The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualisation. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case (S4). In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data (S5). A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research.

中文翻译：

---

通过知识管理及时告知流行病（研究）反应-Zika病毒使用案例

病理生理学研究对流行病的反应通常发生在流行病之后，因此，对改善患者的预后或在流行病期间发展告知临床治疗策略的高质量证据影响很小甚至没有影响。对严重传染病暴发的流行病学（研究）反应的快速而有根据的指导需要对现有病理生理学知识进行快速汇编和整合。作为案例研究，我们选择了始于2015年的Zika病毒（ZIKV）爆发，以开发概念验证知识库。为了从可用资源中提取数据并构建可计算的，易于处理的和全面的分子相互作用图，我们将通用知识管理软件应用于文献挖掘，专家知识策划，数据集成，报告和可视化。包括临床医生，病毒学家，生物信息学家和知识管理专家在内的多学科专家团队遵循了预先定义的工作流程，以快速整合和评估可用证据。传统方法通常需要数月的时间来梳理现有文献，而最初的ZIKV知识库（ZIKA KB）在几周内就完成了。最近，我们使用来自2016年以来出版的大量文献中的其他精选数据对ZIKA KB进行了更新，并通过Web界面和分步指南将其公开发布，以确保所描述用例（S4）的可重复性。此外，还提供了详细的在线用户手册，以使ZIKV研究社区能够生成假设，共享知识，识别知识差距，并以交互方式浏览和解释数据（S5）。生成，验证和完善了爆发期间快速响应的工作流，该工作流也已可用。此处描述的过程可用于及时构建病理生理知识，以应对未来的威胁。由此产生的结构化生物学知识是用于计算数据分析和生成预测模型的有用工具，并为传染病研究开辟了新途径。