Murph Alexander C, Gibson G Casey, Amona Elizabeth B, Beesley Lauren J, Castro Lauren A, Del Valle Sara Y, Osthus Dave
Statistical Sciences, Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Department of Statistical Sciences & Operations Research, Virginia Commonwealth University, Richmond, Virginia, United States of America.
PLoS Comput Biol. 2025 Jun 23;21(6):e1013203. doi: 10.1371/journal.pcbi.1013203. eCollection 2025 Jun.
The Method of Analogues (MOA) has gained popularity in the past decade for infectious disease forecasting due to its non-parametric nature. In MOA, the local behavior observed in a time series is matched to the local behaviors of several historical time series. The known values that directly follow the historical time series that best match the observed time series are used to calculate a forecast. This non-parametric approach leverages historical trends to produce forecasts without extensive parameterization, making it highly adaptable. However, MOA is limited in scenarios where historical data is sparse. This limitation was particularly evident during the early stages of the COVID-19 pandemic, where the emerging global epidemic had little-to-no historical data. In this work, we propose a new method inspired by MOA, called the Synthetic Method of Analogues (sMOA). sMOA replaces historical disease data with a library of synthetic data that describe a broad range of possible disease trends. This model circumvents the need to estimate explicit parameter values by instead matching segments of ongoing time series data to a comprehensive library of synthetically generated segments of time series data. We demonstrate that sMOA has competitive performance with state-of-the-art infectious disease forecasting models, out-performing 78% of models from the COVID-19 Forecasting Hub in terms of averaged Mean Absolute Error and 76% of models from the COVID-19 Forecasting Hub in terms of averaged Weighted Interval Score. Additionally, we introduce a novel uncertainty quantification methodology designed for the onset of emerging epidemics. Developing versatile approaches that do not rely on historical data and can maintain high accuracy in the face of novel pandemics is critical for enhancing public health decision-making and strengthening preparedness for future outbreaks.
类比法（MOA）因其非参数性质，在过去十年中在传染病预测领域颇受欢迎。在MOA中，将时间序列中观察到的局部行为与多个历史时间序列的局部行为进行匹配。与观察到的时间序列最匹配的历史时间序列之后直接出现的已知值用于计算预测。这种非参数方法利用历史趋势进行预测，无需大量参数设置，因此具有高度适应性。然而，在历史数据稀少的情况下，MOA存在局限性。这一局限性在COVID-19大流行的早期阶段尤为明显，当时新出现的全球疫情几乎没有历史数据。在这项工作中，我们提出了一种受MOA启发的新方法，称为合成类比法（sMOA）。sMOA用一个描述广泛可能疾病趋势的合成数据文库取代历史疾病数据。该模型通过将正在进行的时间序列数据段与合成生成的时间序列数据段的综合文库进行匹配，避免了估计明确参数值的需要。我们证明，sMOA在性能上与最先进的传染病预测模型具有竞争力，在平均绝对误差方面优于COVID-19预测中心78%的模型，在平均加权区间得分方面优于COVID-19预测中心76%的模型。此外，我们还引入了一种针对新出现疫情的新型不确定性量化方法。开发不依赖历史数据且在面对新型大流行时能保持高精度的通用方法，对于加强公共卫生决策和增强对未来疫情爆发的防范能力至关重要。
