Dórea Fernanda C, Revie Crawford W
Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala, Sweden.
Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom.
Front Vet Sci. 2021 Mar 12;8:633977. doi: 10.3389/fvets.2021.633977. eCollection 2021.
The biggest change brought about by the "era of big data" to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex "variety" dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.
“大数据时代”给整体健康，尤其是流行病学带来的最大变化，或许不在于所面对的数据量，而在于其多样性。越来越多的新数据源，包括许多最初并非为健康目的而收集的数据源，如今正被用于流行病学推断和情境分析。整合来自多个数据源的证据带来了重大挑战，但围绕这一主题的讨论常常将数据获取和隐私问题与数据整合及互操作性的实际技术挑战混为一谈。我们审视了在人群健康领域日益复杂的数据“多样性”维度上连接数据、生成信息并支持决策的一些机遇，以使数据驱动的监测超越简单的信号检测，并支持更广泛的监测目标。
