Data Adaptive Stochastic Ensemble Net: Optimizing Infection Predictions for COVID-19 Cluster Analysis

Machine learning has garnered significant interest and is extensively utilized in the medical field due to its direct impact on human life. Two components are necessary to develop an AI-based infection prediction assistance system: a training dataset and machine learning prediction model. For AI-based infection prediction model, we first gathered a real-world COVID-19 cluster dataset, consisting of 8,844 confirmed cases across 519 clusters, which includes individual properties and contact relationships between confirmed cases. Second, we introduce the Data Adaptive Stochastic Ensemble Network (DASEN) to enhance prediction robustness. DASEN dynamically adjusts the weight of each component by optimizing the Dirichlet distribution concentration parameter based on the data distribution. We demonstrate the validity of DASEN, showing that different models focus on distinct features and perform well on data with varying characteristics, thus preventing overfitting to majority labels. Notably, DASEN provides superior robustness across all settings with minimal overhead for parameter optimization.