Scholarlink

<p>The transmission dynamics of Ebola virus disease are investigated using a fractional-order compartmental model combined with temporal prediction techniques. The population is divided into susceptible, vaccinated, exposed, infected, critical, hospitalized, funeral-associated, recovered, healthcare-worker, and environmental compartments. A variable-order fractional formulation is employed to incorporate memory effects in disease progression. The system is solved numerically using the Predictor-Evaluate-Correct-Evaluate (PECE) method for different fractional orders, revealing distinct epidemic behaviors. To enhance prediction accuracy, the numerical time-series outputs are used as input to a Long Short-Term Memory (LSTM) network, which learns residual variations and produces corrected epidemic trajectories. The proposed hybrid framework preserves the epidemiological structure while improving smoothness and predictive reliability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fractional-LSTM hybrid modeling for Ebola transmission prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月28日

查看全文
  • Khanjan M. Trivedi,
  • Minakshi Biswas Hathiwala,
  • Chetansinh R. Vaghela

摘要

The transmission dynamics of Ebola virus disease are investigated using a fractional-order compartmental model combined with temporal prediction techniques. The population is divided into susceptible, vaccinated, exposed, infected, critical, hospitalized, funeral-associated, recovered, healthcare-worker, and environmental compartments. A variable-order fractional formulation is employed to incorporate memory effects in disease progression. The system is solved numerically using the Predictor-Evaluate-Correct-Evaluate (PECE) method for different fractional orders, revealing distinct epidemic behaviors. To enhance prediction accuracy, the numerical time-series outputs are used as input to a Long Short-Term Memory (LSTM) network, which learns residual variations and produces corrected epidemic trajectories. The proposed hybrid framework preserves the epidemiological structure while improving smoothness and predictive reliability.