Epidemics最新文献

{"title":"Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation","authors":"John Ellis ,&nbsp;Emma Brown,&nbsp;Claire Colenutt,&nbsp;David Schley ,&nbsp;Simon Gubbins","doi":"10.1016/j.epidem.2024.100740","DOIUrl":"10.1016/j.epidem.2024.100740","url":null,"abstract":"<div><p>To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100740"},"PeriodicalIF":3.8,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175543652400001X/pdfft?md5=0b487ddc9370c198baf00059992893a6&pid=1-s2.0-S175543652400001X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139396591","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":"Epidemiological impact of a large number of false negative SARS-CoV-2 test results in South West England during September and October 2021","authors":"L. Hounsome ,&nbsp;D. Herr ,&nbsp;R. Bryant ,&nbsp;R. Smith ,&nbsp;L. Loman ,&nbsp;J. Harris ,&nbsp;U. Youhan ,&nbsp;E. Dzene ,&nbsp;P. Hadjipantelis ,&nbsp;H. Long ,&nbsp;T. Laurence ,&nbsp;S. Riley ,&nbsp;F. Cumming","doi":"10.1016/j.epidem.2023.100739","DOIUrl":"10.1016/j.epidem.2023.100739","url":null,"abstract":"<div><p>During September and October 2021, a substantial number of Polymerase Chain Reaction (PCR) tests in England processed at a single laboratory were incorrectly reported as negative. We estimate the number of false negative test results issued and investigate the epidemiological impact of this incident. We estimate the number of COVID-19 cases that would have been reported had the sensitivity of the laboratory test procedure not dropped for the period 2 September to 12 October. In addition, by making comparisons between the most affected local areas and comparator populations, we estimate the number of additional infections, cases, hospitalisations and deaths that could have occurred as a result of increased transmission due to false negative test results.We estimate that around 39,000 tests may have been false negatives during this period and, as a direct result of this incident, the most affected areas in the South-West of England could have experienced between 6000 and 34,000 additional reportable cases, with a central estimate of around 24,000 additional reportable cases. Using modelled relationships between key variables, we estimate that this central estimate could have translated to approximately 55,000 additional infections.Each false negative likely led to around 1.5 additional infections. The incident is likely to have had a measurable impact on cases and infections in the affected areas in the South-West of England.</p></div><div><h3>Impact statement</h3><p>These results indicate the significant negative impact of incorrect testing on COVID outcomes; and make a substantial contribution to understanding the impact of testing systems and the need to ensure high accuracy in testing and reporting of results.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100739"},"PeriodicalIF":3.8,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000750/pdfft?md5=216300aca77e4e3309d1e026aef25e7f&pid=1-s2.0-S1755436523000750-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139376169","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 US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy","authors":"Sara L. Loo ,&nbsp;Emily Howerton ,&nbsp;Lucie Contamin ,&nbsp;Claire P. Smith ,&nbsp;Rebecca K. Borchering ,&nbsp;Luke C. Mullany ,&nbsp;Samantha Bents ,&nbsp;Erica Carcelen ,&nbsp;Sung-mok Jung ,&nbsp;Tiffany Bogich ,&nbsp;Willem G. van Panhuis ,&nbsp;Jessica Kerr ,&nbsp;Jessi Espino ,&nbsp;Katie Yan ,&nbsp;Harry Hochheiser ,&nbsp;Michael C. Runge ,&nbsp;Katriona Shea ,&nbsp;Justin Lessler ,&nbsp;Cécile Viboud ,&nbsp;Shaun Truelove","doi":"10.1016/j.epidem.2023.100738","DOIUrl":"10.1016/j.epidem.2023.100738","url":null,"abstract":"<div><p>Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100738"},"PeriodicalIF":3.8,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000749/pdfft?md5=5aec925efd209aadbaed8387f3492c49&pid=1-s2.0-S1755436523000749-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139062209","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":"Predicting the impact of COVID-19 non-pharmaceutical intervention on short- and medium-term dynamics of enterovirus D68 in the US","authors":"Sang Woo Park ,&nbsp;Kevin Messacar ,&nbsp;Daniel C. Douek ,&nbsp;Alicen B. Spaulding ,&nbsp;C. Jessica E. Metcalf ,&nbsp;Bryan T. Grenfell","doi":"10.1016/j.epidem.2023.100736","DOIUrl":"10.1016/j.epidem.2023.100736","url":null,"abstract":"<div><p>Recent outbreaks of enterovirus D68 (EV-D68) infections, and their causal linkage with acute flaccid myelitis (AFM), continue to pose a serious public health concern. During 2020 and 2021, the dynamics of EV-D68 and other pathogens have been significantly perturbed by non-pharmaceutical interventions against COVID-19; this perturbation presents a powerful natural experiment for exploring the dynamics of these endemic infections. In this study, we analyzed publicly available data on EV-D68 infections, originally collected through the New Vaccine Surveillance Network, to predict their short- and long-term dynamics following the COVID-19 interventions. Although long-term predictions are sensitive to our assumptions about underlying dynamics and changes in contact rates during the NPI periods, the likelihood of a large outbreak in 2023 appears to be low. Comprehensive surveillance data are needed to accurately characterize future dynamics of EV-D68. The limited incidence of AFM cases in 2022, despite large EV-D68 outbreaks, poses further questions for the timing of the next AFM outbreaks.