The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review

Abstract

Background: The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. Methods: A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. Results: The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. Conclusions: In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Public health partnerships with faith-based organizations to support vaccination uptake among minoritized communities: A scoping review

“…First, awareness campaigns were carried out via digital pathways. To generate educational materials, PHAs solicited the help of healthcare practitioners of faith (e.g., the American Muslim Health Professionals) [114] and dialogued with faithbased groups [115] to design and guide intervention efforts. Religious scholars endorsed and promoted vaccine safety and efficacy information from PHAs to mitigate hesitancy in their communities [45,[116][117][118], which was shown to improve communication of vaccine-related information and inspire willingness to receive the vaccine [45].…”

“…"Pulpit swaps" allowed vaccine informed pastors to preach in other churches [19,91]. Town hall events [115,[126][127][128], information sessions [129], Q&As [130,131], and group meetings were promotional strategies used to encourage vaccination [54,70,81,91], which was particularly useful for promoting trust among hesitant communities.…”

“…Faith leaders released statements jointly with PHAs to promote COVID-19 vaccines' morally acceptability and encourage vaccinations [80,139,[141][142][143][144][145]. Faith leaders vaccinated publicly in front of congregations [19,107,139,146,147] and answered concerns with an understandable perspective that incorporated faith [103,115].…”

Standard and anomalous waves of COVID-19: A multiple-wave growth model for epidemics

“…Understanding the evolution of possible multiple waves of infections is also, of course, relevant for public health officials, as it may help them to develop better strategies to fight the propagation of the virus. In response to the widespread occurrence of second-wave effects in the COVID-19 epidemic in many countries around the world, there is now a growing body of literature on the subject [6][7][8][9][10][11][12][13][14][15][16][17][18] . In such studies, compartmental models 10,11,14 and agent based models 19,20 are typically the models of choice, although models based on artificial intelligence algorithms have also been used [15][16][17][18] .…”

How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts

“…Artificial intelligence has also been identified as one of the key technologies used during the pandemic. For example, it has been used in the detection of COVID-19 cases, diagnosis, monitoring vital parameters, contact tracing, prediction of virus spread, treatment suggestions as well as vaccine development [22][23][24]. However, the applied use and research on interoperability standards, like FHIR [25], OpenEHR [26], and SNOMED-CT [27], and telehealth technologies (including the Internet of Things), to succor the COVID-19 pandemic, have received less attention [28].…”