Predicting sub-population specific viral evolution
This work addresses the challenge of location-specific viral evolution prediction for therapeutic design and disease surveillance, representing a novel method for a known bottleneck.
The paper tackled the problem of forecasting viral variant distributions across different sub-populations, such as countries, by proposing a model that explicitly models transmission rates and interdependence, resulting in outperforming baselines in predicting viral protein distributions across continents and countries in multi-year evaluations on SARS-CoV-2 and influenza A/H3N2.
Forecasting the change in the distribution of viral variants is crucial for therapeutic design and disease surveillance. This task poses significant modeling challenges due to the sharp differences in virus distributions across sub-populations (e.g., countries) and their dynamic interactions. Existing machine learning approaches that model the variant distribution as a whole are incapable of making location-specific predictions and ignore transmissions that shape the viral landscape. In this paper, we propose a sub-population specific protein evolution model, which predicts the time-resolved distributions of viral proteins in different locations. The algorithm explicitly models the transmission rates between sub-populations and learns their interdependence from data. The change in protein distributions across all sub-populations is defined through a linear ordinary differential equation (ODE) parametrized by transmission rates. Solving this ODE yields the likelihood of a given protein occurring in particular sub-populations. Multi-year evaluation on both SARS-CoV-2 and influenza A/H3N2 demonstrates that our model outperforms baselines in accurately predicting distributions of viral proteins across continents and countries. We also find that the transmission rates learned from data are consistent with the transmission pathways discovered by retrospective phylogenetic analysis.