp;Sen Pei ,&nbsp;Jeffrey Shaman","doi":"10.1016/j.epidem.2025.100817","DOIUrl":"10.1016/j.epidem.2025.100817","url":null,"abstract":"<div><div>Pathogenic bacteria are a major threat to patient health in hospitals. Here we leverage electronic health records from a major New York City hospital system collected during 2020–2021 to support simulation inference of nosocomial transmission and pathogenic bacteria detection using an agent-based model (ABM). The ABM uses these data to inform simulation of importation from the community, nosocomial transmission, and patient spontaneous decolonization of bacteria. We additionally use patient clinical culture results to inform an observational model of detection of the pathogenic bacteria. The model is coupled with a Bayesian inference algorithm, an iterated ensemble adjustment Kalman filter, to estimate the likelihood of detection upon testing and nosocomial transmission rates. We evaluate parameter identifiability for this model-inference system and find that the system is able to estimate modelled nosocomial transmission and effective sensitivity upon clinical culture testing. We apply the framework to estimate both quantities for seven prevalent bacterial pathogens: <em>Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus</em> (both sensitive, MSSA, and resistant, MRSA, phenotypes), <em>Enterococcus faecium</em> and <em>Enterococcus faecalis</em>. We estimate that nosocomial transmission for <em>E. coli</em> is negligible<em>.</em> While bacterial pathogens have different importation rates, nosocomial transmission rates were similar among organisms, except <em>E. coli</em>. We also find that estimated likelihoods of detection are similar for all pathogens. This work highlights how fine-scale patient data can support inference of the epidemiological properties of micro-organisms and how hospital traffic and patient contact determine epidemiological features. Evaluation of the transmission potential for different pathogens could ultimately support the development of hospital control measures, as well as the design of surveillance strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100817"},"PeriodicalIF":3.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394841","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":"Estimating the generation time for influenza transmission using household data in the United States","authors":"Louis Yat Hin Chan ,&nbsp;Sinead E. Morris ,&nbsp;Melissa S. Stockwell ,&nbsp;Natalie M. Bowman ,&nbsp;Edwin Asturias ,&nbsp;Suchitra Rao ,&nbsp;Karen Lutrick ,&nbsp;Katherine D. Ellingson ,&nbsp;Huong Q. Nguyen ,&nbsp;Yvonne Maldonado ,&nbsp;Son H. McLaren ,&nbsp;Ellen Sano ,&nbsp;Jessica E. Biddle ,&nbsp;Sarah E. Smith-Jeffcoat ,&nbsp;Matthew Biggerstaff ,&nbsp;Melissa A. Rolfes ,&nbsp;H. Keipp Talbot ,&nbsp;Carlos G. Grijalva ,&nbsp;Rebecca K. Borchering ,&nbsp;Alexandra M. Mellis","doi":"10.1016/j.epidem.2025.100815","DOIUrl":"10.1016/j.epidem.2025.100815","url":null,"abstract":"<div><div>The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially since the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023. More than 200 individuals who tested positive for influenza and their household contacts were enrolled within 7 days of the first illness in the household. All participants were prospectively followed for 10 days, completing daily symptom diaries and collecting nasal swabs, which were then tested for influenza via RT-PCR. We analyzed these data by modifying a previously published Bayesian data augmentation approach that imputes infection times of cases to obtain both intrinsic (assuming no susceptible depletion) and realized (observed within household) generation times. We assessed the robustness of the generation time estimate by varying the incubation period, and generated estimates of the proportion of transmission occurring before symptomatic onset, the infectious period, and the latent period. We estimated a mean intrinsic generation time of 3.2 (95 % credible interval, CrI: 2.9–3.6) days, with a realized household generation time of 2.8 (95 % CrI: 2.7–3.0) days. The generation time exhibited limited sensitivity to incubation period variation. Estimates of the proportion of transmission that occurred before symptom onset, the infectious period, and the latent period were sensitive to variations in the incubation period. Our study contributes to the ongoing efforts to refine estimates of the generation time for influenza. Our estimates, derived from recent data following the COVID-19 pandemic, are consistent with previous pre-pandemic estimates, and will be incorporated into real-time Rt estimation efforts.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100815"},"PeriodicalIF":3.0,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048454","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":"Reconstructing the first COVID-19 pandemic wave with minimal data in England","authors":"Siyu Chen ,&nbsp;Jennifer A. Flegg ,&nbsp;Katrina A. Lythgoe ,&nbsp;Lisa J. White","doi":"10.1016/j.epidem.2025.100814","DOIUrl":"10.1016/j.epidem.2025.100814","url":null,"abstract":"<div><div>Accurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it was particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. We previously developed an early pandemic diagnostic tool that linked minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure to be considerably higher than suggested by seroprevalence surveys. Here, we re-examine and evaluate the model in the context of reconstructing the first COVID-19 epidemic wave in England from three perspectives: validation against the Office for National Statistics (ONS) Coronavirus Infection Survey, relationship among model performance and data abundance and time-varying case detection ratios. We find that our model can recover the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 if two or three serological measurements are given as additional model inputs, while the second wave during winter of 2020 is validated by estimates from the ONS Coronavirus Infection Survey. Moreover, the model estimates that by the end of October in 2020 the UK government’s official COVID-9 online dashboard reported COVID-19 cases only accounted for 9.1 % of cumulative exposure, dramatically varying across the two epidemic waves in England in 2020, 4.3 % vs 43.7 %.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100814"},"PeriodicalIF":3.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014802","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}