Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System

Arsevska, Elena; Valentin, Sarah; Rabatel, Julien; Hervé, Jocelyn de Goër de; Falala, Sylvain; Lancelot, Renaud; Roche, Mathieu

doi:10.1371/journal.pone.0199960

Cited by 39 publications

(50 citation statements)

References 30 publications

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“…This model has been integrated into the PADI-web system. This dataset is an enriched version of the data that were used in PADI-web (the main difference being that partial candidate labels in PADI-web were all considered as correct) to obtain F-measure scores of 80% for locations, 83% for dates, 95% for diseases, 95% for hosts, and 85% for case numbers [2] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…In the context of epidemiological surveillance on the Web [3] , this dataset [4] contains a set of news articles in English related to animal disease outbreaks, used to train and evaluate the information extraction module of the system PADI-web [2] . It is composed of 532 articles (in JSON) with information about the article itself (e.g., publication date, title, content, URL).…”

Section: Datamentioning

confidence: 99%

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PADI-web corpus: Labeled textual data in animal health domain

2019

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Monitoring animal health worldwide, especially the early detection of outbreaks of emerging pathogens, is one of the means of preventing the introduction of infectious diseases in countries (Collier et al., 2008) [3]. In this context, we developed PADI-web, a Platform for Automated extraction of animal Disease Information from the Web (Arsevska et al., 2016, 2018). PADI-web is a text-mining tool that automatically detects, categorizes and extracts disease outbreak information from Web news articles. PADI-web currently monitors the Web for five emerging animal infectious diseases, i.e., African swine fever, avian influenza including highly pathogenic and low pathogenic avian influenza, foot-and-mouth disease, bluetongue, and Schmallenberg virus infection. PADI-web collects Web news articles in near-real time through RSS feeds. Currently, PADI-web collects disease information from Google News because of its international and multiple language coverage. We implemented machine learning techniques to identify the relevant disease information in texts (i.e., location and date of an outbreak, affected hosts, their numbers and clinical signs). In order to train the model for Information Extraction (IE) from news articles, a corpus in English has been manually labeled by domain experts. This labeled corpus (Rabatel et al., 2017) is presented in this data paper.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

PADI-web corpus: Labeled textual data in animal health domain

2019

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“…All pre-selected 15 papers were then peer-reviewed by both section editors and external reviewers (at least four reviewers per paper). Three papers 8 9 10 were finally selected as best papers (see Table 1 ). A content summary of these selected papers can be found in the appendix of this synopsis.…”

Section: Paper Selectionmentioning

confidence: 99%

Artificial Intelligence for Surveillance in Public Health

Thiébaut

Cossin

Informatics³

2019

Yearb Med Inform

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Objectives: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. Methods: The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 15 candidate best papers. These papers were then peer-reviewed by external reviewers to give the editorial team an enlightened selection of the best papers. Results: Among the 805 references retrieved from PubMed and Web of Science, three were finally selected as best papers. All three papers are about surveillance using digital tools. One study is about the surveillance of flu, another about emerging animal infectious diseases and the last one is about foodborne illness. The sources of information are Google news, Twitter, and Yelp restaurant reviews. Machine learning approaches are most often used to detect signals. Conclusions: Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge.

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“…Also, less controlled information, which may provide important signals and be crucial in determining the success of health risk control, are not included in the evaluation since they do not fit into these well-specified performance assessment methodology. In this context, textual information on the web (e.g., news articles, official disease reports, newsletters, information flows on social networks) are relevant for early detection of emerging infectious disease outbreaks (33). For instance, Barboza et al (34) have shown that the web monitoring systems can detect avian influenza epizootics 12.7 days before the official notification to the World Organization for Animal Health (OIE).…”

Section: Fundaments To Rethink Health Surveillance Valuementioning

confidence: 99%

Valuing Health Surveillance as an Information System: Interdisciplinary Insights

Antoine‐Moussiaux

Vandenberg

Kozlakidis

et al. 2019

Front. Public Health

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The economic evaluation of health surveillance systems and of health information is a methodological challenge, as for information systems in general. Main present threads are considering cost-effectiveness solutions, minimizing costs for a given technically required output, or cost-benefit analysis, balancing costs with economic benefits of duly informed public interventions. The latter option, following a linear command-and-control perspective, implies considering a main causal link between information, decision, action, and health benefits. Yet, valuing information, taking into account its nature and multiple sources, the modalities of its processing cycle, from production to diffusion, decentralized use and gradual building of a shared information capital, constitutes a promising challenge. This work proposes an interdisciplinary insight on the value of health surveillance to get a renewed theoretical framework integrating information and informatics theory and information economics. The reflection is based on a typological approach of value, basically distinguishing between use and non-use values. Through this structured discussion, the main idea is to expand the boundaries of surveillance evaluation, to focus on changes and trends, on the dynamic and networked structure of information systems, on the contribution of diverse data, and on the added value of combining qualitative and quantitative information. Distancing itself from the command-and-control model, this reflection considers the behavioral fundaments of many health risks, as well as the decentralized, progressive and deliberative dimension of decision-making in risk management. The framework also draws on lessons learnt from recent applications within and outside of health sector, as in surveillance of antimicrobial resistance, inter-laboratory networks, the use of big data or web sources, the diffusion of technological products and large-scale financial risks. Finally, the paper poses the bases to think the challenge of a workable approach to economic evaluation of health surveillance through a better understanding of health information value. It aims to avoid over-simplifying the range of health information benefits across society while keeping evaluation within the boundaries of what may be ascribed to the assessed information system.

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Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System

Cited by 39 publications

References 30 publications

PADI-web corpus: Labeled textual data in animal health domain

PADI-web corpus: Labeled textual data in animal health domain

Artificial Intelligence for Surveillance in Public Health

Valuing Health Surveillance as an Information System: Interdisciplinary Insights

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