Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance

Abstract

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

“…These challenges have resorted data intensive modeling [6] and computational intelligence methods [7] that embrace the complexity of the issues that arise from the pandemic, from lack of human resources [8] and data resources [9,10], to the lack of emergency preparedness and capabilities to respond effectively [11]. An increasing amount of studies set out to explore models and artifacts that leverage artificial intelligence (AI) methods and methodologies to explore pandemic facts and circumstances from several differing yet often complementary angles, from the composites and overarching description of the virus itself [12], to diseases detection and diagnosis [13,14] to prediction on infection rates [15], patient management [16], the protection of healthcare workers [17,18], as well as hygiene measures, prevention and containment [19], drug development [20], and treatment [21][22][23]. The use of AI techniques is perceived to be a paradigm shift [24] towards approaches that use data science in empowering ways to craft, test and deploy public health care policies [25,26].…”

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

“…Data science techniques have been widely applied in past epidemics to help health professionals and authorities take better measures against the disease [3]. Today, data science applications tackle COVID-19 in three main phases: screening, tracking and forecasting, and medical aid [4]. In particular, the different use cases can be arranged in eight lines of application: assessing risk and prioritising patients; testing and diagnostics; simulating and modelling; contact tracing; comprehending social interventions; logistic planning and economic action; automated patient services; and supporting the development of vaccines and new therapies [1] As part of the data science's applications to support a pandemic response, prediction systems to improve the healthcare supply chain appear necessary.…”

On Interval Type-2 Fuzzy Logic System Using the Upper and Lower Method for Supervised Classification Problems

“…is the membership function (MF) associated with the k-th input of the l-th rule, and θ l is the weight of the height deffuzification associated with the l-th rule, (l = 1, • • • , M ). Equation (2)(3)(4)(5)(6)(7)(8) can also be expressed as…”

“…The Θ in Equations (3-6) and (3-7) is the same as that used in T1-FLSMO (see Equation (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)). Additionally, it is important to define the upper vector of FBFs Φ x (q) and lower vector of FBFs Φ x (q) , as…”

“…Mohan et al [7] performed a heart disease prediction using a hybrid ML model. Moreover, the authors in [8] presented a survey showing the ML algorithm and models that can be used to aid the battling of the covid-19 virus. Regarding financial applications, the authors in [9] applied ML to predict the stock market and regional relative directions based on financial network indicators.…”