Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends

The COVID-19 pandemic has spread rapidly and significantly impacted most countries in the world. Providing an accurate forecast of COVID-19 at multiple scales would help inform public health decisions, but recent forecasting models are typically used at the state or country level. Furthermore, traditional mathematical models are limited by simplifying assumptions, while machine learning algorithms struggle to generalize to unseen trends. This motivates the need for hybrid machine learning models that integrate domain knowledge for accurate long-term prediction. We propose a three-layer, geographically informed ensemble, an extensive peer-learning framework, for predicting COVID-19 trends at the country, continent, and global levels. As the base layer, we develop a country-level predictor using a hybrid Graph Attention Network that incorporates a modified SIR model, adaptive loss function, and edge weights informed by mobility data. We aggregated 163 country GATs to train the continent and world MLP layers of the ensemble. Our results indicate that incorporating quantitatively accurate equations and real-world data to model inter-community interactions improves the performance of spatio-temporal machine learning algorithms. Additionally, we demonstrate that integrating geographic information (continent composition) improves the performance of the world predictor in our layered architecture. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Analyzing Twitter Data to Evaluate People's Attitudes towards Public Health Policies and Events in the Era of COVID-19.

Policymakers and relevant public health authorities can analyze people's attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people's attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people's attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people's attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.