A survey on modelling of infectious disease spread and control on social contact networks
arXiv:2102.02768
Abstract
Infectious diseases are a significant threat to human society which was over sighted before the incidence of COVID-19, although according to the report of the World Health Organisation (WHO) about 4.2 million people die annually due to infectious disease. Due to recent COVID-19 pandemic, more than 2 million people died during 2020 and 96.2 million people got affected by this devastating disease. Recent research shows that applying individual interactions and movements data could help managing the pandemic though modelling the spread of infectious diseases on social contact networks. Infectious disease spreading can be explained with the theories and methods of diffusion processes where a dynamic phenomena evolves on networked systems. In the modelling of diffusion process, it is assumed that contagious items spread out in the networked system through the inter-node interactions. This resembles spreading of infectious virus, e.g. spread of COVID-19, within a population through individual social interactions. The evolution behaviours of the diffusion process are strongly influenced by the characteristics of the underlying system and the mechanism of the diffusion process itself. Thus, spreading of infectious disease can be explained how people interact with each other and by the characteristics of the disease itself. This paper presenters the relevant theories and methodologies of diffusion process that can be used to model the spread of infectious diseases.
arXiv commentUnder submission. part of arXiv:1906.02856
References in corpus (36)
- Statistical mechanics of complex networks
- Community structure in social and biological networks
- Understanding individual human mobility patterns
- Epidemic processes in complex networks
- The spread of epidemic disease on networks
- Self-similar community structure in organisations
- Renormalization group analysis of the small-world network model
- Immunization of complex networks
- Search in Power-Law Networks
- Efficient Immunization Strategies for Computer Networks and Populations
- Activity driven modeling of time varying networks
- Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
- Small-world properties of the Indian Railway network
- Information diffusion epidemics in social networks
- Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees
- Time varying networks and the weakness of strong ties
- Triadic closure as a basic generating mechanism of communities in complex networks
- Maximal planar networks with large clustering coefficient and power-law degree distribution
- Dynamical and bursty interactions in social networks
- Efficient local strategies for vaccination and network attack
- Why are most COVID-19 infection curves linear?
- Modeling human dynamics of face-to-face interaction networks
- Improving immunization strategies
- An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
- Non-perturbative heterogeneous mean-field approach to epidemic spreading in complex networks
- Exploiting temporal network structures of human interaction to effectively immunize populations
- Immunization strategies for epidemic processes in time-varying contact networks
- Scale-free networks with an exponent less than two
- Burstiness and spreading on temporal networks
- Epidemiologically optimal static networks from temporal network data
- Epidemic Spreading on Activity-Driven Networks with Attractiveness
- Random walk centrality for temporal networks
- From calls to communities: a model for time varying social networks
- Latent Self-Exciting Point Process Model for Spatial-Temporal Networks
- Universal Components of Real-world Diffusion Dynamics based on Point Processes
- Vaccination strategies on dynamic networks with indirect transmission links and limited contact information