每周顺序贝叶斯更新提高了流行病早期阶段死亡的预测
Weekly sequential Bayesian updating improves prediction of deaths at an early epidemic stage
摘要：
背景资料：2020年初爆发新型冠状病毒疫情后，全球各地的市政当局、地方政府及政策制定者不得不在充满不确定性的情况下规划非药物干预措施。在流行病的早期阶段，没有疫苗或药物治疗，算法预测可以成为一个强大的工具，为地方决策提供信息。然而，当我们复制一个著名的流行病学模型，以告知巴西南部地区的卫生当局时，我们发现该模型过于依赖手动预定的协变量，并且对数据趋势的变化反应过度。研究方法：我们对原始方法提出的四种变体允许访问每日报告的感染数据，并更明确地考虑到病例漏报的情况。两个提议的版本也试图模拟测试报告的延迟。我们模拟了从2020年5月31日到2021年1月31日期间的每周死亡预测。该工作流程使我们能够在第一个校准周后运行更精简的模型。每周更新的Google Mobility数据在每次模拟运行时用作模型的协变量。研究结果：这些变化使模型的反应性显着降低，并且在观察到死亡高峰后更快地适应场景。假设报告的病例被少报，这对模型的稳定性大有好处，而建模时追溯性地添加数据（由于所用数据的“热”性质）对性能的影响可以忽略不计。释义：虽然不像死亡统计那样可靠，但病例统计在与“高估”参数一起建模时，为改进模型的预测提供了一个很好的替代方案，特别是在长期预测和感染高峰之后。
英文摘要：
Background: Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Methods: Our four proposed variations of the original method allow accessing data of daily reported infections and take into account the under-reporting of cases more explicitly. Two of the proposed versions also attempt to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021. That workflow allowed us to run a lighter version of the model after the first calibration week. Google Mobility data, weekly updated, were used as covariates to the model at each simulated run. Findings: The changes made the model significantly less reactive and more rapid in adapting to scenarios after a peak in deaths is observed. Assuming that reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot" nature of the data used) had a negligible impact on performance. Interpretation: Although not as reliable as death statistics, case statistics, when modelled in conjunction with an "overestimate" parameter, provide a good alternative for improving the forecasting of models, especially in long-range predictions and after the peak of an infection wave.
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