An Ebola outbreak of unprecedented scope emerged in West Africa in December 2013 and presently continues unabated in the countries of Guinea, Sierra Leone, and Liberia. Ebola is not new to Africa, ...and outbreaks have been confirmed as far back as 1976. The current West African Ebola outbreak is the largest ever recorded and differs dramatically from prior outbreaks in its duration, number of people affected, and geographic extent. The emergence of this deadly disease in West Africa invites many questions, foremost among these: why now, and why in West Africa? Here, we review the sociological, ecological, and environmental drivers that might have influenced the emergence of Ebola in this region of Africa and its spread throughout the region. Containment of the West African Ebola outbreak is the most pressing, immediate need. A comprehensive assessment of the drivers of Ebola emergence and sustained human-to-human transmission is also needed in order to prepare other countries for importation or emergence of this disease. Such assessment includes identification of country-level protocols and interagency policies for outbreak detection and rapid response, increased understanding of cultural and traditional risk factors within and between nations, delivery of culturally embedded public health education, and regional coordination and collaboration, particularly with governments and health ministries throughout Africa. Public health education is also urgently needed in countries outside of Africa in order to ensure that risk is properly understood and public concerns do not escalate unnecessarily. To prevent future outbreaks, coordinated, multiscale, early warning systems should be developed that make full use of these integrated assessments, partner with local communities in high-risk areas, and provide clearly defined response recommendations specific to the needs of each community.
Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics ...are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model's performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model's ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.
The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of ...delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0-19, 20-64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0-19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20-64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0-19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0-19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies.
Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that ...could be overlooked if the data were reviewed in tabular form; these details can influence a researcher's recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources - data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data.
In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves.
EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.
Arthroscopic treatment of hip labral tears has increased significantly in recent years. There is limited evidence comparing nonoperative management to arthroscopic treatment. The purpose of this ...study is to evaluate the progression to total hip arthroplasty (THA), as well as the cost associated with arthroscopic management of labral tears compared to nonoperative treatment.
The Humana claims database was queried from 2007 through 2016. International Classification of Diseases and Current Procedural Terminology codes were used to identify patients with hip labral tears and hip arthroscopy and THA procedures. Two cohorts were created: a nonoperative group and an operative group. Following propensity score matching, the rate of conversion and time to THA conversion were calculated. Cost was calculated using the total cost reimbursed for encounters within 6 months. Continuous variables were analyzed using Student t-test and Mann-Whitney test, and categorical variables were analyzed using chi-square test.
After propensity matching, 864 patients were included in the analysis. The conversion rate to THA in the operative group (6.7%) and the nonoperative group (5.3%) was not statistically different (P = .391). The operative group had a longer time to THA (21.5 ± 16.8 months) than the nonoperative group (15.9 ± 19.5 months; P = .044). The cost for the operative group was significantly higher ($14,266.55 ± $7187.96) compared to the nonoperative group ($2941.96 ± $2664.00; P < .001).
This study did not find a difference in the rate of conversion to THA for operative vs nonoperative groups. Time to THA in the operative group was longer, however, at the expense of higher costs.
Wildlife species are identified as an important source of emerging zoonotic disease. Accordingly, public health programs have attempted to expand in scope to include a greater focus on wildlife and ...its role in zoonotic disease outbreaks. Zoonotic disease transmission dynamics involving wildlife are complex and nonlinear, presenting a number of challenges. First, empirical characterization of wildlife host species and pathogen systems are often lacking, and insight into one system may have little application to another involving the same host species and pathogen. Pathogen transmission characterization is difficult due to the changing nature of population size and density associated with wildlife hosts. Infectious disease itself may influence wildlife population demographics through compensatory responses that may evolve, such as decreased age to reproduction. Furthermore, wildlife reservoir dynamics can be complex, involving various host species and populations that may vary in their contribution to pathogen transmission and persistence over space and time. Mathematical models can provide an important tool to engage these complex systems, and there is an urgent need for increased computational focus on the coupled dynamics that underlie pathogen spillover at the human-wildlife interface. Often, however, scientists conducting empirical studies on emerging zoonotic disease do not have the necessary skill base to choose, develop, and apply models to evaluate these complex systems. How do modeling frameworks differ and what considerations are important when applying modeling tools to the study of zoonotic disease? Using zoonotic disease examples, we provide an overview of several common approaches and general considerations important in the modeling of wildlife-associated zoonoses.
