A tight fit of the SIR dynamic epidemic model to daily cases of COVID-19 reported during the 2021-2022 Omicron surge in New York City: A novel approach.

IF 1.6 3区 医学 Q3 HEALTH CARE SCIENCES & SERVICES Statistical Methods in Medical Research Pub Date : 2024-10-01 DOI:10.1177/09622802241277956
Jeffrey E Harris
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Abstract

We describe a novel approach for recovering the underlying parameters of the SIR dynamic epidemic model from observed data on case incidence. We formulate a discrete-time approximation of the original continuous-time model and search for the parameter vector that minimizes the standard least squares criterion function. We show that the gradient vector and matrix of second-order derivatives of the criterion function with respect to the parameters adhere to their own systems of difference equations and thus can be exactly calculated iteratively. Applying our new approach, we estimated a four-parameter SIR model from daily reported cases of COVID-19 during the SARS-CoV-2 Omicron/BA.1 surge of December 2021-March 2022 in New York City. The estimated SIR model showed a tight fit to the observed data, but less so when we excluded residual cases attributable to the Delta variant during the initial upswing of the wave in December. Our analyses of both the real-world COVID-19 data and simulated case incidence data revealed an important problem of weak parameter identification. While our methods permitted for the separate estimation of the infection transmission parameter and the infection persistence parameter, only a linear combination of these two key parameters could be estimated with precision. The SIR model appears to be an adequate reduced-form description of the Omicron surge, but it is not necessarily the correct structural model. Prior information above and beyond case incidence data may be required to sharply identify the parameters and thus distinguish between alternative epidemic models.

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将 SIR 动态流行病模型与纽约市 2021-2022 年 Omicron 疫情激增期间报告的 COVID-19 每日病例紧密拟合:一种新方法。
我们介绍了一种从病例发生率观测数据中恢复 SIR 动态流行病模型基本参数的新方法。我们提出了原始连续时间模型的离散时间近似值,并寻找能使标准最小二乘法准则函数最小化的参数向量。我们证明,判据函数相对于参数的梯度向量和二阶导数矩阵遵循各自的差分方程组,因此可以通过迭代精确计算。应用我们的新方法,我们从 2021 年 12 月至 2022 年 3 月纽约市 SARS-CoV-2 Omicron/BA.1 高峰期间每日报告的 COVID-19 病例中估算出了一个四参数 SIR 模型。估算出的 SIR 模型与观察到的数据非常吻合,但当我们剔除了 12 月病例潮最初上升阶段的三角洲变异体残留病例后,与观察到的数据的吻合程度就降低了。我们对实际 COVID-19 数据和模拟病例发病率数据的分析表明了一个重要的问题,即参数识别能力较弱。虽然我们的方法允许对感染传播参数和感染持续参数进行单独估计,但只能对这两个关键参数的线性组合进行精确估计。SIR 模型似乎是对奥米克龙激增的充分简化描述,但并不一定是正确的结构模型。除了病例发生率数据之外,可能还需要其他先验信息来精确确定参数,从而区分其他流行病模型。
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
Statistical Methods in Medical Research
Statistical Methods in Medical Research 医学-数学与计算生物学
CiteScore
4.10
自引率
4.30%
发文量
127
审稿时长
>12 weeks
期刊介绍: Statistical Methods in Medical Research is a peer reviewed scholarly journal and is the leading vehicle for articles in all the main areas of medical statistics and an essential reference for all medical statisticians. This unique journal is devoted solely to statistics and medicine and aims to keep professionals abreast of the many powerful statistical techniques now available to the medical profession. This journal is a member of the Committee on Publication Ethics (COPE)
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