KhudaBukhsh Wasiur R, Bastian Caleb Deen, Wascher Matthew, Klaus Colin, Sahai Saumya Yashmohini, Weir Mark, Kenah Eben, Root Elisabeth, Tien Joseph H, Rempala Grzegorz
School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom.
Applied Mathematics, Princeton University and Massive Dynamics, Princeton, NJ, USA.
medRxiv. 2022 Jul 29:2022.07.27.22278117. doi: 10.1101/2022.07.27.22278117.
As the Coronavirus 2019 (COVID-19) disease started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at the Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: 1) A Dynamic Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. 2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology has been made available publicly.
We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.
随着2019冠状病毒病（COVID-19）在俄亥俄州开始迅速传播，俄亥俄州立大学（OSU）传染病研究所（IDI）内的生态、流行病学和人口健康（EEPH）项目主动向俄亥俄州卫生部（ODH）提供疫情建模和决策分析支持。本文描述了OSU/IDI应对建模团队用于预测该州新感染病例的全州范围情况以及潜在医院负担的方法。该方法有两个组成部分：1）一种基于动态生存分析（DSA）的统计方法，用于进行参数推断、全州范围预测和不确定性量化。2）一个地理组成部分，将全州范围的预测计数下推至全州潜在医院负担情况。我们用公开可用数据展示了整体方法。该方法的Python实现已公开提供。
我们提出了一种名为动态生存分析（DSA）的新颖统计方法，用于对具有不完整数据的疫情曲线进行建模。DSA方法相对于标准统计方法具有优势，主要是因为它不需要易感人群规模、疾病总体流行率以及疫情曲线形状的先验知识。该研究背后的主要动机是在疫情早期获得俄亥俄州COVID-19病例数预测以及由此产生的医院负担情况。所提出的方法应用于俄亥俄州的COVID-19发病率数据，以支持俄亥俄州卫生部（ODH）和俄亥俄医院协会（OHA）对该州每个医院集水区（HCA）的医院负担进行预测。