Primary care providers (PCPs) frequently address dermatologic concerns and perform skin examinations during clinical encounters. For PCPs who evaluate concerning skin lesions, dermoscopy (a ...noninvasive skin visualization technique) has been shown to increase the sensitivity for skin cancer diagnosis compared with unassisted clinical examinations. Because no formal consensus existed on the fundamental knowledge and skills that PCPs should have with respect to dermoscopy for skin cancer detection, the objective of this study was to develop an expert consensus statement on proficiency standards for PCPs learning or using dermoscopy.
A 2-phase modified Delphi method was used to develop 2 proficiency standards. In the study's first phase, a focus group of PCPs and dermatologists generated a list of dermoscopic diagnoses and associated features. In the second phase, a larger panel evaluated the proposed list and determined whether each diagnosis was reflective of a foundational or intermediate proficiency or neither.
Of the 35 initial panelists, 5 PCPs were lost to follow-up or withdrew; 30 completed the fifth and last round. The final consensus-based list contained 39 dermoscopic diagnoses and associated features.
This consensus statement will inform the development of PCP-targeted dermoscopy training initiatives designed to support early cancer detection.
Background. Social network analysis (SNA) is an innovative approach to the collection and analysis of infectious disease transmission data. We studied whether this approach can detect patterns of ...Mycobacterium tuberculosis transmission and play a helpful role in the complex process of prioritizing tuberculosis (TB) contact investigations. Methods. We abstracted routine demographic and clinical variables from patient medical records and contact interview forms. We also administered a structured questionnaire about places of social aggregation to TB patients and their contacts. All case-contact, contact-contact, case-place, and contact-place dyads (pairs and links) were considered in order to analyze the structure of a social network of TB transmission. Molecular genotyping was used to confirm SNA-detected clusters of TB. Results. TB patients not linked through conventional contact-investigation data were connected through mutual contacts or places of social aggregation, using SNA methods. In some instances, SNA detected connected groups prior to the availability of genotyping results. A positive correlation between positive results of contacts' tuberculin skin test (TST) and location in denser portions of the person-place network was observed (P < .01). Conclusions. Correlation between TST-positive status and dense subgroup occurrence supports the value of collecting place data to help prioritize TB contact investigations. TB controllers should consider developing social network analysis capacity to facilitate the systematic collection, analysis, and interpretation of contact-investigation data.
Structural principles underlying the composition of protective antiviral monoclonal antibody (mAb) cocktails are poorly defined. Here, we exploited antibody cooperativity to develop a therapeutic mAb ...cocktail against Ebola virus. We systematically analyzed the antibody repertoire in human survivors and identified a pair of potently neutralizing mAbs that cooperatively bound to the ebolavirus glycoprotein (GP). High-resolution structures revealed that in a two-antibody cocktail, molecular mimicry was a major feature of mAb-GP interactions. Broadly neutralizing mAb rEBOV-520 targeted a conserved epitope on the GP base region. mAb rEBOV-548 bound to a glycan cap epitope, possessed neutralizing and Fc-mediated effector function activities, and potentiated neutralization by rEBOV-520. Remodeling of the glycan cap structures by the cocktail enabled enhanced GP binding and virus neutralization. The cocktail demonstrated resistance to virus escape and protected non-human primates (NHPs) against Ebola virus disease. These data illuminate structural principles of antibody cooperativity with implications for development of antiviral immunotherapeutics.
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•Human mAbs of two epitope specificities bind cooperatively to the ebolavirus GP•Cooperativity is mediated by a mAb that enhances binding to a vulnerable GP epitope•A two-mAb cocktail exhibits enhanced potency against heterologous ebolaviruses•Two 30 mg/kg doses of the cocktail fully protected non-human primates (NHPs) challenged with EBOV
Cooperative interactions of monoclonal antibodies (mAbs) with viral antigens are poorly understood. Gilchuk et al. perform structural and functional analysis of cooperativity in a cocktail of two human mAbs, recognizing major epitopes of ebolavirus glycoprotein (GP), and define cooperative binding of the GP as a mechanism for enhanced ebolavirus neutralization.
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