Can neural networks estimate parameters in epidemiology models using real observed data?

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Muhammad Jalil Ahmad, Korhan Günel
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引用次数: 0

Abstract

The primary objective of this study is to address the challenges associated with estimating parameters in mathematical epidemiology models, which are crucial for understanding the dynamics of infectious diseases within a population. The scope of this research includes the development and application of a two-phase neural network for parameter estimation, specifically within the context of epidemic compartmental models. This study presents a novel approach by integrating an extreme learning machine with a heuristic population-based optimization method within a two-phase neural network framework. The networks are driven by a heuristic population-based optimization method, enhancing the accuracy and efficiency of parameter estimation in mathematical epidemiology models. The effectiveness of the method is validated using actual COVID-19 data provided by the Turkish Ministry of Health. The data includes cases categorized as Susceptible, Exposed, Infected, Removed, and Deceased, which are crucial components of epidemic compartmental models. The obtained results highlight the capability of the proposed method to provide insights into the spread of infectious diseases by offering reliable estimates of model parameters. This, in turn, supports better understanding and forecasting of disease dynamics. The methodology provides a significant contribution to the field by offering a new, efficient technique for parameter estimation in epidemiological models.

神经网络能否利用实际观测数据估计流行病学模型中的参数?
本研究的主要目的是解决与数学流行病学模型中参数估计相关的挑战,这对于理解人群中传染病的动态至关重要。本研究的范围包括用于参数估计的两相神经网络的开发和应用,特别是在流行病区室模型的背景下。本研究提出了一种新的方法,在两阶段神经网络框架内将极限学习机与启发式基于种群的优化方法相结合。该网络采用启发式种群优化方法驱动,提高了数学流行病学模型参数估计的准确性和效率。使用土耳其卫生部提供的COVID-19实际数据验证了该方法的有效性。数据包括易感、暴露、感染、移除和死亡病例,这是流行病区隔模型的关键组成部分。获得的结果突出表明,所提出的方法能够通过提供可靠的模型参数估计来深入了解传染病的传播。这反过来又有助于更好地理解和预测疾病动态。该方法为流行病学模型的参数估计提供了一种新的、有效的技术,对该领域做出了重大贡献。
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
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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