Electrochemiluminescence as a Transformative Diagnostic Tool for SARS-CoV-2 Variant Detection: Implications for Future Pandemic Preparedness
Year: 2026
Type: article
Abstract: Over the past two decades, the three major betacoronavirus outbreaks, SARS-CoV (2002), MERS-CoV (2012), and SARS-CoV-2 (2019), have resulted in millions of infections and deaths globally. SARS-CoV-2 has displayed rapid mutations, enhancing its transmissibility, immune evasion, and resistance to existing vaccines and diagnostics. For these reasons, it has been named the most notorious viral outbreak in modern history. Due to the ever-mutating Spike (S) protein, key interactions with bioreceptors have been disrupted, making conventional detection methods more challenging. To minimize viral spread and improve diagnostic accuracy, innovative biosensing technologies are critical. As such, electrochemiluminescence (ECL) offers a powerful biosensing platform for SARS-CoV-2 variant detection by combining the high sensitivity of optical detection with electrochemistry. Its advantages include culture-independent detection, low background noise, a broad dynamic range, and the ability to detect low viral concentrations. Recent advances in multiple disciplines, such as novel luminophore development, nanotechnology, lab-on-a-chip miniaturization, and AI-driven machine learning, have further enhanced its diagnostic powers. Furthermore, smartphone-integrated ECL platforms enable rapid, point-of-care testing, improving disease surveillance and outbreak response. By uniting AI with ECL biosensing, the diagnostic system can refine variant classification and predict emerging mutations, making ECL a promising candidate for the next generation of pandemic diagnostics. Despite the many advantages, there is limited research that has explored ECL for SARS-CoV-2 variant detection, which is addressed in this review. This review highlights ECL's potential to become a gold-standard technique for detecting rapidly evolving pathogens, offering a robust and accessible solution for future infectious disease monitoring.
Source: ACS Sensors
Institutions: University of Cape Town, University of the Western Cape
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W7225794596