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

## TL;DR

This paper estimates the international importation risk of the SARS-CoV-2 Omicron BQ.1 variant using deep learning and incomplete mobility data.

## Contribution

A deep learning model is introduced to estimate importation risk with incomplete mobility data for public health planning.

## Key findings

- France and Spain had the highest importation risk during the study period.
- 13 other non-African countries, including Canada and the US, also showed high importation risk.
- The model demonstrates how deep learning can support public health policy development.

## Abstract

A novel Omicron subvariant named BQ.1 emerged in Nigeria in July 2022 and has since become a dominant strain, causing a significant number of repeated infections even in countries with high-vaccination rates. Due to the high flow of people between Western Africa and other non-African countries, there is a high risk of Omicron BQ.1 being introduced to other countries from Western Africa. In this context, we developed a model based on deep neural networks to estimate the probability that the Omicron BQ.1 introduced to other countries from Western Africa based on the incomplete population mobility data from Western Africa to other non-African countries. Our study found that the highest risk was in France and Spain during the study period, while the importation risk of other 13 non-African countries including Canada and the United States is also high. Our approach sheds light on how deep learning techniques can assist in the development of public health policies, and it has the potential to be extended to other types of viruses.