A computational simulation platform for designing real-time monitoring systems with application to COVID-19

With the aim of contributing to the fight against the coronavirus disease 2019 (COVID-19), numerous strategies have been proposed. While developing an effective vaccine can take months up to years, detection of infected patients seems like one of the best ideas for controlling the situation. The role of biosensors in containing highly pathogenic viruses, saving lives and economy is evident. A new competitive numerical platform specifically for designing microfluidic-integrated biosensors is developed and presented in this work. Properties of the biosensor, sample, buffer fluid and even the microfluidic channel can be modified in this model. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care (POC) applications. First, the validation of the presented numerical platform against experimental data and then results and discussion, highlighting the important role of the design parameters on the performance of the biosensor is presented. For the latter, the baseline case has been set on the previous studies on the biosensors suitable for SARS-CoV, which has the highest similarity to the 2019 nCoV. Subsequently, the effects of concentration of the targeted molecules in the sample, installation position and properties of the biosensor on its performance were investigated in 11 case studies. The presented numerical framework provides an insight into understanding of the virus reaction in the design process of the biosensor and enhances our preparation for any future outbreaks. Furthermore, the integration of biosensors with different devices for accelerating the process of defeating the pandemic is proposed.

Automated summary

A new competitive numerical platform for designing microfluidic-integrated biosensors has been developed, allowing for modifications of various properties to design specific biosensors for point-of-care applications. Validation against experimental data and analysis of design parameters have highlighted the importance of optimization in enhancing biosensor performance.