**6. Current state of microfluidic systems in SARS-CoV-2 diagnostics**

Advancements in diagnostic technology over the past 2 years have allowed for infectious diseases, such as SARS-CoV-2, to be more readily and accurately diagnosed [2, 9, 11]. Improvements made to costs, accuracy, turnaround times, and processing speeds in non-microfluidic diagnostic tests have greatly improved the ability of healthcare workers and governments to better prevent and manage mass outbreaks [12]. For example, rapid antigen tests have demonstrated their effectiveness in reducing the number of potential secondary infections by providing people with a qualitative means of diagnosing for COVID-19 [14]. However, this does not mean that these tests and their detection technology cannot still be improved upon. Microfluidic technology offers a means to continuously advance the world's diagnostic capabilities and preparation for the next potential pandemic-level virus.

The past decade has demonstrated the effectiveness of microfluidics in the on-site diagnosis of various infectious pathogens, such as Zika, HIV-1 and more recently SARS-CoV-2 [2]. The errant lack of tests, medical devices, and human resources seen throughout the pandemic, prompted a large increase in the demand for technology capable of being remotely operated and readily analyzed [18, 20]. Advancements in information technology and computational processing created a revolution of new approaches by which microfluidic devices can accurately diagnose COVID-19


#### **Figure 8.**

*Various microfluidic diagnostic devices for SARS-CoV-2 [25].*


#### **Table 2.**

*Comparison of rapid test to microfluidic diagnostic tests.*

*Perspective Chapter: Microfluidic Technologies for On-Site Detection and Quantification… DOI: http://dx.doi.org/10.5772/intechopen.105950*

patients, on-site [21, 24]. **Figure 8** highlights several of the most current, digitally integrated, microfluidic platforms capable of diagnosing SARS-CoV-2.

In many of the diagnostic platforms highlighted in **Figure 8**, smartphones are used as an imaging processor to read fluorescent signals through machine learning and artificial intelligence [25]. With the ever-increasing global access to the internet, smartphone-enabled microfluidic diagnostic devices can produce results that can be uploaded to a data-cloud to be immediately stored. Moreover, these smartphoneenabled devices can reduce the turnaround time for qualitatively diagnosing SARS-CoV-2 to, on average, less than 15 minutes.

Despite the processing speed advantages which smartphone-enabled systems offer, there is still unmet need for faster and more robust microfluidic devices capable of quantitative analysis [26]. As seen in **Table 2**, several types of existing quantitative microfluidic diagnostic tools are slowly becoming more comparable even the most current rapid diagnostic tests for SARS-CoV-2. It is only a matter of time before quantitative microfluidic-based tests will be able to either perform comparably, or better, than the rapid antigen tests.

## **7. Conclusions**

This chapter deals with the experience of COVID-19 over the last 2 years and highlights the significance of microfluidics to the history and advancement of SARS-CoV-2 diagnostic technology. In these years, conventional rapid PCR and ELISA COVID-19 technology has advanced greatly. However, limitations in their modularity, sensitivity, turnaround time and cost greatly reduce their future viability as on-site diagnostic tools. The current state of microfluidic, information and smartphone technology allow microfluidic-based diagnostics to address many limitations associated with conventional on-site/rapid tests. The advantages of microfluidic integration into medical diagnostics are discussed throughout this chapter. The expectation is that microfluidics will advance our future diagnostic abilities to help better prepare for, and manage, the next possible pandemic-level threat.

## **Acknowledgements**

The authors would like to acknowledge Alexander Diab-Liu and Kamaya Bosland for their help in proofreading this work.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Notes/thanks/other declarations**

Thank you to Dr. Xu and IntechOpen for the opportunity to work on this chapter.
