Evaluation of rapid detection methods for H5N1 virus using biosensors: An AI-based study

High mortality and zoonotic potential predispose the H5N1 avian influenza virus as a critical threat. Knowing that an epidemic could be occurring, quick and precise diagnostic techniques are essential for managing and containing possible epidemics. To detect H5N1 in saliva samples, this study investigates the theoretical design, simulation, and evaluation of three kind of biosensors based on different technologies with potential as rapid identifications tools to diagnose quickly H5N1: Lateral Flow Tests (LFT), Field Effect transistors (FET) based electrochemical sensors and Quartz Crystal Microbalance (QCM) sensors. Through detailed AI-based simulations, we show the capabilities, sensitivities, and specificities of these biosensors, highlighting their potential for applications in general biology as well as their suitability both for routine home practice and for applications by control entities in public settings. We therefore wish to pave the way to a framework for the quick creation of detection tools that can be swiftly implemented for rapid deployment in case of an outbreak of disease.