To Study and Analyze Existing Decision-Making Techniques for Monitoring of Chikungunya Disease
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Abstract
Chikungunya virus (CHIKV) is a rapidly spreading vector-borne disease that poses significant global health risks due to its recurrent outbreaks, long-lasting arthralgia, and diagnostic overlap with other arboviruses such as dengue and Zika. Effective monitoring and decision-making systems are crucial to improve outbreak prediction, optimize resource allocation, and enhance public health responses. This study explores a wide range of decision-making techniques and time-series models, mechanistic and Bayesian frameworks, machine learning (ML), deep learning (DL), multi-criteria decision-making (MCDM), geospatial and spatiotemporal models, and IoT/edge-enabled decision support. We compare their interpretability, data requirements, real-time suitability, and adaptability to low-resource settings. In addition, we emphasize challenges such as data scarcity, non-stationary dynamics due to climate variability, and the need for explainable and equitable AI-driven systems. By synthesizing state-of-the-art methods and emerging digital health technologies, this paper provides actionable insights for researchers, healthcare practitioners, and policymakers in building robust, scalable, and context-aware monitoring frameworks for Chikungunya disease.
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References
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