Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control

Abstract

COVID-19 is a life-threatening contagious virus that has spread across the globe rapidly. To reduce the outbreak impact of COVID-19 virus illness, continual identification and remote surveillance of patients are essential. Medical service delivery based on the Internet of Things (IoT) technology backed up by the fog-cloud paradigm is an efficient and time-sensitive solution for remote patient surveillance. Conspicuously, a comprehensive framework based on Radio Frequency Identification Device (RFID) and body-wearable sensor technologies supported by the fog-cloud platform is proposed for the identification and management of COVID-19 patients. The J48 decision tree is used to assess the infection degree of the user based on corresponding symptoms. RFID is used to detect Temporal Proximity Interactions (TPI) among users. Using TPI quantification, Temporal Network Analysis is used to analyze and track the current stage of the COVID-19 spread. The statistical performance and accuracy of the framework are assessed by utilizing synthetically-generated data for 250,000 users. Based on the comparative analysis, the proposed framework acquired an enhanced measure of classification accuracy, and sensitivity of 96.68% and 94.65% respectively. Moreover, significant improvement has been registered for proposed fog-cloud-based data analysis in terms of Temporal Delay efficacy, Precision, and F-measure.

Keywords: COVID-19; Intelligent framework; Self organized mapping; Temporal analysis.

Fig. 1

COVID-19 confirmed cases over geographical distribution by WHO (as on 10 December 2021).

Fig. 2

COVID-19 risk index analysis.

Fig. 3

Layered architecture of IoT-fog-cloud computing.

Fig. 4

Layered architecture of proposed model.

Fig. 5

Life cycle of COVID-19 infection disease.

Fig. 6

TNA visualization (a) region 1; (b) region 2; (c) region 3.

Fig. 7

Closed proximity.

Fig. 8

Step-wise depiction of the presented approach; different phases have been formulated for data acquisition where data is acquired in real-time. The data acquired is pre-processed and classified using J48 classifier. Finally, based on the specific class of the patient, temporal graph is formulated for real-time graphical assessment.

Fig. 9

Temporal delay.

Fig. 10

Illustration of J48 classifier tree with estimation of different categories of patients; the box value indicate the number of infected patients in different category with respect to total sample size; SS Starting State, ST Intermediary State.

Fig. 11

Comparative temporal analysis.