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Hawkins, Jared

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Hawkins

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Hawkins, Jared
Author's Publications: search.filters.isAuthorOfPublication.0965fe75-3a57-44de-b95d-7c3ff530075c

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Now showing 1 - 7 of 7
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    Scalable and cost-effective NGS genotyping in the cloud
    (BioMed Central, 2015) Souilmi, Yassine; Lancaster, Alex K.; Jung, Jae-Yoon; Rizzo, Ettore; Hawkins, Jared; Powles, Ryan; Amzazi, Saaïd; Ghazal, Hassan; Tonellato, Peter; Wall, Dennis P.
    Background: While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10’s of dollars. Results: We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. Conclusions: Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0134-9) contains supplementary material, which is available to authorized users.
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    Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data
    (Public Library of Science, 2017) McGough, Sarah; Brownstein, John; Hawkins, Jared; Santillana, Mauricio
    Background: Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015–2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission. Methodology/Principal Findings We combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015–2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions. Significance Given the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak.
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    Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health
    (MDPI, 2018) Harris, Jenine K.; Hinyard, Leslie; Beatty, Kate; Hawkins, Jared; Nsoesie, Elaine O.; Mansour, Raed; Brownstein, John
    Foodborne illness is a serious and preventable public health problem affecting 1 in 6 Americans with cost estimates over $50 billion annually. Local health departments license and inspect restaurants to ensure food safety and respond to reports of suspected foodborne illness. The City of St. Louis Department of Health adopted the HealthMap Foodborne Dashboard (Dashboard), a tool that monitors Twitter for tweets about food poisoning in a geographic area and allows the health department to respond. We evaluated the implementation by interviewing employees of the City of St. Louis Department of Health involved in food safety. We interviewed epidemiologists, environmental health specialists, health services specialists, food inspectors, and public information officers. Participants viewed engaging innovation participants and executing the innovation as challenges while they felt the Dashboard had relative advantage over existing reporting methods and was not complex once in place. This study is the first to examine practitioner perceptions of the implementation of a new technology in a local health department. Similar implementation projects should focus more on process by developing clear and comprehensive plans to educate and involve stakeholders prior to implementation.
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    Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project
    (Wolters Kluwer Health, Inc., 2017) Harris, Jenine K.; Hawkins, Jared; Nguyen, Leila; Nsoesie, Elaine O.; Tuli, Gaurav; Mansour, Raed; Brownstein, John
    Context: Foodborne illness affects 1 in 4 US residents each year. Few of those sickened seek medical care or report the illness to public health authorities, complicating prevention efforts. Citizens who report illness identify food establishments with more serious and critical violations than found by regular inspections. New media sources, including online restaurant reviews and social media postings, have the potential to improve reporting. Objective: We implemented a Web-based Dashboard (HealthMap Foodborne Dashboard) to identify and respond to tweets about food poisoning from St Louis City residents. Design and Setting: This report examines the performance of the Dashboard in its first 7 months after implementation in the City of St Louis Department of Health. Main Outcome Measures: We examined the number of relevant tweets captured and replied to, the number of foodborne illness reports received as a result of the new process, and the results of restaurant inspections following each report. Results: In its first 7 months (October 2015-May 2016), the Dashboard captured 193 relevant tweets. Our replies to relevant tweets resulted in more filed reports than several previously existing foodborne illness reporting mechanisms in St Louis during the same time frame. The proportion of restaurants with food safety violations was not statistically different (P = .60) in restaurants inspected after reports from the Dashboard compared with those inspected following reports through other mechanisms. Conclusion: The Dashboard differs from other citizen engagement mechanisms in its use of current data, allowing direct interaction with constituents on issues when relevant to the constituent to provide time-sensitive education and mobilizing information. In doing so, the Dashboard technology has potential for improving foodborne illness reporting and can be implemented in other areas to improve response to public health issues such as suicidality, spread of Zika virus infection, and hospital quality.
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    COSMOS: Python library for massively parallel workflows
    (Oxford University Press, 2014) Gafni, Erik; Luquette, Joe; Lancaster, Alex K.; Hawkins, Jared; Jung, Jae-Yoon; Souilmi, Yassine; Wall, Dennis P.; Tonellato, Peter
    Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services. Availability and implementation: Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at http://lpm.hms.harvard.edu and http://wall-lab.stanford.edu. Contact: dpwall@stanford.edu or peter_tonellato@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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    Measuring patient-perceived quality of care in US hospitals using Twitter
    (BMJ Publishing Group, 2016) Hawkins, Jared; Brownstein, John; Tuli, Gaurav; Runels, Tessa; Broecker, Katherine; Nsoesie, Elaine O; McIver, David J; Rozenblum, Ronen; Wright, Adam; Bourgeois, Florence; Greaves, Felix
    Background: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. Objective: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. Design: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. Key results Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). Conclusions: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators.
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    Measuring Patient-Perceived Quality of Care in U.S. Hospitals Using Twitter
    (2015-09-29) Hawkins, Jared; Brownstein, John S.; Bourgeois, Florence T.; Wright, Adam
    BACKGROUND Patients use Twitter to share feedback about their experience receiving health care. Identifying and analyzing the content of posts sent to each hospital may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. OBJECTIVE To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in U.S. hospitals and compare patient sentiments about hospitals on Twitter to established quality measures. DESIGN Tweets directed to U.S. hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Additionally, sentiment was calculated for patient experience tweets using natural language processing. KEY RESULTS Roughly half of the hospitals in the U.S. have a presence on Twitter. Of the tweets directed toward these hospitals, ~9% were related to patient experience. Analyses revealed that specific hospital characteristics were associated with lower sentiment. Finally, hospital sentiment was moderately correlated with a commonly used measure of quality. CONCLUSIONS Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a reliable predictor of treatment quality and may be valuable to patients, researchers, policy makers and hospital administrators.