Viral traits from deep mutational scanning and socio-demographic context predict SARS-CoV-2 lineage fitness across...

To assess the joint effects of SARS-CoV-2 viral traits (ACE2 binding, immune escape, and cell entry) and socio-demographic context on viral lineage fitness across countries and to develop predictive models of lineage fitness using these factors.

A total of 5,092,686 SARS-CoV-2 spike protein sequences from nine representative countries, spanning the least developed, developing, and advanced economies, were collected. Viral traits for individual sequences were calculated using deep mutational scanning data. Lineage-specific growth rates (i.e., viral fitness) were estimated with multinomial logistic regression. To examine the link between the socio-demographic index (SDI) and viral fitness changes, we first evaluated the effects of viral traits on lineage growth rates using a Gamma regression model. Then we assessed the linear relationship between SDI and these effects. Machine learning models combining viral traits and SDI were trained to predict viral fitness, with performance assessed by regression and classification metrics.

Immune escape exhibited the strongest association with viral fitness changes in the least developed countries (rate ratio [RR] = 1.90, 95% CI: 1.72-2.09), with this effect declining as economic development increased (developing countries: RR = 1.50, 95% CI: 1.42-1.58 and advance countries: RR = 1.20, 95% CI: 1.14-1.26). After model comparison, XGBoost showed the best predictive performance using viral traits, when SDI was included (RMSE = 16.269, R² = 0.786, Accuracy = 0.923).

Viral traits and socio-economic context jointly shaped SARS-CoV-2 variant fitness. Immune escape was more influential for lineage growth in low-resource settings, emphasizing the need for equitable vaccine access and targeted interventions against the spread of immune-escape variants.