Improving probabilistic infectious disease forecasting through coherence.
Improving probabilistic infectious disease forecasting through coherence.
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DOI：
10.1371/journal.pcbi.1007623
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发表时间：
2021-01
影响因子：
4.3
通讯作者：
Osthus D
中科院分区：
文献类型：
--
作者：
Gibson GC;Moran KR;Reich NG;Osthus D
据估计，每年有104亿美元的医疗费用和3140万人次的门诊就诊，流感在美国构成了严重的疾病负担。为了对流感的传播提供洞察力和提前警告，美国疾病控制和预防中心(CDC)在国家和地区层面开展了一项预测加权流感样疾病(WILI)的挑战。许多模型为每个地理单元产生独立的预测，忽略了国家WRI是区域WRI的加权和这一约束，其中权重对应于该区域的人口规模。我们提出了一种新的算法，它将一组独立的预测分布转换为遵守这一约束，我们称之为概率相干。实施概率一致性导致我们在多个流感季节测试的79%的模型的预测技能提高，突显了尊重预测系统的地理等级的重要性。季节性流感在全国范围内造成了重大的公共卫生负担。准确的流感预测可能有助于公共卫生官员分配资源并计划应对新爆发的疫情。美国疾病控制和预防中心(CDC)报告了多个地理单位的流感数据，包括区域和国家，其中国家数据是区域数据的加权总和。为了提高所有提交给疾控中心年度流感预测挑战的模型的流感预测准确性，我们检查了这种地理限制对疾控中心公布的一组独立预测的影响。我们开发了一种新的方法来转换预测密度，以遵守地理约束，尊重地理单元之间的相关结构。这种方法在79%的模型上显示出一致的改进，并且在按目标和测试季节分层时也是如此。我们的方法可以应用于具有地理层次的传染病内外的其他预测系统。
With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy. Seasonal influenza causes a significant public health burden nationwide. Accurate influenza forecasting may help public health officials allocate resources and plan responses to emerging outbreaks. The U.S. Centers for Disease Control and Prevention (CDC) reports influenza data at multiple geographical units, including regionally and nationally, where the national data are by construction a weighted sum of the regional data. In an effort to improve influenza forecast accuracy across all models submitted to the CDC’s annual flu forecasting challenge, we examined the effect of imposing this geographical constraint on the set of independent forecasts, made publicly available by the CDC. We developed a novel method to transform forecast densities to obey the geographical constraint that respects the correlation structure between geographical units. This method showed consistent improvement across 79% of models and that held when stratified by targets and test seasons. Our method can be applied to other forecasting systems both within and outside an infectious disease context that have a geographical hierarchy.
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