This repository is about using Graph neural networks (GNN) for predicting dynamics of COVID-19 pandemic in the United States.
A package for calculating Effective Reproductive Numbers (Rt) is represented.
Files are including as follows: 1- Selecting graph for GNN: either connectivity_network_graph or neighborhood_graph files
2- Time series of effective reproductive numbers(Rt): R_fifty_states
3- Calculating Rt based on COVID-19 confirmed cases in all states: EpiEstim
4- The results of using connectivity network: matrix of connectivity among states
5- Running the code based on Pytorch:Run.py
Access to published paper: https://www.mdpi.com/1660-4601/18/7/3834
Abstract: The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the United States. While the GTNN model’s edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with Rt numbers collected over the previous four days and asked them to predict the following day for all states in the United States. The performance of these models was evaluated with the datasets that included Rt values reflecting conditions from 22 January through 26 November 2020 (before the start of COVID-19 vaccination in the United States). To determine the efficiency, we compared the results of two models with each other and with those generated by a baseline Long short-term memory (LSTM) model. The results indicated that the GTNN model outperformed both the NGNN and LSTM models for predicting Rt.