\begin{algorithmic}[1]
\For{$variant$ in variants}
    \State dates $\gets$ unique dates in which $variant$ exists
    
    \State dates.sort()

    \State model\_initial\_weights $\gets$ random

    \For{$d$ in dates}
        \State retro\_data $\gets$ all data before $d$
        \State processed\_data $\gets$ preprocess $retro\_data$
        \State dataset $\gets$ structured $processed\_data$ into graphs
        \State train\_dataset, val\_dataset $\gets$ temporally split $dataset$ into 80\% training and the most recent 20\% for validation

        \State model $\gets$ initialize desired model with $model\_initial\_weights$
        \State epochs $\gets$ 100
        \State optimizer $\gets$ Adam(lr=0.05)
        \State early\_stopper $\gets$ EarlyStopper(patience=3)
        \State $model$.train($epochs$, $optimizer$, $early\_stopper$, $train\_dataset$, $val\_dataset$)
        \State best\_model\_weights $\gets early\_stopper$
        \State $model\_initial\_weights$ $\gets$ $best\_model\_weights$
    \EndFor
\EndFor
\end{algorithmic}

\begin{algorithmic}
[1]
\For{$variant$ in variants}
\State dates $\gets$ unique dates in which $variant$ exists
\State dates.sort()
\State model\_initial\_weights $\gets$ random
\For{$d$ in dates}
\State retro\_data $\gets$ all data before $d$
\State processed\_data $\gets$ preprocess $retro\_data$
\State dataset $\gets$ structured $processed\_data$ into graphs
\State train\_dataset, val\_dataset $\gets$ temporally split $dataset$ into 80\% training and the most recent 20\% for validation
\State model $\gets$ initialize desired model with $model\_initial\_weights$
\State epochs $\gets$ 100
\State optimizer $\gets$ Adam(lr=0.05)
\State early\_stopper $\gets$ EarlyStopper(patience=3)
\State $model$.train($epochs$, $optimizer$, $early\_stopper$, $train\_dataset$, $val\_dataset$)
\State best\_model\_weights $\gets early\_stopper$
\State $model\_initial\_weights$ $\gets$ $best\_model\_weights$
\EndFor
\EndFor
\end{algorithmic}
{
    "title": "global prediction of covid-19 variant emergence using dynamics-informed   graph neural networks",
    "id": "2401.03390",
    "abstract": "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.",
    "categories": "q-bio.pe cs.lg physics.soc-ph",
    "doi": "",
    "created": "2024-01-07",
    "updated": "",
    "authors": [
        "majd al aawar",
        "srikar mutnuri",
        "mansooreh montazerin",
        "ajitesh srivastava"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2401.03390"
}
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