Biosurveillance: A Review and Update

Kman, Nicholas; Bachmann, Daniel

doi:10.1155/2012/301408

Cited by 52 publications

(38 citation statements)

References 24 publications

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“…Syndromic surveillance systems actively monitor data in real time to identify potential signals of illnesses that may require further response or investigation [26, 27]. One could build on syndromic surveillance by leveraging electronic sources of healthcare and environmental data and repurpose it for surveillance and clinical research.…”

Section: Discussionmentioning

confidence: 99%

Quantitative bias analysis in an asthma study of rescue-recovery workers and volunteers from the 9/11 World Trade Center attacks

Jurek

Maldonado

2016

Annals of Epidemiology

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Purpose When learning bias analysis, epidemiologists are taught to quantitatively adjust for multiple biases by correcting study results in the reverse order of the error sequence. To understand the error sequence for a particular study, one must carefully examine the health study’s epidemiologic data-generating process. In this manuscript, we describe the unique data-generating process of a man-made disaster epidemiologic study. Methods We described the data-generating process and conducted a bias analysis for a study associating September 11, 2001 dust cloud exposure and self-reported newly-physician diagnosed asthma among rescue-recovery workers and volunteers. We adjusted an odds ratio estimate for the combined effect of missing data, outcome misclassification, and nonparticipation. Results Under our assumptions about systematic error, the odds ratios adjusted for all three biases ranged from 1.33 to 3.84. Most of the adjusted estimates were greater than the observed odds ratio of 1.77 and were outside the 95% confidence limits (1.55, 2.01). Conclusions Man-made disasters present some situations that are not observed in other areas of epidemiology. Future epidemiologic studies of disasters could benefit from a proactive approach that focuses on the technical aspect of data collection and gathers information on bias parameters to provide more meaningful interpretations of results.

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Section: Discussionmentioning

confidence: 99%

Quantitative bias analysis in an asthma study of rescue-recovery workers and volunteers from the 9/11 World Trade Center attacks

Jurek

Maldonado

2016

Annals of Epidemiology

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“…Clinical labs play a critical role in surveillance of infectious diseases. For example, the Laboratory Response Network (LRN), consisting of over 120 biological laboratories, provides active surveillance of a number of disease agents in humans ranging from mild (e.g., non-pathogenic E. coli and Staphylococcus aureus) to severe (e.g., Ebola and Marburg), based on clinical or environmental samples [4]. Other systems monitor non-traditional public health indicators such as school absenteeism rates, over-thecounter medication sales, 911 calls, veterinary data, and ambulance run data.…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…The majority of current systems rely on syndromic surveillance data (i.e., about symptoms) including clinical diagnoses, chief complaints, school and work absenteeism, illness-related 911 calls, and emergency room admissions [86]. …”

Section: Related Workmentioning

confidence: 99%

“…For example, the Laboratory Response Network, consisting of over 120 biological laboratories, provides active surveillance of a number of diseases ranging from mild (e.g., non-pathogenic E. coli and Staphylococcus aureus ) to severe (e.g., Ebola and Marburg), based on clinical or environmental samples [86]. Other systems monitor non-traditional public health indicators such as school absenteeism rates, over-the-counter medication sales, 911 calls, veterinary data, and ambulance runs.…”

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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Biosurveillance: A Review and Update

Cited by 52 publications

References 24 publications

Quantitative bias analysis in an asthma study of rescue-recovery workers and volunteers from the 9/11 World Trade Center attacks

Quantitative bias analysis in an asthma study of rescue-recovery workers and volunteers from the 9/11 World Trade Center attacks

Global Disease Monitoring and Forecasting with Wikipedia

Measuring Global Disease with Wikipedia

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