Pandemic Monitoring and Contactless Surveillance Using AI-Driven Drone-Based Tracking Systems

Rekhadevi, B and Peermohamed, k (2026) Pandemic Monitoring and Contactless Surveillance Using AI-Driven Drone-Based Tracking Systems. In: Pandemic Monitoring and Contactless Surveillance Using AI-Driven Drone-Based Tracking Systems. RAC mics.

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Abstract

Abstract
The rapid advancement of AI-driven drone technology has revolutionized the approach to pandemic
surveillance and management, offering unprecedented capabilities for real-time, contactless
monitoring. This chapter explores the integration of unmanned aerial vehicles (UAVs) with advanced
AI algorithms and sensors to enhance public health responses during infectious disease outbreaks. By
leveraging drones equipped with multi-modal sensing technologies, such as thermal imaging,
biometric sensors, and environmental detectors, pandemic surveillance becomes more efficient,
scalable, and non-invasive. The chapter discusses the critical role of drones in monitoring large public
gatherings, enforcing public health guidelines, and providing real-time decision-making support to
health authorities. Ethical, legal, and privacy concerns related to drone surveillance are also examined,
emphasizing the importance of balancing public health benefits with the protection of civil liberties.
Furthermore, challenges related to latency, data accuracy, and algorithmic fairness are addressed to
ensure the responsible deployment of AI-driven drones in pandemic management. This work
highlights the potential of AI-powered drones to transform global public health strategies, ensuring
rapid, efficient, and equitable responses to future pandemics.
Keywords: AI-driven drones, pandemic surveillance, real-time monitoring, multi-modal sensors public health, ethical guidelines.

Item Type: Book Section
Subjects: Computer Science Engineering > Artificial Intelligence
Domains: Computer Science Engineering
Depositing User: user 12 12
Date Deposited: 22 May 2026 05:57
Last Modified: 22 May 2026 05:57
URI: https://ir.vistas.ac.in/id/eprint/20562

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