The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018–2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d’Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks. Data are available in a public, open access repository.

中文翻译：

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如何改善疫情应对：刚果民主共和国东部埃博拉病毒综合疫情分析案例研究

爆发分析的新兴领域呼吁关注来自多个来源的数据的需求，以便为管理传染病爆发的循证决策提供信息。迄今为止，这些方法还没有系统地整合来自社会和行为科学的证据。在 2018-2020 年刚果民主共和国东部埃博拉病毒爆发期间，以 Cellulle d'Analyse en Sciences Sociales（社会科学分析小组）的形式出现了一种创新的解决方案，可以系统和及时地生成综合和可操作的社会科学证据（ CASS)，一个社会科学分析单元。CASS 与流行病学小组下的数据科学家和流行病学家密切合作，以产生综合爆发分析 (IOA)，其中定量流行病学分析与行为实地研究和社会科学分析相辅相成，以帮助更好地解释和理解爆发动态的驱动因素和障碍。CASS 的主要活动是进行对决策者有用的可操作的社会科学分析。这包括确保研究问题是相关的，由来自该领域的流行病学数据驱动，研究可以快速进行（即通常在几天内），定期和系统地向合作伙伴提交研究结果，并与响应参与者共同制定建议. 还随着时间的推移监控基于 CASS 分析的建议的实施情况，以衡量它们对响应操作的影响。本练习论文介绍了 CASS 逻辑模型，通过基于现场的外部主导咨询开发，并记录了有助于 CASS 和 IOA 的有用性和适应性的关键因素，以指导未来疫情的复制。数据可在公共、开放访问的存储库中获得。