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100736"},"PeriodicalIF":3.8,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000725/pdfft?md5=cb4e69ff64f60b0e8cd7d3fd6ccac356&pid=1-s2.0-S1755436523000725-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138744925","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 case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective","authors":"Epke A. Le Rutte ,&nbsp;Andrew J. Shattock ,&nbsp;Cheng Zhao ,&nbsp;Soushieta Jagadesh ,&nbsp;Miloš Balać ,&nbsp;Sebastian A. Müller ,&nbsp;Kai Nagel ,&nbsp;Alexander L. Erath ,&nbsp;Kay W. Axhausen ,&nbsp;Thomas P. Van Boeckel ,&nbsp;Melissa A. Penny","doi":"10.1016/j.epidem.2023.100734","DOIUrl":"10.1016/j.epidem.2023.100734","url":null,"abstract":"<div><p>This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic.</p><p>Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency.</p><p>This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100734"},"PeriodicalIF":3.8,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000701/pdfft?md5=41aee9a81b2e351e51317db259a7d544&pid=1-s2.0-S1755436523000701-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138688295","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":"Effectiveness of a COVID-19 contact tracing app in a simulation model with indirect and informal contact tracing","authors":"Ka Yin Leung ,&nbsp;Esther Metting ,&nbsp;Wolfgang Ebbers ,&nbsp;Irene Veldhuijzen ,&nbsp;Stijn P. Andeweg ,&nbsp;Guus Luijben ,&nbsp;Marijn de Bruin ,&nbsp;Jacco Wallinga ,&nbsp;Don Klinkenberg","doi":"10.1016/j.epidem.2023.100735","DOIUrl":"10.1016/j.epidem.2023.100735","url":null,"abstract":"<div><p>During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100735"},"PeriodicalIF":3.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000713/pdfft?md5=4d5245534a992cfda8d09f20498b1a01&pid=1-s2.0-S1755436523000713-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575138","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":"Seasonality as a driver of pH1N12009 influenza vaccination campaign impact","authors":"Kirsty J. Bolton ,&nbsp;James M. McCaw ,&nbsp;Mathew P. Dafilis ,&nbsp;Jodie McVernon ,&nbsp;Jane M. Heffernan","doi":"10.1016/j.epidem.2023.100730","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100730","url":null,"abstract":"<div><p>Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza A virus (IAV) to global health. Retrospective analysis of pandemic outcomes, including comparative investigation of intervention efficacy in different regions, provide important contributions to the evidence base for future pandemic planning. The swine-origin IAV pandemic of 2009 exhibited regional variation in onset, infection dynamics and annual infection attack rates (IARs). For example, the UK experienced three severe peaks of infection over two influenza seasons, whilst Australia experienced a single severe wave. We adopt a seasonally forced 2-subtype model for the transmission of pH1N12009 and seasonal H3N2 to examine the role vaccination campaigns may play in explaining differences in pandemic trajectories in temperate regions. Our model differentiates between the nature of vaccine- and infection-acquired immunity. In particular, we assume that immunity triggered by infection elicits heterologous cross-protection against viral shedding in addition to long-lasting neutralising antibody, whereas vaccination induces imperfect reduction in susceptibility. We employ an Approximate Bayesian Computation (ABC) framework to calibrate the model using data for pH1N12009 seroprevalence, relative subtype dominance, and annual IARs for Australia and the UK. Heterologous cross-protection substantially suppressed the pandemic IAR over the posterior, with the strength of protection against onward transmission inversely correlated with the initial reproduction number. We show that IAV pandemic timing relative to the usual seasonal influenza cycle influenced the size of the initial waves of pH1N12009 in temperate regions and the impact of vaccination campaigns.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100730"},"PeriodicalIF":3.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175543652300066X/pdfft?md5=d6c11505f53bc45da411fec3d77b8bfc&pid=1-s2.0-S175543652300066X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138490664","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 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}