Digital disease detection: A systematic review of event-based internet biosurveillance systems

O’Shea, Jesse

doi:10.1016/j.ijmedinf.2017.01.019

Cited by 103 publications

(66 citation statements)

References 14 publications

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“…During stage two (localized emergence), contact with animals or animal products results in spillover of the pathogen from its natural reservoir(s) into humans but with little to no onward person-to-person transmission. During stage three (pandemic emergence), the pathogen is able to sustain long transmission chains, that is, a series of disease transmission events, such as a sequential series of person-to-person transmissions, and its movement across borders is facilitated by human travel patterns 65 . epidemiology platforms are currently operating 88 , and their flexible nature and cost-effective, real-time reporting make them effective tools for gathering epidemic intelligence, particularly in settings lacking traditional disease surveillance systems.…”

Section: Digital Epidemiologymentioning

confidence: 99%

Towards a genomics-informed, real-time, global pathogen surveillance system

2017

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The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes - now a staple of the public health microbiology laboratory in well-resourced settings - can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity.

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Section: Digital Epidemiologymentioning

confidence: 99%

Towards a genomics-informed, real-time, global pathogen surveillance system

2017

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“…principal component analysis), and alternative statistical models (e.g. ensemble methods) [7,20]. Numerous other studies, mostly from the US, have aimed to predict ILI incidence rates from online data.…”

Section: Discussionmentioning

confidence: 99%

“…[3,6] Since then, the number of scholarly articles published in the field of digital epidemiology has grown significantly. [2,7] The discipline is, nevertheless, in an early stage and should still be considered as being experimental. Especially outside of the United States, there has been little effort to investigate the value of online data sources for epidemiological purposes.…”

Section: Introductionmentioning

confidence: 99%

PREPRINT: Using digital epidemiology methods to monitor influenza-like illness in the Netherlands in real-time: the 2017-2018 season

Schneider

Gool

Spreeuwenberg

et al. 2018

Preprint

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IntroductionDespite the early development of Google Flu Trends in 2009, digital epidemiology methods have not been adopted widely, with most research focusing on the USA. In this article we demonstrate the prediction of real-time trends in influenza-like illness (ILI) in the Netherlands using search engine query data. MethodsWe used flu-related search query data from Google Trends in combination with traditional surveillance data from 40 general sentinel practices to build our predictive models. We introduced an artificial 4-week delay in the use of GP data in the models, in order to test the predictive performance of the search engine data.Simulating the weekly use of a prediction model across the 2017/2018 flu season we used lasso regression to fit 52 prediction models (one for each week) for weekly ILI incidence. We used rolling forecast cross-validation for lambda optimization in each model, minimizing the maximum absolute error. ResultsThe models accurately predicted the number of ILI cases during the 2017/18 ILI epidemic in real time with a mean absolute error of 1.40 (per 10,000 population) and a maximum absolute error of 6.36. The model would also have identified the onset, peak, and end of the epidemic with reasonable accuracyThe number of predictors that were retained in the prediction models was small, ranging from 3 to 5, with a single keyword ('Griep' = 'Flu') having by far the most weight in all models. DiscussionThis study demonstrates the feasibility of accurate real-time ILI incidence predictions in the Netherlands using internet search query data .Digital ILI monitoring strategies may be useful in countries with poor surveillance systems, or for monitoring emergent diseases, including influenza pandemics. We hope that this transparent and accessible case study inspires and supports further developments in field of digital epidemiology in Europe and beyond.

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“…The spectrum of data envisaged for inclusion in event‐based surveillance is much wider than that used in indicator‐based surveillance, with any digital information that can be scraped from Internet sites being utilised. The drive towards increasingly powerful event‐based surveillance has already led to the development of multiple approaches: a recent review identified 50 such event‐based Internet systems (O'Shea, ). This proliferation in turn has led to many to call for initiatives in the direction of a “super‐system … to pool expert systems’ expertise” (Barboza et al., , p. 8).…”

Section: Imagining the Sequencing Singularitymentioning

confidence: 99%

Portable sequencing, genomic data, and scale in global emerging infectious disease surveillance

Shaw

Sugden

2018

Geography and Environment

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Emerging infectious diseases (EIDs) occur when pathogens unpredictably spread into new contexts. EID surveillance systems seek to rapidly identify EID outbreaks to contain spread and improve public health outcomes. Sequencing data has historically not been integrated into real‐time responses, but portable DNA sequencing technology has prompted optimism among epidemiologists. Specifically, attention has focused on the goal of a “sequencing singularity”: the integration of portable sequencers in a worldwide event‐based surveillance network with other digital data (Gardy & Loman, Nature Reviews Genetics, 19, 2018, p. 9). The sequencing singularity vision is a powerful socio‐technical imaginary, shaping the discourse around the future of portable sequencing. Ethical and practical issues are bound by the vision in two ways: they are framed only as obstacles, and they are formulated only at the scales made visible by its implicit geography. This geography privileges two extremes of scale – the genomic and the global – and leaves intermediate scales comparatively unmapped. We explore how widespread portable sequencing could challenge this geography. Portable sequencers put the ability to produce genomic data in the hands of the individual. The explicit assertion of rights over data may therefore become a matter disputed more at an interpersonal scale than an international one. Portable sequencers also promise ubiquitous, indiscriminate sequencing of the total metagenomic content of samples, raising the question of what (or who) is under surveillance and inviting consideration of the human microbiome and more‐than‐human geographies. We call into question a conception of a globally integrated stream of sequencing data as composed mostly of “noise,” within which signals of pathogen “emergence” are “hidden,” considering it instead from the perspective of recent work into more‐than‐human geographies. Our work highlights a practical need for researchers to consider both the alternative possibilities they foreclose as well as the exciting opportunities they move towards when they deploy their visions of the future.

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Digital disease detection: A systematic review of event-based internet biosurveillance systems

Cited by 103 publications

References 14 publications

Towards a genomics-informed, real-time, global pathogen surveillance system

Towards a genomics-informed, real-time, global pathogen surveillance system

PREPRINT: Using digital epidemiology methods to monitor influenza-like illness in the Netherlands in real-time: the 2017-2018 season

Portable sequencing, genomic data, and scale in global emerging infectious disease surveillance

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