Nagraj V P, Benefield Amy E, Williams Desiree, Turner Stephen D
Signature Science, LLC, Charlottesville, Virginia, United States of America.
PLoS One. 2025 Mar 28;20(3):e0320442. doi: 10.1371/journal.pone.0320442. eCollection 2025.
Methods for reviewing epidemiological signals are necessary to building and maintaining data-driven public health capabilities. We have developed a novel approach for assessing the plausibility of infectious disease forecasts and surveillance data. The PLANES (PLausibility ANalysis of Epidemiological Signals) methodology is designed to be multi-dimensional and flexible, yielding an overall score based on individual component assessments that can be applied at various temporal and spatial granularities. Here we describe PLANES, provide a demonstration analysis, and discuss how to use the open-source rplanes R package. PLANES aims to enable modelers and public health end-users to evaluate forecast plausibility and surveillance data integrity, ultimately improving early warning systems and informing evidence-based decision-making.
审查流行病学信号的方法对于建立和维持数据驱动的公共卫生能力至关重要。我们开发了一种新方法来评估传染病预测和监测数据的可信度。PLANES（流行病学信号可信度分析）方法旨在具有多维度和灵活性，根据各个组成部分的评估得出一个总体分数，该分数可应用于不同的时间和空间粒度。在此，我们描述PLANES，提供一个示范分析，并讨论如何使用开源的rplanes R包。PLANES旨在使建模人员和公共卫生终端用户能够评估预测的可信度和监测数据的完整性，最终改进早期预警系统并为循证决策提供依据。
