Algorithmic hospital catchment area estimation using label propagation.
Algorithmic hospital catchment area estimation using label propagation.
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DOI：
10.1186/s12913-022-08127-7
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发表时间：
2022-06-27
影响因子：
2.8
通讯作者：
Tsaneva-Atanasova, Krasimira
中科院分区：
文献类型：
--
作者：
Challen, Robert J.;Griffith, Gareth J.;Lacasa, Lucas;Tsaneva-Atanasova, Krasimira
医院集水区界定了医院的主要人口，对于评估医院的潜在需求至关重要，例如，由于传染病爆发。我们提出了一种基于标签传播的新算法，用于根据医院的容量和附近人口的人口统计数据估计医院集水区域，而不需要任何医院活动数据。该算法被证明可以生成从细粒度地理区域到更大规模集水区的映射，提供与单个医院或一组医院相关的连续和现实的地理细分。在对基于英国COVID-19爆发期间收集的活动数据的替代方法进行验证时，发现标签传播算法具有高度的一致性，并且具有相似的准确性。该算法可用于在尚未获得活动数据的新情况下对医院集水区进行估计，例如在传染病爆发的早期阶段。在线版本包含补充材料，可在（10.1186/s12913-022-08127-7）获得。
Hospital catchment areas define the primary population of a hospital and are central to assessing the potential demand on that hospital, for example, due to infectious disease outbreaks. We present a novel algorithm, based on label propagation, for estimating hospital catchment areas, from the capacity of the hospital and demographics of the nearby population, and without requiring any data on hospital activity. The algorithm is demonstrated to produce a mapping from fine grained geographic regions to larger scale catchment areas, providing contiguous and realistic subdivisions of geographies relating to a single hospital or to a group of hospitals. In validation against an alternative approach predicated on activity data gathered during the COVID-19 outbreak in the UK, the label propagation algorithm is found to have a high level of agreement and perform at a similar level of accuracy. The algorithm can be used to make estimates of hospital catchment areas in new situations where activity data is not yet available, such as in the early stages of a infections disease outbreak. The online version contains supplementary material available at (10.1186/s12913-022-08127-7).
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