Practical usage of computer-supported outbreak detection in five European countries

Hulth, Anette; Andrews, Nick; Ethelberg, Steen; Dreesman, Johannes; Faensen, Daniel; Pelt, Wilfrid van; Schnitzler, J

doi:10.2807/ese.15.36.19658-en

Cited by 40 publications

(10 citation statements)

References 19 publications

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“…However, EARS work with recent data for baselines. Other set of techniques that work with long term data use quasi-Poisson regression and consider seasonality and trends for outbreak detection [15]- [18].…”

Section: Related Workmentioning

confidence: 99%

Towards Data Driven Spatio-Temporal Threshold Identification Based on Cost Effective Public Health Information Management Framework

et al. 2022

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Appropriate public health actions comes from data driven decision support systems. While sophisticated health information exchange framework may be costly in developing countries, the health care delivery system in place provides a promising infrastructure that spans all parts in a region. Therefore, while digital and non digital data is constantly being generated from variety of sources including public and private health sectors, the health care delivery systems remain the primary and most fundamental source for data on population health status. For low and middle income countries with minimum digitization and resource constraints, traditional existing health care delivery system can be taken advantage of, for efficient production and timely transmission and utilisation of data to identify thresholds for disease outbreak in order to improve health status and health system performance. Due to lack of appropriate universally agreed criteria for threshold, defining local thresholds for infectious disease is not only crucial but also more appropriate.In this paper, we present a low cost data driven framework called Health Data Driven Framework (HDDF) through which data generated at health care facilities may be used for threshold detection and alarm generation. We also identify a localized method based on spatio-temporal mining of available data for appropriate threshold identification.INDEX TERMS Data driven public health, public health informatics, threshold identification, infectious diseases, spatio temporal analysis

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Section: Related Workmentioning

confidence: 99%

Towards Data Driven Spatio-Temporal Threshold Identification Based on Cost Effective Public Health Information Management Framework

et al. 2022

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“…Two implementations of the Farrington method, which is currently the method of choice at European public health institutes (Hulth, Andrews, Ethelberg, Dreesman, Faensen, van Pelt, and Schnitzler 2010), exist in the package. First, the original method as described in Farrington et al (1996) is implemented as function farrington.…”

Section: One Size Fits Them All For Count Datamentioning

confidence: 99%

“…The original method is widely used in public health surveillance (Hulth et al 2010). The reason for its success is primarily that it does not need to be fine-tuned for each specific pathogen.…”

Section: One Size Fits Them All For Count Datamentioning

confidence: 99%

Monitoring Count Time Series inR: Aberration Detection in Public Health Surveillance

Salmon¹,

Schumacher²,

Höhle³

2016

J. Stat. Soft.

101

116

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Public health surveillance aims at lessening disease burden by, e.g., timely recognizing emerging outbreaks in case of infectious diseases. Seen from a statistical perspective, this implies the use of appropriate methods for monitoring time series of aggregated case reports. This paper presents the tools for such automatic aberration detection offered by the R package surveillance. We introduce the functionalities for the visualization, modeling and monitoring of surveillance time series. With respect to modeling we focus on univariate time series modeling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. Applications of such modeling include illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g., by spline-modeling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-multinomial modeling. With respect to monitoring we consider detectors based on either a Shewhart-like single timepoint comparison between the observed count and the predictive distribution or by likelihoodratio based cumulative sum methods. Finally, we illustrate how surveillance can support aberration detection in practice by integrating it into the monitoring workflow of a public health institution. Altogether, the present article shows how well surveillance can support automatic aberration detection in a public health surveillance context.

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“…On the other hand, the Farrington algorithm is largely considered a robust and fast method. Thanks to these characteristics, currently it is the method used at European public health institutes [Hulth et al 2010]. This algorithm has a set of parameters to specify.…”

Section: Approach and Rationalementioning

confidence: 99%

Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?

Stewart¹,

Romanò²,

Kanhabua³

et al. 2016

Preprint

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Social media services such as Twitter are a valuable source of information for decision support systems. Many studies have shown that this also holds for the medical domain, where Twitter is considered a viable tool for public health officials to sift through relevant information for the early detection, management, and control of epidemic outbreaks. This is possible due to the inherent capability of social media services to transmit information faster than traditional channels. However, the majority of current studies have limited their scope to the detection of common and seasonal health recurring events (e.g., Influenza-like Illness), partially due to the noisy nature of Twitter data, which makes outbreak detection and management very challenging. Within the European project M-ECO, we developed a Twitter-based Epidemic Intelligence (EI) system, which is designed to also handle a more general class of unexpected and aperiodic outbreaks. In particular, we faced three main research challenges in this endeavor: 1) dynamic classification to manage terminology evolution of Twitter messages, 2) alert generation to produce reliable outbreak alerts analyzing the (noisy) tweet time series, and 3) ranking and recommendation to support domain experts for better assessment of the generated alerts. In this paper, we empirically evaluate our proposed approach to these challenges using real-world outbreak datasets and a large collection of tweets. We validate our solution with domain experts, describe our experiences, and give a more realistic view on the benefits and issues of analyzing social media for public health.

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Practical usage of computer-supported outbreak detection in five European countries

Abstract: Binary file ES_Abstracts_Final_ECDC.txt matches

Cited by 40 publications

References 19 publications

Towards Data Driven Spatio-Temporal Threshold Identification Based on Cost Effective Public Health Information Management Framework

Towards Data Driven Spatio-Temporal Threshold Identification Based on Cost Effective Public Health Information Management Framework

Monitoring Count Time Series inR: Aberration Detection in Public Health Surveillance

Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?

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