Informed machine learning for epidemics: from data analysis to time-series forecasting

This research dives deep into the application of Machine Learning (ML) in epidemiology, aiming to craft innovative strategies to address challenges presented by epidemic phenomena. Recognizing the dual nature of epidemics — as complex physical occurrences and as catalysts of pressure on healthcare systems — we focus on two main areas: data analysis for patient condition assessment and time-series forecasting for epidemiological trend prediction. In the realm of data analysis, we introduced a tree-based ML pipeline apt at evaluating the severity of infections using patient records. On the forecasting front, we addressed the challenges posed by the scarcity of data and the intricate nature of epidemics, developing methodologies for predicting epidemiological trends. In this setting, we exploit domain knowledge to guide the learning process, ensuring accurate and reliable predictions even when confronted with incomplete and noisy data.