The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Abstract

Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning . The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches

“…It is also worthwhile to note that ML and DL hybrids/ensembles have attracted great attention from the ID communities in the past few years, evident by their increased use in publications. Hybrid and ensemble models are information fusion concepts that combine statistical, mechanistic, ML, and/or DL approaches working together (hybrid) or independently (ensemble) to minimize prediction noise and increase accuracy over the individual models, which could be one of the possible explanations for their increased popularity in recent years 8,35 .…”

“…In recent years, factors, such as an exponential increase in computing power, easy access to large and diverse datasets, and advancements in artificial intelligence, have facilitated extraordinary growth in the field of infectious disease predictions 7 . Machine Learning (ML) and Deep Learning (DL) methods are widely used for a variety of disease intelligence tasks, including temporal, spatial, and risk factor predictions 8 . ML models have been shown to outperform traditional statistical techniques to give more accurate and reliable predictions 9,10 .…”

Observing flow of He II with unsupervised machine learning

“…23 More broadly, the technique to use machine learning to identify clusters and to infer their movement may be applicable to studies beyond fluid flow, e.g., migration of herds of animals, 24 traffic flow 25 and spread of transmissible diseases. 26 We demonstrate the ability to track the flow of the normal component of He II across a 1 cm by 1 cm field-of-view. Motion is evident in the images of the fluorescence and by tracking individual excimer clouds identified using an unsupervised machine learning clustering algorithm.…”

Machine learning in handling disease outbreaks: a comprehensive review

“…Research on the spread of infectious diseases has increased since the COVID-19 pandemic struck globally. Alfred and Obit [12] discussed the role of machine learning in handling the disease outbreak attempting to reduce its spreading. The study focused on detecting and predicting disease attacks by applying various classification and prediction models using structured and unstructured data.…”