Machine Learning for Infectious Disease Risk Prediction: A Survey
Mutong Liu Yang Liu Jiming Liu
Infectious diseases, either emerging or long-lasting, place numerous people
at risk and bring heavy public health burdens worldwide. In the process against
infectious diseases, predicting the epidemic risk by modeling the disease
transmission plays an essential role in assisting with preventing and
controlling disease transmission in a more effective way. In this paper, we
systematically describe how machine learning can play an essential role in
quantitatively characterizing disease transmission patterns and accurately
predicting infectious disease risks. First, we introduce the background and
motivation of using machine learning for infectious disease risk prediction.
Next, we describe the development and components of various machine learning
models for infectious disease risk prediction. Specifically, existing models
fall into three categories: Statistical prediction, data-driven machine
learning, and epidemiology-inspired machine learning. Subsequently, we discuss
challenges encountered when dealing with model inputs, designing task-oriented
objectives, and conducting performance evaluation. Finally, we conclude with a
discussion of open questions and future directions.