Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz, University, Alkharj, Saudi Arabia.
Department of Mathematics, COMSATS University Islamabad, Attock Campus, Pakistan.
PLoS One. 2024 Apr 18;19(4):e0298451. doi: 10.1371/journal.pone.0298451. eCollection 2024.
The paper presents an innovative computational framework for predictive solutions for simulating the spread of malaria. The structure incorporates sophisticated computing methods to improve the reliability of predicting malaria outbreaks. The study strives to provide a strong and effective tool for forecasting the propagation of malaria via the use of an AI-based recurrent neural network (RNN). The model is classified into two groups, consisting of humans and mosquitoes. To develop the model, the traditional Ross-Macdonald model is expanded upon, allowing for a more comprehensive analysis of the intricate dynamics at play. To gain a deeper understanding of the extended Ross model, we employ RNN, treating it as an initial value problem involving a system of first-order ordinary differential equations, each representing one of the seven profiles. This method enables us to obtain valuable insights and elucidate the complexities inherent in the propagation of malaria. Mosquitoes and humans constitute the two cohorts encompassed within the exposition of the mathematical dynamical model. Human dynamics are comprised of individuals who are susceptible, exposed, infectious, and in recovery. The mosquito population, on the other hand, is divided into three categories: susceptible, exposed, and infected. For RNN, we used the input of 0 to 300 days with an interval length of 3 days. The evaluation of the precision and accuracy of the methodology is conducted by superimposing the estimated solution onto the numerical solution. In addition, the outcomes obtained from the RNN are examined, including regression analysis, assessment of error autocorrelation, examination of time series response plots, mean square error, error histogram, and absolute error. A reduced mean square error signifies that the model's estimates are more accurate. The result is consistent with acquiring an approximate absolute error close to zero, revealing the efficacy of the suggested strategy. This research presents a novel approach to solving the malaria propagation model using recurrent neural networks. Additionally, it examines the behavior of various profiles under varying initial conditions of the malaria propagation model, which consists of a system of ordinary differential equations.
本文提出了一个用于预测疟疾传播的创新计算框架。该结构采用了复杂的计算方法，以提高预测疟疾爆发的可靠性。该研究旨在通过使用基于人工智能的递归神经网络 (RNN) 为疟疾传播预测提供一个强大有效的工具。该模型分为人类和蚊子两组。为了开发该模型，对传统的罗斯-麦克唐纳模型进行了扩展，以便更全面地分析复杂的动力学。为了更深入地了解扩展的罗斯模型，我们使用 RNN 将其视为一个涉及一阶常微分方程组的初值问题，每个方程组代表七个谱中的一个。这种方法使我们能够获得有价值的见解，并阐明疟疾传播所固有的复杂性。蚊子和人类构成了数学动力模型论述中所包含的两个群体。人类动力学由易感染、暴露、感染和康复的个体组成。另一方面，蚊子种群分为三类：易感染、暴露和感染。对于 RNN，我们使用了 0 到 300 天的输入，间隔长度为 3 天。通过将估计解叠加到数值解上来评估方法的精度和准确性。此外，还检查了 RNN 的结果，包括回归分析、误差自相关评估、时间序列响应图检查、均方误差、误差直方图和绝对误差。较小的均方误差表示模型的估计更准确。结果与获得接近零的近似绝对误差一致，表明所提出的策略是有效的。这项研究提出了一种使用递归神经网络解决疟疾传播模型的新方法。此外，它还检查了疟疾传播模型在不同初始条件下的各种谱的行为，该模型由一个常微分方程组组成。
