**1. Introduction**

The new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which is the coronavirus disease virus (COVID-19), has quickly emerged around the world since the end of December 2019 and caused a lot of deaths [1]. As a result, various measures have been taken by WHO and researchers in different fields to limit the outspread of the virus and its damage. The rapid detection of the virus to control the pandemic situation was, therefore, a real challenge. Microfluidic biosensors played an important role in the fight against the disease, nevertheless, the low rate of diffusion of target analytes toward the sensitive surface generates a long response time and therefore limits their uses [2, 3]. The binding reaction between the analytes and the ligands immobilized on the sensitive surface leads to the creation of analyte-ligand complexes on this surface, the concentration of which has a determining part in the detection process [4, 5]. Quantitative real-time polymerase chain reaction (qRT-PCR) is currently the most used method for the detection of viruses in respiratory infections. This method can detect very small amounts of viruses [3] but requires well-sophisticated laboratories, a long detection time, and above all, it can be prone to errors [6]. Other methods of diagnosis such as point-of-care (POC) technologies remain, despite some disadvantages, hopeful ways for sensitive, rapid, and inexpensive diagnosis [7]. As, at the microscopic scale,

mass transport of analytes is very difficult because the Reynolds number is low (Re <1) and the fluid flow is always laminar [8], the binding kinetics of the analyte-ligand reaction, as well as the response time of such detection devices, are generally limited. Currently, several mechanical and physical effects have been used to enhance mass transport in microfluidic networks, such as magnetic effect [9], AC electrokinetic effect (ACEK) [10, 11], and optical forces [12]. Other studies have shown that several design parameters can be adjusted to improve the performance of biosensors [13–17]. In this context, we achieved a 2D finite element numerical simulation on the kinetics of SARS-COV-2 to optimize the performance of a microfluidic biosensor with integrated flow confinement. To determine the degree of influence of some input factors on the biosensor detection time, a main flow of water mixed with analytes connected to a perpendicular flow of pure water has been studied numerically. The make-up flow contributes to the confinement of the target analytes in a thin layer above the biosensor and thus increases the rate of the binding reaction.
