Department of Public Health, College of Public Health and Health Informatics, University of Ha'il, Ha'il, Saudi Arabia.
Department of Computer and Networks Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
J Infect Dev Ctries. 2022 Jan 31;16(1):90-100. doi: 10.3855/jidc.15004.
COVID-19 has become a global concern because it has extensive damage to health, social and economic systems worldwide. Consequently, there is an urgent need to develop tools to understand, analyze, monitor and control further outbreaks of the disease.
The Susceptible Infected Recovered-Particle SwarmOptimization model and the feed-forward artificial neural network model were separately developed to model COVID-19 dynamics based on daily time-series data reported by the Saudi authorities from March 2, 2020 to February 21, 2021. The collected data were divided into training and validation datasets. The effectiveness of the investigated models was evaluated by using various performance metrics. The Susceptible-Infected-Recovered-Particle-Swarm-Optimization model was found to well predict the cumulative infected and recovered cases and to optimally tune the contact rate and the characteristic duration of the illness. The feed-forward artificial neural network model was found to be efficient in modeling daily new and cumulative infections, recoveries and deaths.
The forecasts provided by the investigated models had high coefficient of determination values of more than 0.97 and low mean absolute percentage errors (around 7% on average).
Both the Susceptible-Infected-Recovered-Particle-Swarm-Optimization and feed-forward artificial neural network models were efficient in modeling COVID-19 dynamics in Saudi Arabia. The results produced by the models can help the Saudi health authorities to analyze the virus dynamics and prepare efficient measures to control any future occurrence of the epidemic.
COVID-19 已成为全球关注的焦点，因为它对全球的健康、社会和经济系统造成了广泛的破坏。因此，迫切需要开发工具来理解、分析、监测和控制疾病的进一步爆发。
分别基于沙特当局 2020 年 3 月 2 日至 2021 年 2 月 21 日报告的每日时间序列数据，使用易感性-感染-恢复-粒子群优化模型和前馈人工神经网络模型分别对 COVID-19 动态进行建模。收集的数据分为训练数据集和验证数据集。使用各种性能指标评估所研究模型的有效性。易感性-感染-恢复-粒子群优化模型被发现可以很好地预测累积感染和恢复病例，并优化接触率和疾病特征持续时间。前馈人工神经网络模型被发现可以有效地对每日新感染、累积感染、恢复和死亡进行建模。
所研究模型提供的预测具有很高的决定系数值（超过 0.97）和较低的平均绝对百分比误差（平均约为 7%）。
易感性-感染-恢复-粒子群优化和前馈人工神经网络模型都可以有效地对沙特阿拉伯的 COVID-19 动态进行建模。模型产生的结果可以帮助沙特卫生当局分析病毒动态，并制定有效的措施来控制未来任何疫情的发生。
