Machine Learning for Infectious Disease Risk Prediction: A Survey

Abstract

Infectious diseases place a heavy burden on public health worldwide. In this paper, we systematically investigate how machine learning (ML) can play an essential role in quantitatively characterizing disease transmission patterns and accurately predicting infectious disease risks. First, we introduce the background and motivation for using ML for infectious disease risk prediction. Next, we describe the development and application of various ML models for infectious disease risk prediction, categorizing them according to the models’ alignment with vital public health concerns specific to two distinct phases of infectious disease propagation: (1) the pandemic and epidemic phases (the P-E phaseS) and (2) the endemic and elimination phases (the E-E phaseS), with each presenting its own set of critical questions. Subsequently, we discuss challenges encountered when dealing with model inputs, designing task-oriented objectives, and conducting performance evaluations. We conclude with a discussion of open questions and future directions.