Big Data for Infectious Disease Surveillance and Modeling

Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile

doi:10.1093/infdis/jiw400

Cited by 214 publications

(166 citation statements)

References 17 publications

(18 reference statements)

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“…Central to infectious disease control, are surveillance systems that help to track diseases and pathogens (Bansal, Chowell, Simonsen, Vespignani, & Viboud, ). Disease surveillance systems are “recognized as one of the most important tools to assess, predict, and mitigate infectious disease outbreaks” (Salathé, , p. 399).…”

Section: How the Starry Sky Beetle Contributes To Refine The Politicamentioning

confidence: 99%

Conceptualizing data‐deliberation: The starry sky beetle, environmental system risk, and Habermasian CSR in the digital age

Schultz

Seele

2019

Business Ethics: A Eur Rev

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Building on an illustrative case of a systemic environmental threat and its multi‐stakeholder response, this paper draws attention to the changing political impacts of corporations in the digital age. Political Corporate Social Responsibility (PCSR) theory suggests an expanded sense of politics and corporations, including impacts that may range from voluntary initiatives to overcome governance gaps, to avoiding state regulation via corporate political activity. Considering digitalization as a stimulus, we explore potential responsibilities of corporations toward public goods in contexts with functioning governments. We show that digitalization—in the form of transparency, surveillance, and data‐sharing—offers corporations’ scope for deliberative public participation. The starry sky beetle infestation endangering public and private goods is thereby used to illustrate the possibility of expanding the political role of corporations in the digital sphere. We offer a contribution by conceptualizing data‐deliberation as a Habermasian variation of PCSR, defined as the (a) voluntary disclosure of corporate data and its transparent, open sharing with the public sector (b) along with the cooperation with governmental institutions on data analytics methods for examining large‐scale datasets (c) thereby complying with existing national and international regulations on data protection, in particular with respect to privacy and personal data.

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Section: How the Starry Sky Beetle Contributes To Refine The Politicamentioning

confidence: 99%

Conceptualizing data‐deliberation: The starry sky beetle, environmental system risk, and Habermasian CSR in the digital age

Schultz

Seele

2019

Business Ethics: A Eur Rev

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“…These streams can also go beyond disease surveillance and provide information on behaviors and outcomes related to vaccine or drug use [13]. Big Data can be used in the health care to get innovative outcomes in the following areas [14]:…”

Section: Data Sources In Healthcare and Big Data Advantagesmentioning

confidence: 99%

Big Data and Data-Driven Healthcare Systems

Alexander¹,

Wang²

2018

JBMS

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Data analytics has been used in healthcare. Healthcare systems generate big data. Traditional data management techniques are often unable to manage the voluminous amounts of data produced in healthcare systems. Big Data analytics which is overcoming the limitations of traditional data analytics will bring revolutions in healthcare systems. Big data and Big Data analytics in healthcare systems are presented in this paper. Information security, privacy, and challenges of Big Data analytics in healthcare are also discussed.

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“…Data collected by volunteers who self-report symptoms in near real time also could be exploited. [56] Similarly, by combining models, we could retain the benefits of each of them and improve the estimates of ILI incidence rates. For example, we could use another algorithm, such as stacking, [57] to concomitantly use the SVM and Elastic Net models.…”

Section: Perspectivesmentioning

confidence: 99%

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

Poirier¹,

Lavenu²,

Bertaud³

et al. 2018

Preprint

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Background Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. Several works have investigated the possibility to use internet-users' activity data and different statistical models to predict influenza epidemics in near real-time. However, very few studies have investigated hospital big data. Objective Here, we compared internet and electronic health records (EHR) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods We used Google Data for internet data and the clinical data warehouse eHOP, that includes all EHR from Rennes University Hospital (France), for hospital data. We compared three statistical models, Random Forest (RF), Elastic Net, and Support Vector Machine (SVM).

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Big Data for Infectious Disease Surveillance and Modeling

Cited by 214 publications

References 17 publications

Conceptualizing data‐deliberation: The starry sky beetle, environmental system risk, and Habermasian CSR in the digital age

Conceptualizing data‐deliberation: The starry sky beetle, environmental system risk, and Habermasian CSR in the digital age

Big Data and Data-Driven Healthcare Systems

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

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