Machine Learning for Infectious Disease Risk Prediction: A Survey

TL;DR

This survey reviews how machine learning techniques are applied to predict infectious disease risks, highlighting models, challenges, and future research directions in the field.

Contribution

It systematically categorizes existing machine learning models for infectious disease risk prediction and discusses current challenges and future research avenues.

Findings

Machine learning models are categorized into statistical, data-driven, and epidemiology-inspired.

Challenges include handling model inputs, designing objectives, and evaluating performance.

The survey identifies open questions and future directions in the field.

Abstract

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.