Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance.

BACKGROUND: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. METHODS: We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. RESULTS: Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. CONCLUSIONS: The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Dissemination of information in event-based surveillance, a case study of Avian Influenza.

Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.

Animal disease surveillance: How to represent textual data for classifying epidemiological information.

The value of informal sources in increasing the timeliness of disease outbreak detection and providing detailed epidemiological information in the early warning and preparedness context is recognized. This study evaluates machine learning methods for classifying information from animal disease-related news at a fine-grained level (i.e., epidemiological topic). We compare two textual representations, the bag-of-words method and a distributional approach, i.e., word embeddings. Both representations performed well for binary relevance classification (F-measure of 0.839 and 0.871, respectively). Bag-of-words representation was outperformed by word embedding representation for classifying sentences into fine-grained epidemiological topics (F-measure of 0.745). Our results suggest that the word embedding approach is of interest in the context of low-frequency classes in a specialized domain. However, this representation did not bring significant performance improvements for binary relevance classification, indicating that the textual representation should be adapted to each classification task.

Elaboration of a new framework for fine-grained epidemiological annotation.

Event-based surveillance (EBS) gathers information from a variety of data sources, including online news articles. Unlike the data from formal reporting, the EBS data are not structured, and their interpretation can overwhelm epidemic intelligence (EI) capacities in terms of available human resources. Therefore, diverse EBS systems that automatically process (all or part of) the acquired nonstructured data from online news articles have been developed. These EBS systems (e.g., GPHIN, HealthMap, MedISys, ProMED, PADI-web) can use annotated data to improve the surveillance systems. This paper describes a framework for the annotation of epidemiological information in animal disease-related news articles. We provide annotation guidelines that are generic and applicable to both animal and zoonotic infectious diseases, regardless of the pathogen involved or its mode of transmission (e.g., vector-borne, airborne, by contact). The framework relies on the successive annotation of all the sentences from a news article. The annotator evaluates the sentences in a specific epidemiological context, corresponding to the publication date of the news article.

PADI-web 3.0: A new framework for extracting and disseminating fine-grained information from the news for animal disease surveillance.

PADI-web (Platform for Automated extraction of animal Disease Information from the web) is a biosurveillance system dedicated to monitoring online news sources for the detection of emerging animal infectious diseases. PADI-web has collected more than 380,000 news articles since 2016. Compared to other existing biosurveillance tools, PADI-web focuses specifically on animal health and has a fully automated pipeline based on machine-learning methods. This paper presents the new functionalities of PADI-web based on the integration of: (i) a new fine-grained classification system, (ii) automatic methods to extract terms and named entities with text-mining approaches, (iii) semantic resources for indexing keywords and (iv) a notification system for end-users. Compared to other biosurveillance tools, PADI-web, which is integrated in the French Platform for Animal Health Surveillance (ESA Platform), offers strong coverage of the animal sector, a multilingual approach, an automated information extraction module and a notification tool configurable according to end-user needs.

ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis.

Here, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a single corpus (intra), or from corpora (inter). We assessed the two approaches, the corpus or corpora to be analysed and the type of statistical measures used. Our experimental findings revealed that the proposed methodology could be used: (1) to efficiently extract representative, discriminant and new terms from a given corpus or corpora, and (2) to provide quantitative and qualitative analyses on these terms regarding the study domain.

Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence.

Event-based surveillance (EBS) systems monitor a broad range of information sources to detect early signals of disease emergence, including new and unknown diseases. In December 2019, a newly identified coronavirus emerged in Wuhan (China), causing a global coronavirus disease (COVID-19) pandemic. A retrospective study was conducted to evaluate the capacity of three event-based surveillance (EBS) systems (ProMED, HealthMap and PADI-web) to detect early COVID-19 emergence signals. We focused on changes in online news vocabulary over the period before/after the identification of COVID-19, while also assessing its contagiousness and pandemic potential. ProMED was the timeliest EBS, detecting signals one day before the official notification. At this early stage, the specific vocabulary used was related to 'pneumonia symptoms' and 'mystery illness'. Once COVID-19 was identified, the vocabulary changed to virus family and specific COVID-19 acronyms. Our results suggest that the three EBS systems are complementary regarding data sources, and all require timeliness improvements. EBS methods should be adapted to the different stages of disease emergence to enhance early detection of future unknown disease outbreaks.

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

Since 2013, the French Animal Health Epidemic Intelligence System (in French: Veille Sanitaire Internationale, VSI) has been monitoring signals of the emergence of new and exotic animal infectious diseases worldwide. Once detected, the VSI team verifies the signals and issues early warning reports to French animal health authorities when potential threats to France are detected. To improve detection of signals from online news sources, we designed the Platform for Automated extraction of Disease Information from the web (PADI-web). PADI-web automatically collects, processes and extracts English-language epidemiological information from Google News. The core component of PADI-web is a combined information extraction (IE) method founded on rule-based systems and data mining techniques. The IE approach allows extraction of key information on diseases, locations, dates, hosts and the number of cases mentioned in the news. We evaluated the combined method for IE on a dataset of 352 disease-related news reports mentioning the diseases involved, locations, dates, hosts and the number of cases. The combined method for IE accurately identified (F-score) 95% of the diseases and hosts, respectively, 85% of the number of cases, 83% of dates and 80% of locations from the disease-related news. We assessed the sensitivity of PADI-web to detect primary outbreaks of four emerging animal infectious diseases notifiable to the World Organisation for Animal Health (OIE). From January to June 2016, PADI-web detected signals for 64% of all primary outbreaks of African swine fever, 53% of avian influenza, 25% of bluetongue and 19% of foot-and-mouth disease. PADI-web timely detected primary outbreaks of avian influenza and foot-and-mouth disease in Asia, i.e. they were detected 8 and 3 days before immediate notification to OIE, respectively.