Complex network modelling and analysis of Hajj Crowd and MERS-CoV outbreak

Abstract

Representing, analysing and modelling natural and artificially-existing systems into complex networks provides a useful insight into understanding the behaviour of these networks in detail and predicting their future. Such networks share the common property that their vertex connectivity follows a scale-free behaviour due to their evolving nature, and new node addition take place under preferential attachment behaviour. Hubs or highly connected nodes are widely believed to have special importance in network management. An attack on these hubs can cause a serious and irreparable damage to the functionality of the whole network. The resilience of these targeted nodes, and therefore the entire network, cannot be achieved without effectively tracking them within the network. To improve services offered to a large network of people who have gathered for a particular purpose, modelling and analysis of the individual and their collective behaviour is very important. Hajj is an annual religious gathering, organized for a duration of about a week, where millions of pilgrims from all over the world get together to perform religious rituals within certain Spatio-temporal zones. The Hajj management faces many challenges, including cultural diversity, literacy level, religious knowledge about rituals, lack of availability of translators and shortage of time available at hand in emergency situations. To study Hajj Crowd as a complex network, we have deployed a crowdsourcing framework that uses smartphones’ sensory data to capture spatial and temporal coordinates, with the ability to define quality aware user contexts. To deal with the Spatio-temporal and activity data from the crowd, we have developed a cloud-based framework that can receive and store context and user interaction data from the smartphone application. We have analysed the data, defined a high-quality user context, and provided context-aware services as an incentive. We have modelled Hajj Crowd as Hajj Geo Social Network (HGSN), a one-mode complex network of pilgrims that uses commonly available communication services to help them perform their Spatio-temporal religious activities. Initially, we have analysed the HGSN with respect to different metrics, such as, node degree, betweenness, closeness, average path length. It was observed that HGSN is an evolving network and the node-degree follows the power law behaviour. Moreover, we have discovered that pilgrims with many acquaintances or those who are members of linguistically diverse subnets can bridge these subnets and can play a key role in information diffusion. The Recent outbreak of MERS-CoV (Middle East Respiratory Syndrome-Corona Virus) has raised a serious threat to pilgrims’ health. This is a global concern that this infectious disease can result in a worldwide outbreak once visitors return to their home countries, resulting in the creation of a disease spread network spanning all affected areas, with its hub centered at the location of the Hajj event. We have utilized the spatio-temporal information available about MERS-CoV reported cases in Saudi Arabia and modelled it as MERS-CoV network between different location where MERS-CoV cases were reported. The same network analysis approach was applied to study the impact of MERS-CoV spreading on Hajj related areas. Analyzing this network can help concerned authorities deduce useful information to combat the MERS-CoV spreading regarding areas of concerns to pilgrim health.