Numerical investigation of a fractional order Wolbachia invasive model using stochastic Bayesian neural network

Faiz, Zeshan; Javeed, Shumaila; Ahmed, Iftikhar; Baleanu, Dumitru

doi:10.1016/j.aej.2024.03.030

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2024

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Cited by 3 publications

References 38 publications

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Neural Network for FCEVs and RM Power Management using V2G technology

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2024 International Conference on Circuit, Systems and Communication (ICCSC)

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Neural Network for FCEVs and RM Power Management using V2G technology

Elabbassi,

Khala,

Yanboiy

et al. 2024

2024 International Conference on Circuit, Systems and Communication (ICCSC)

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A Semi-Runge-Kutta Method for Incommensurate Systems of Fractional Differential Equations

Shiri,

Shi,

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Wolbachia-Based Approaches to Controlling Mosquito-Borne Viral Threats: Innovations, AI Integration, and Future Directions in the Context of Climate Change

Branda,

Cella,

Scarpa

et al. 2024

Viruses

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Wolbachia-based mosquito control strategies have gained significant attention as a sustainable approach to reduce the transmission of vector-borne diseases such as dengue, Zika, and chikungunya. These endosymbiotic bacteria can limit the ability of mosquitoes to transmit pathogens, offering a promising alternative to traditional chemical-based interventions. With the growing impact of climate change on mosquito population dynamics and disease transmission, Wolbachia interventions represent an adaptable and resilient strategy for mitigating the public health burden of vector-borne diseases. Changes in temperature, humidity, and rainfall patterns can alter mosquito breeding habitats and extend the geographical range of disease vectors, increasing the urgency for effective control measures. This review highlights innovations in Wolbachia-based mosquito control and explores future directions in the context of climate change. It emphasizes the integration of Wolbachia with other biological approaches and the need for multidisciplinary efforts to address climate-amplified disease risks. As ecosystems shift, Wolbachia interventions could be crucial in reducing mosquito-borne diseases, especially in vulnerable regions. AI integration in Wolbachia research presents opportunities to enhance mosquito control strategies by modeling ecological data, predicting mosquito dynamics, and optimizing intervention outcomes. Key areas include refining release strategies, real-time monitoring, and scaling interventions. Future opportunities lie in advancing AI-driven approaches for integrating Wolbachia with other vector control measures, promoting adaptive, data-driven responses to climate-amplified disease transmission.

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Numerical investigation of a fractional order Wolbachia invasive model using stochastic Bayesian neural network

Cited by 3 publications

References 38 publications

Neural Network for FCEVs and RM Power Management using V2G technology

Neural Network for FCEVs and RM Power Management using V2G technology

A Semi-Runge-Kutta Method for Incommensurate Systems of Fractional Differential Equations

Wolbachia-Based Approaches to Controlling Mosquito-Borne Viral Threats: Innovations, AI Integration, and Future Directions in the Context of Climate Change

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