About the project

WP2 Outbreak detection

WP leads: SSI (DK) and RKI (DE)

The SARS-CoV-2 pandemic has highlighted that timely sharing of laboratory test results is crucial for an informed response during an unfolding epidemic. Many countries experienced challenges and delays in sharing of test results in a timely fashion, for instance due to lack of capacity, fragmented systems, or lack of digitalization. A specific challenge is the sharing of genetic data, due to its complexity.

Infectious disease outbreaks can have a considerable impact on the health and health systems of countries. Timely and accurate detection of such events will help to contain further spread of disease and reduce harmful consequences. Automated tools can help manage and foster analysis across different datasets. Especially in context of limited human resources or high workload, this can contribute to a comprehensive surveillance and timely implementation of measures. Many countries do not use any methods for automatic outbreak detection yet or use methods that have been designed for other surveillance systems and datasets than their own. Also, there are no extensive evaluations of outbreak detection methods for a broad range of surveillance data.

The objective of WP2 Outbreak detection is to support outbreak detection and pandemic preparedness by improving real time surveillance for a coordinated response and timely manner. By improving national surveillance systems, the goal is to strengthen overall surveillance in Europe.

This WP is structured in two main technical tasks (Improving Laboratory Based-Reporting and Outbreak & Signal Detection) with associated subtasks including pilots in selected piloting sites. Lessons learnt from the pilot projects are envisaged to be shared with other participating countries in form of reports, training material, and during site visits.

Improving laboratory-based reporting

Timely and reliable laboratory data is vital for infectious disease surveillance and an informed response. The importance of this has become particularly apparent during the unfolding of the SARS-CoV-2 pandemic, where many countries encountered challenges, such as insufficient timeliness in sharing laboratory test results, for instance due to lack of capacity, fragmented systems, or lack of digitalization. A specific challenge centered on the sharing of genetic data, due to its complexity. Task 1 on Improving Laboratory-Based Reporting therefore aims to make an inventory of current limitations and improving national laboratory surveillance systems through country-specific pilots.

Task 1 started off with a survey on mapping needs and gaps with regards to laboratory-based surveillance among countries of the UNITED4Surveillance consortium. The survey focused on general, legal, technical (data & IT), policy and organizational, as well as financial aspects and identified concrete gaps. Results were presented during a workshop and were summarized in a deliverable report. During the workshop, selected countries also further introduced their respective laboratory-based surveillance systems, followed by discussions on joint challenges. Another subtask of task 1 focused on data standards and exploring a logical data model for genotyping/subtyping data. This work started by a workshop during which selected countries presented the data model of their respective laboratory surveillance database, followed by a discussion on what an ideal data model should look like. Conclusions from this work were summarized in a milestone report. Four countries – Denmark, Finland, the Netherlands and Norway – have furthermore started pilots on either setting up a new open-source sequence database or upgrading existing laboratory surveillance systems, respectively. Lessons learnt and experiences from the respective pilots are shared among piloting countries during site visits, which have started and will continue throughout 2024.

Outbreak & Signal Detection

Task 2 on Outbreak & Signal Detection focuses on improving algorithms for outbreak detection based on routine surveillance data for infectious diseases. Through early detection of infectious disease outbreaks, the further spread of diseases can potentially be contained and harmful consequences can be reduced. Automated signal detection tools can thereby help to identify specific events of concern and foster automated data analysis. The timelier and more reliable the signals are, the greater their contribution to comprehensive surveillance and effective implementation of outbreak management measures.

The work under task 2 started off with a survey and workshop aiming to get an overview of the types of surveillance systems with and without implemented outbreak detection methods of participating countries. Further, the countries’ functional and technical requirements for the tool were collected and different outbreak detection methods were systematically evaluated using real world surveillance data from participating countries. Based on these insights and needs, the first version of a Signal Detection Tool was jointly developed by data scientists from Austria, Germany, Finland and Denmark. The tool was deployed to ten piloting countries at the beginning of April 2024 and subsequently the use and user-friendliness of the tool will be evaluated. It is planned to make the tool publicly available.

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