利用非参数霍克斯模型检测 SARS-Cov-2 的激增

IF 3 3区 医学 Q2 INFECTIOUS DISEASES
Sophie Phillips , George Mohler , Frederic Schoenberg
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引用次数: 0

摘要

霍克斯点过程模型已被证明能高精度地预测包括 SARS-CoV-2 (Covid-19)在内的流行性疾病的每日新增病例数。在此,我们探讨了霍克斯模型预测美国 Covid-19 突增病例的准确性。我们使用霍克斯模型估计了美国 50 个州中每个州的 Covid-19 病例数的有效繁殖率 Rt 和传播密度参数,然后用简单的指数平滑法预测了未来几周的 Rt。基于 Rt>x 的分类器仅使用截至 2020 年 8 月至 2021 年 12 月的数据预测每周即将出现的病例激增。在误报率低于 5%的情况下,基于 Rt 的预测比基于平滑原始病例数数据的预测更准确,Rt>1.39 的最高准确率达到 90%。最佳决策边界使用 Rt 和观测数据的组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Detection of surges of SARS-Cov-2 using nonparametric Hawkes models

Detection of surges of SARS-Cov-2 using nonparametric Hawkes models
Hawkes point process models have been shown to forecast the number of daily new cases of epidemic diseases, including SARS-CoV-2 (Covid-19), with high accuracy. Here, we explore how accurately Hawkes models forecast surges of Covid-19 in the United States. We use Hawkes models to estimate the effective reproduction rate Rt and transmission density parameters for Covid-19 case counts in each of the 50 United States, then forecast Rt in future weeks with simple exponential smoothing. A classifier based on Rt>x is applied to predict upcoming surges in cases each week from August 2020 to December 2021, using only data available up to that week. At false alarm rates below 5%, the forecasts based on Rt are correct more often than forecasts based on smoothing the raw case count data, achieving a maximum accuracy of 90% with Rt>1.39. The optimal decision boundary uses a combination of Rt and observed data.
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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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