Challenges and Opportunities in Emerging Disease Detection Marion Koopmans Erasmus Medical Centre, Rotterdam, Netherlands
A key component of clinical and public health preparedness is the ability to detect emerging infectious diseases (EID) as early as possible, and reliable diagnostics are essential. However, the unpredictable nature of EID makes this a challenging task. EID outbreaks do not present with distinctive clinical signs, the differential diagnostic list may be extensive, and laboratory capacity may be suboptimal, particularly in low-income regions. As a consequence, the occurrence of clusters of human disease often is the first trigger of a more in-depth investigation. Enhancing EID preparedness therefore requires a more granular approach than the simplistic “increased diagnostics” advise. Retrospective investigations have identified common drivers of disease emergence, some of which could be amenable to early warning surveillance. Using this knowledge to design hot spot- or risk-based surveillance has long been debated, but remains patchy at best, as the added costs are not easily accepted in underfunded health systems. Opportunities arise from the use of new catch-all technologies such as metagenomic sequencing and systems serology, and integration of this data with data that captures ecosystem changes potentially affecting disease dynamics. A foreseeable future could be for instance that changes in drivers trigger enhanced agnostic sequencing of clinical samples from potential disease hosts and/or humans with unexplained illness in hot spot regions. Barriers to such applications exist given that human medicine, public health, veterinary medicine and environmental health are organized in silo's. However, new models for data sharing that acknowledge root causes of such barriers may help advance our preparedness.
Sources of funding: EU grants COMPARE (No 643476. , PREPARE (602525), ZON MW TOP grants (91213058)
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