Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference

Lee, Elizabeth C.; Asher, Jason; Goldlust, Sandra; Kraemer, John; Lawson, Andrew; Bansal, Shweta

doi:10.1093/infdis/jiw344

Cited by 66 publications

(47 citation statements)

References 35 publications

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“…The key to applying this disease surveillance method is the availability of detailed and timely primary data (Bansal et al, ; Simonsen et al, ). Such data does not solely stem from doctors and health institutions but also makes use of social data streams, including social media, search engine queries, and crowdsourcing (Lee et al, ). Primary surveillance data and computer analytics are used to track and visualize the spread of diseases and to apply appropriate countermeasures (Dowell et al, ).…”

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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“…Another risk is that many data sources do not capture basic demographics (age, sex, ethnicity) and infants, the elderly and economically disadvantaged groups-who are most at risk from communicable diseases-are likely to be under-represented . Methods developed to minimise bias and validate results in conventional epidemiological research, such as sampling protocols and case definitions, cannot easily be applied (Lee et al 2016).…”

Section: Syndromic Surveillance Digital Epidemiology and Big Datamentioning

confidence: 99%

Communicable Disease Surveillance Ethics in the Age of Big Data and New Technology

Gilbert

Degeling

Johnson

2019

ABR

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Surveillance is essential for communicable disease prevention and control. Traditional notification of demographic and clinical information, about individuals with selected (notifiable) infectious diseases, allows appropriate public health action and is protected by public health and privacy legislation, but is slow and insensitive. Big data-based electronic surveillance, by commercial bodies and government agencies (for profit or population control), which draws on a plethora of internet-and mobile device-based sources, has been widely accepted, if not universally welcomed. Similar anonymous digital sources also contain syndromic information, which can be analysed, using customised algorithms, to rapidly predict infectious disease outbreaks, but the data are nonspecific and predictions sometimes misleading. However, public health authorities could use these online sources, in combination with de-identified personal health data, to provide more accurate and earlier warning of infectious disease eventsincluding exotic or emerging infections-even before the cause is confirmed, and allow more timely public health intervention. Achieving optimal benefits would require access to selected data from personal electronic health and laboratory (including pathogen genomic) records and the potential to (confidentially) re-identify individuals found to be involved in outbreaks, to ensure appropriate care and infection control. Despite existing widespread digital surveillance and major potential community benefits of extending its use to communicable disease control, there is considerable public disquiet about allowing public health authorities access to personal health data. Informed public discussion, greater transparency and an ethical framework will be essential to build public trust in the use of new technology for communicable disease control.

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“…Next, Lee et al [15] review the technical, practical, and ethical challenges of using big data to understand the spatial distribution and transmission of infectious diseases. The heterogeneity of data sets and data types particular to this field makes integration of different data streams obtained at different spatial scales technically challenging; greater use of multilevel Bayesian statistical approaches would help alleviate this issue.…”

Section: Beyond Surveillance: Big Data For Modeling Of Disease Transmmentioning

confidence: 99%

Big Data for Infectious Disease Surveillance and Modeling

et al. 2016

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We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.

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Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference

Cited by 66 publications

References 35 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

Communicable Disease Surveillance Ethics in the Age of Big Data and New Technology

Big Data for Infectious Disease Surveillance and Modeling

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