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Photonics enabled intelligence system to identify SARS-CoV 2 mutations
Bakr Ahmed Taha1, Qussay Al-Jubouri2, Yousif Al Mashhadany3
1UKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Malaysia.
Abstract:
The COVID-19, MERS-CoV, and SARS-CoV are hazardous epidemics that have resulted in many deaths which caused a worldwide debate. Despite control efforts, SARS-CoV-2 continues to spread, and the fast spread of this highly infectious illness has posed a grave threat to global health. The effect of the SARS-CoV-2 mutation, on the other hand, has been characterized by worrying variations that modify viral characteristics in response to the changing resistance profile of the human population. The repeated transmission of virus mutation indicates that epidemics are likely to occur. Therefore, an early identification system of ongoing mutations of SARS-CoV-2 will provide essential insights for planning and avoiding future outbreaks. This article discussed the following highlights: First, comparing the omicron mutation with other variants; second, analysis and evaluation of the spread rate of the SARS-CoV 2 variations in the countries; third, identification of mutation areas in spike protein; and fourth, it discussed the photonics approaches enabled with artificial intelligence. Therefore, our goal is to identify the SARS-CoV 2 virus directly without the need for sample preparation or molecular amplification procedures. Furthermore, by connecting through the optical network, the COVID-19 test becomes a component of the Internet of healthcare things to improve precision, service efficiency, and flexibility and provide greater availability for the evaluation of the general population. KEY POINTS: • A proposed framework of photonics based on AI for identifying and sorting SARS-CoV 2 mutations. • Comparative scatter rates Omicron variant and other SARS-CoV 2 variations per country. • Evaluating mutation areas in spike protein and AI enabled by photonic technologies for SARS-CoV 2 virus detection.
Insights
This study proposes an AI-powered photonics framework for rapid SARS-CoV-2 mutation detection. It analyzes Omicron variant spread and spike protein mutations, enabling early identification for future outbreak prevention.
Area of Science:
- Virology
- Biophotonics
- Artificial Intelligence
Background:
- The ongoing spread of SARS-CoV-2, causing COVID-19, poses a significant global health threat.
- Viral mutations, like those observed in the Omicron variant, alter characteristics and necessitate continuous monitoring.
- Previous epidemics like MERS-CoV and SARS-CoV highlight the danger of coronaviruses.
Purpose of the Study:
- To develop an early identification system for SARS-CoV-2 mutations using photonics and AI.
- To compare the spread rates of the Omicron variant with other SARS-CoV-2 variants globally.
- To identify critical mutation areas within the SARS-CoV-2 spike protein.
Main Methods:
- A novel framework combining photonics and artificial intelligence (AI) for SARS-CoV-2 mutation identification.
- Analysis of global data to compare the spread rates of different SARS-CoV-2 variants, including Omicron.
- Evaluation of mutation sites within the viral spike protein.
Main Results:
- A proposed AI-enabled photonics framework for identifying and sorting SARS-CoV-2 mutations.
- Comparative analysis of Omicron variant scatter rates against other SARS-CoV-2 variants across countries.
- Identification of key mutation areas in the spike protein.
Conclusions:
- Photonics approaches, enhanced by AI, offer a direct method for SARS-CoV-2 detection without sample preparation.
- This technology can be integrated into the Internet of Healthcare Things for improved public health surveillance.
- Early detection of SARS-CoV-2 mutations is crucial for planning and preventing future epidemics.
