Enhancing seasonal influenza projections: A mechanistic metapopulation model for long-term scenario planning

IF 3 3区 医学 Q2 INFECTIOUS DISEASES
James Turtle, Michal Ben-Nun, Pete Riley
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引用次数: 0

Abstract

In temperate regions, annual preparation by public health officials for seasonal influenza requires early-season long-term projections. These projections are different from short-term (e.g., 1–4 weeks ahead) forecasts that are typically updated weekly. Whereas short-term forecasts estimate what “will” likely happen in the near term, the goal of scenario projections is to guide long-term decision-making using “what if” scenarios. We developed a mechanistic metapopulation model and used it to provide long-term influenza projections to the Flu Scenario Modeling Hub. The scenarios differed in their assumptions about influenza vaccine effectiveness and prior immunity. The parameters of the model were inferred from early season hospitalization data and then simulated forward in time until June 3, 2023. We submitted two rounds of projections (mid-November and early December), with the second round being a repeat of the first with three more weeks of data (and consequently different model parameters). In this study, we describe the model, its calibration, and projections targets. The scenario projection outcomes for two rounds are compared with each other at state and national level reported daily hospitalizations. We show that although Rounds 2 and 3 were identical in definition, the addition of three weeks of data produced an improvement to model fits. These changes resulted in earlier projections for peak incidence, lower projections for peak magnitude and relatively small changes to cumulative projections. In both rounds, all four scenarios presented conceivable outcomes, with some scenarios agreeing well with observations. We discuss how to interpret this agreement, emphasizing that this does not imply that one scenario or another provides the ground truth. Our model's performance suggests that its underlying assumptions provided plausible bounds for what could happen during an influenza season following two seasons of low circulation. We suggest that such projections would provide actionable estimates for public health officials.

加强季节性流感预测:用于长期情景规划的机制性元人群模型
在温带地区,公共卫生官员每年都要为季节性流感做好准备,这就需要在季初进行长期预测。这些预测不同于通常每周更新的短期(如提前 1-4 周)预测。短期预测估计的是近期 "将 "可能发生的情况,而情景预测的目标则是利用 "如果 "情景来指导长期决策。我们开发了一个机理元种群模型,并将其用于向流感情景建模中心提供长期流感预测。这些情景在流感疫苗有效性和先期免疫力方面的假设各不相同。该模型的参数是根据季初住院数据推断出来的,然后向前模拟至 2023 年 6 月 3 日。我们提交了两轮预测(11 月中旬和 12 月初),第二轮是第一轮的重复,增加了三周的数据(因此模型参数也不同)。在本研究中,我们介绍了模型、校准和预测目标。我们将两轮的情景预测结果与州和国家层面报告的每日住院人数进行了比较。我们发现,尽管第 2 轮和第 3 轮在定义上完全相同,但新增的三周数据改善了模型拟合效果。这些变化导致发病高峰预测提前,高峰规模预测降低,累计预测变化相对较小。在两轮预测中,所有四种方案都提出了可以想象的结果,其中一些方案与观测结果非常吻合。我们讨论了如何解释这种一致性,强调这并不意味着某一种方案提供了基本事实。我们模型的表现表明,其基本假设为两个低流行季节之后的流感季节可能发生的情况提供了合理的范围。我们认为,这种预测将为公共卫生官员提供可行的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
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