International Importation Risk Estimation of SARS-CoV-2 Omicron Variant with Incomplete Mobility Data.

Area of Science:

• Virology
• Epidemiology
• Computational Biology

Background:

• A novel Omicron subvariant, BQ.1, emerged in Nigeria in July 2022.
• BQ.1 has become a dominant strain, causing significant reinfections globally, even in highly vaccinated populations.
• High population mobility between Western Africa and other countries presents a substantial risk for BQ.1 introduction.

Purpose of the Study:

• To develop a predictive model for estimating the probability of BQ.1 introduction into non-African countries from Western Africa.
• To utilize deep neural networks and incomplete population mobility data for risk assessment.
• To inform public health policies regarding international virus spread.

Main Methods:

• Development of a deep neural network model.
• Analysis of incomplete population mobility data from Western Africa to non-African countries.
• Estimation of BQ.1 importation probabilities.

Main Results:

• Identified France and Spain as having the highest importation risk during the study period.
• Determined a high importation risk for 13 other non-African countries, including Canada and the United States.
• Demonstrated the utility of deep learning in predicting infectious disease spread.

Conclusions:

• Deep learning models can effectively estimate the international spread risk of emerging viral variants.
• The findings highlight specific countries at high risk for BQ.1 importation, enabling targeted public health interventions.
• This approach has potential applications for monitoring and managing the spread of other infectious diseases.