Chikungunya Disease Detection and Monitoring: A Comprehensive Survey

Chikungunya Disease Detection and Monitoring: A Comprehensive Survey

Prabhjot Kaur1 Jagdeep Kaur2

1Research Scholar, Department of Computer Science and Engineering

Sant Baba Bhag Singh University, Jalandhar.

2 Professor, Department of Computer Science and Engineering

Sant Baba Bhag Singh University, Jalandhar.

Abstract

Chikungunya virus (CGN) is a mosquito-borne alphavirus responsible for causing significant outbreaks characterized by fever, severe joint pain, rash, and long-lasting arthralgia, impacting millions worldwide. Despite its clinical similarity to other arboviral infections, early and accurate diagnosis remains critical for effective patient management and outbreak control. This paper presents a comprehensive review of CGN’s epidemiology, transmission dynamics, monitoring challenges. Furthermore, it explores the recent advancements in digital health technologies specifically the integration of Internet of Things (IoT) devices, and Artificial Intelligence (AI) to enhance real-time detection, monitoring, and epidemiological surveillance of CGN. These innovations offer promising avenues for early warning systems, improved vector control strategies, and data-driven public health interventions. By harnessing digital tools, healthcare systems can achieve more efficient outbreak responses, reduce disease burden, and better protect vulnerable populations. This study underscores the imperative for multidisciplinary approaches combining cutting-edge digital technologies to combat emerging arboviral threats like Chikungunya.

Keywords: Chikungunya virus, Digital Twin, mosquito-borne diseases, IoT, Artificial Intelligence.