描述和预测covid - 19进化的大流行方程。

IF 5.9 Q1 Computer Science
Michael Shur
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引用次数: 6

摘要

这项工作的目的是描述COVID-19大流行的动态,包括缓解措施、引入或取消隔离以及引入疫苗接种时和一旦引入疫苗接种的效果。所使用的方法包括通过大流行方程中增长时间常数的演变推导出描述缓解措施的大流行方程,从而导致上升幅度大于下降幅度的不对称大流行曲线和缓解措施。大流行方程预测了隔离解除和企业开放如何导致大流行曲线的峰值。有效的疫苗接种减少了大流行方程预测的每日新感染。许多地区的大流行曲线具有相似的时间依赖性,但随时间变化。从较先进的大流行曲线中提取的大流行方程参数可用于预测大流行仍处于初始阶段的地区的大流行演变。使用多个大流行地点进行参数提取,可以使用引入的大流行方程对预测大流行演变进行不确定性量化。与其他流行病模型相比,我们的方法允许更容易的参数提取,适用于使用人工智能模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Pandemic Equation for Describing and Predicting COVID19 Evolution.

Pandemic Equation for Describing and Predicting COVID19 Evolution.

Pandemic Equation for Describing and Predicting COVID19 Evolution.

Pandemic Equation for Describing and Predicting COVID19 Evolution.

The purpose of this work is to describe the dynamics of the COVID-19 pandemics accounting for the mitigation measures, for the introduction or removal of the quarantine, and for the effect of vaccination when and if introduced. The methods used include the derivation of the Pandemic Equation describing the mitigation measures via the evolution of the growth time constant in the Pandemic Equation resulting in an asymmetric pandemic curve with a steeper rise than a decrease and mitigation measures. The Pandemic Equation predicts how the quarantine removal and business opening lead to a spike in the pandemic curve. The effective vaccination reduces the new daily infections predicted by the Pandemic Equation. The pandemic curves in many localities have similar time dependencies but shifted in time. The Pandemic Equation parameters extracted from the well advanced pandemic curves can be used for predicting the pandemic evolution in the localities, where the pandemics is still in the initial stages. Using the multiple pandemic locations for the parameter extraction allows for the uncertainty quantification in predicting the pandemic evolution using the introduced Pandemic Equation. Compared with other pandemic models our approach allows for easier parameter extraction amenable to using Artificial Intelligence models.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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