用于预测新冠肺炎奥密克戎变异株感染人数的数据驱动深度学习神经网络。

Ebenezer O Oluwasakin, Abdul Q M Khaliq
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引用次数: 0

摘要

传染病流行病对医疗和公共卫生从业者来说是一个挑战。它们需要及时治疗,但实时识别和定义流行病具有挑战性。了解传染病流行的预测可以评估和预防疾病的影响。实时工作的流行病数学模型是预防疾病的重要工具,数据驱动的深度学习使识别数学模型中参数的实用算法成为可能。本文将SIR模型简化为一个包含常参数和含时函数的逻辑微分方程。与时间相关的函数导致了常数、有理和对偶模型。这些模型使用现有数据中的几个恒定参数来预测报告感染新冠肺炎奥密克戎变异株的时间和人数。这三个模型中有两个,理性模型和双国家模型,为采取严格缓解措施的国家提供了准确的预测,但未能为采取部分缓解措施的各国提供准确的预测。因此,我们引入了一个基于神经网络的时间序列模型,以预测在采取部分和严格缓解措施的特定国家报告感染新冠肺炎奥密克戎变异株的时间和人数。介绍了一种基于物流信息的神经网络算法。该算法将特定国家报告感染新冠肺炎奥密克戎变异株的每日和累计人数作为输入。该算法有助于从可用数据中为每个模型确定涉及几个恒定参数的分析解。使用葡萄牙、意大利和中国的奥密克戎变异株数据的误差指标证明了这些模型的准确性。我们的研究结果表明,由于疫情的长期现有数据,常数模型无法准确预测被观察国家新冠肺炎奥密克戎变异株的每日或累计感染情况。然而,理性和双向模型准确预测了采取严格缓解措施的国家的累计感染,但在预测每日感染方面存在不足。此外,这两种模式在采取部分缓解措施的国家表现不佳。值得注意的是,时间序列模型因其多功能性而脱颖而出,无论缓解措施的严格程度如何,它都能有效预测各国的每日感染和累计感染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.

Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.

Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.

Data-Driven Deep Learning Neural Networks for Predicting the Number of Individuals Infected by COVID-19 Omicron Variant.

Infectious disease epidemics are challenging for medical and public health practitioners. They require prompt treatment, but it is challenging to recognize and define epidemics in real time. Knowing the prediction of an infectious disease epidemic can evaluate and prevent the disease's impact. Mathematical models of epidemics that work in real time are important tools for preventing disease, and data-driven deep learning enables practical algorithms for identifying parameters in mathematical models. In this paper, the SIR model was reduced to a logistic differential equation involving a constant parameter and a time-dependent function. The time-dependent function leads to constant, rational, and birational models. These models use several constant parameters from the available data to predict the time and number of people reported to be infected with the COVID-19 Omicron variant. Two out of these three models, rational and birational, provide accurate predictions for countries that practice strict mitigation measures, but fail to provide accurate predictions for countries that practice partial mitigation measures. Therefore, we introduce a time-series model based on neural networks to predict the time and number of people reported to be infected with the COVID-19 Omicron variant in a given country that practices both partial and strict mitigation measures. A logistics-informed neural network algorithm was also introduced. This algorithm takes as input the daily and cumulative number of people who are reported to be infected with the COVID-19 Omicron variant in the given country. The algorithm helps determine the analytical solution involving several constant parameters for each model from the available data. The accuracy of these models is demonstrated using error metrics on Omicron variant data for Portugal, Italy, and China. Our findings demonstrate that the constant model could not accurately predict the daily or cumulative infections of the COVID-19 Omicron variant in the observed country because of the long series of existing data of the epidemics. However, the rational and birational models accurately predicted cumulative infections in countries adopting strict mitigation measures, but they fell short in predicting the daily infections. Furthermore, both models performed poorly in countries with partial mitigation measures. Notably, the time-series model stood out for its versatility, effectively predicting both daily and cumulative infections in countries irrespective of the stringency of their mitigation measures.

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