Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Abstract

a b s t r a c t H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.

COVID-19 and information - communication technology: Common components in an interactive framework for predicting, preventing, controlling and monitoring the new COVID-19 disease

“…Experimental evaluation of this hypothesis proved that using fog infrastructure can provide a better response time for delay-sensitive programs with the least impact on system accuracy. Article [4] provides an effective cloud computing architecture that predicts patients infected with H1N1 and provides preventive measures for infection control. This program includes four processing components along with a secure medical database for cloud storage.…”

“…Social Network Analysis (SNA) is used to present the outbreak situation. Other studies that have proposed cloud computing architectures at the time of outbreak of infectious diseases include; Tazkia et al (2015) study that attempted to identify the possible prevalence of dengue virus based on statistical calculations and geographic information system (GIS) [76], Sandhu et al (2016) study [4] that aimed to prevent and predict H1N1 in uenza and MERS-Corona, and a study [77] that proposed a IoTbased cloud framework to control the outbreak of Ebola virus with continuous tele-monitoring of infected patients in real time using radio frequency identi cation (RFID) technology, cloud computing and J48 decision tree. Since there is no speci c treatment for coronavirus, there is an urgent need for global monitoring of those infected with Covid-19 [24].…”

“…According to the survey, the amount of agreement with Tele-multiple disciplinary care service was 93.6%. Computer science has been widely used to analyze viral epidemic and examine its changes [4]. The sudden increase in COVID-19 cases has put a lot of pressure on health care services around the world.…”

“…3: Maintaining the care of patients, who are unable to access health care facilities. 4: teleconsultation for healthy people who are quarantined at home.…”

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment

“…Their methodology used a random decision tree to classify the infection in patient depending on H1N1 attributes. The system additionally introduced the concept of SNA graph and Outbreak Role Index to prevent the outbreak and to aware the users about how much they are probable to get infected respectively [23]. A web-based system was developed to predict dengue and identify the dengue hotspot areas, trends, outbreak behaviour, and case rate.…”