Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed
  Graph Neural Networks
Majd Al Aawar Srikar Mutnuri Mansooreh Montazerin Ajitesh Srivastava
During the COVID-19 pandemic, a major driver of new surges has been the
emergence of new variants. When a new variant emerges in one or more countries,
other nations monitor its spread in preparation for its potential arrival. The
impact of the variant and the timing of epidemic peaks in a country highly
depend on when the variant arrives. The current methods for predicting the
spread of new variants rely on statistical modeling, however, these methods
work only when the new variant has already arrived in the region of interest
and has a significant prevalence. The question arises: Can we predict when (and
if) a variant that exists elsewhere will arrive in a given country and reach a
certain prevalence? We propose a variant-dynamics-informed Graph Neural Network
(GNN) approach. First, We derive the dynamics of variant prevalence across
pairs of regions (countries) that applies to a large class of epidemic models.
The dynamics suggest that ratios of variant proportions lead to simpler
patterns. Therefore, we use ratios of variant proportions along with some
parameters estimated from the dynamics as features in a GNN. We develop a
benchmarking tool to evaluate variant emergence prediction over 87 countries
and 36 variants. We leverage this tool to compare our GNN-based approach
against our dynamics-only model and a number of machine learning models.
Results show that the proposed dynamics-informed GNN method retrospectively
outperforms all the baselines, including the currently pervasive framework of
Physics-Informed Neural Networks (PINNs) that incorporates the dynamics in the
loss function.