Notifiable infectious disease surveillance with data collected by search engine

Zhou, Xi‐Lang; Shen, Haibin

doi:10.1631/jzus.c0910371

Cited by 28 publications

(23 citation statements)

References 9 publications

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“…Whereas there is a lag of 1-2 weeks in tracking influenza, most foodborne disease outbreaks are not detected for several months after they occur, by which time the outbreak and opportunity for intervention are virtually over. A Chinese study by Zhou et al in 2010 [30] used both Baidu search queries and Baidu news articles to track infectious diseases including dysentery. They were able to reduce the distorting effect of disease-related news reports by using a combination of search frequency data and news count data.…”

Section: Discussionmentioning

confidence: 99%

Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation

Bernardo¹,

Rajić²,

Young³

et al. 2013

J Med Internet Res

152

136

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BackgroundThe threat of a global pandemic posed by outbreaks of influenza H5N1 (1997) and Severe Acute Respiratory Syndrome (SARS, 2002), both diseases of zoonotic origin, provoked interest in improving early warning systems and reinforced the need for combining data from different sources. It led to the use of search query data from search engines such as Google and Yahoo! as an indicator of when and where influenza was occurring. This methodology has subsequently been extended to other diseases and has led to experimentation with new types of social media for disease surveillance.ObjectiveThe objective of this scoping review was to formally assess the current state of knowledge regarding the use of search queries and social media for disease surveillance in order to inform future work on early detection and more effective mitigation of the effects of foodborne illness.MethodsStructured scoping review methods were used to identify, characterize, and evaluate all published primary research, expert review, and commentary articles regarding the use of social media in surveillance of infectious diseases from 2002-2011.ResultsThirty-two primary research articles and 19 reviews and case studies were identified as relevant. Most relevant citations were peer-reviewed journal articles (29/32, 91%) published in 2010-11 (28/32, 88%) and reported use of a Google program for surveillance of influenza. Only four primary research articles investigated social media in the context of foodborne disease or gastroenteritis. Most authors (21/32 articles, 66%) reported that social media-based surveillance had comparable performance when compared to an existing surveillance program. The most commonly reported strengths of social media surveillance programs included their effectiveness (21/32, 66%) and rapid detection of disease (21/32, 66%). The most commonly reported weaknesses were the potential for false positive (16/32, 50%) and false negative (11/32, 34%) results. Most authors (24/32, 75%) recommended that social media programs should primarily be used to support existing surveillance programs.ConclusionsThe use of search queries and social media for disease surveillance are relatively recent phenomena (first reported in 2006). Both the tools themselves and the methodologies for exploiting them are evolving over time. While their accuracy, speed, and cost compare favorably with existing surveillance systems, the primary challenge is to refine the data signal by reducing surrounding noise. Further developments in digital disease surveillance have the potential to improve sensitivity and specificity, passively through advances in machine learning and actively through engagement of users. Adoption, even as supporting systems for existing surveillance, will entail a high level of familiarity with the tools and collaboration across jurisdictions.

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Section: Discussionmentioning

confidence: 99%

Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation

Bernardo¹,

Rajić²,

Young³

et al. 2013

J Med Internet Res

152

136

View full text Add to dashboard Cite

show abstract

“…Promising results have been obtained for most disease categories by mode of transmission. These include vector borne diseases [11], airborne diseases [16-19], food borne diseases [63,64,99], contagious diseases [69], water borne diseases [62], sexually transmitted diseases [71], non communicable diseases [100] and even psychiatric conditions like suicide risk [101]. …”

Section: Resultsmentioning

confidence: 99%

Design Choices for Automated Disease Surveillance in the Social Web

Magumba

Nabende

Mwebaze

2018

OJPHI

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The social web has emerged as a dominant information architecture accelerating technology innovation on an unprecedented scale. The utility of these developments to public health use cases like disease surveillance, information dissemination, outbreak prediction and so forth has been widely investigated and variously demonstrated in work spanning several published experimental studies and deployed systems. In this paper we provide an overview of automated disease surveillance efforts based on the social web characterized by their different high level design choices regarding functional aspects like user participation and language parsing approaches. We briefly discuss the technical rationale and practical implications of these different choices in addition to the key limitations associated with these systems within the context of operable disease surveillance. We hope this can offer some technical guidance to multi-disciplinary teams on how best to implement, interpret and evaluate disease surveillance programs based on the social web.

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“…These traces of disease observations are embedded in search queries [5, 7, 9, 12, 14, 17, 21, 25, 26, 31, 32, 33, 39, 49, 50, 53, 59, 63, 64, 71, 72 73, 77, 78, 81, 85, 87, 90, 97, 103, 104, 109, 119, 126, 127, 131, 132, 141, 142, 144, 146, 157, 158, 162, 163, 166, 168, 169, 170, 173, 177, 179, 180, 182], social media messages [1, 2, 8, 10, 20, 36, 40, 41, 42, 46, 51, 60, 62, 68, 76, 84, 89, 92, 93, 115, 116, 118, 123, 124, 148, 149, 151, 176], web server access logs [57, 79, 101, 105], and combinations thereof [13, 19, 30, 91, 136, 143, 167]. …”

Section: Related Workmentioning

confidence: 99%

“…The disease surveillance work cited above has been applied to a wide variety of infectious and non-infectious conditions: allergies [87], asthma [136, 176], avian influenza [25], cancer [39], chicken pox [109, 126], chikungunya [109], chlamydia [42, 78, 109], cholera [36, 57], dengue [7, 31, 32, 57, 62, 109], diabetes [42, 60], dysentery [180], Ebola [5, 57], erythromelalgia [63], food poisoning [12], gastroenteritis [45, 50, 71, 126], gonorrhea [77, 78, 109], hand foot and mouth disease [26, 167], heart disease [51, 60], hepatitis [109], HIV/AIDS [57, 76, 177, 180], influenza [1, 2, 8, 9, 10, 13, 19, 20, 21, 30, 33, 40, 41, 43, 46, 48, 53, 57, 59, 68, 72, 73, 79, 81, 84, 85, 89, 90, 91, 92, 93, 97, 101, 103, 104, 105, 109, 115, 116, 118, 123, 124, 126, 131, 132, 141, 14...…”

Section: Related Workmentioning

confidence: 99%

Measuring Global Disease with Wikipedia

Priedhorsky

Osthus

Daughton

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

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Notifiable infectious disease surveillance with data collected by search engine

Cited by 28 publications

References 9 publications

Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation

Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation

Design Choices for Automated Disease Surveillance in the Social Web

Measuring Global Disease with Wikipedia

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