Dynamics and Control of Diseases in Networks with Community Structure

By Marcel Salathé, James H. Jones. PLoS Comput. Biol., 2010

598 citations recorded by the source corpus.

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

Subfields: Epidemiology, Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9f4951ff12dc97f4d8b7b64780676e1f33deb7a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

In Modeling and Simulation

In Statistical and Nonlinear Physics

Citations in the corpus

The problem of pattern and scale in ecology: what have we learned in 20 years? (2013, background): “In more generalised networks, interactions can be measured directly via the real-time monitoring of individual movement (Salath e & Jones 2010; Katz et al. 2011), or the measurement of chemical cues, such as signalling across plants (Unsicker et al. 2009) or quorum sensing in bacteria (Diggle et…”
A high-resolution human contact network for infectious disease transmission (2010, background): “Sociotechnological networks can provide large long-term datasets (11) but fail to capture the CPIs relevant for disease transmission.”
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian (2018, background): “This close relationship between Markov chains and graphs has led to a broad adoption of diffusion-based algorithms in network science [87], with applications to ranking connected objects [59], analyzing disease spreading [111], and respondentdriven sampling [112].”
Modelling the influence of human behaviour on the spread of infectious diseases: a review (2010, unknown): “Web-based social networks provide an underlying structure of friendships and acquaintances and can be used as basis for surveys, opinion polls and epidemic simulations (Salathé and Jones, 2010).”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “In fact, the currently growing available human generated data and computing power have driven the fast emergence of various social, technological and biological networks [145] [146] [147] [148] .”
Networks and the Epidemiology of Infectious Disease (2010, background): “Although the impact of communities in transmission processes has not been fully explored, a few studies have shown it can have a profound impact on disease dynamics [100, 101].”
Vital nodes identification in complex networks (2016, background): “The nodes with high betweenness values are usually the bridges connecting two communities, and the nodes acting as bridges between communities are usually playing vital roles [46,47].”
Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks (2013, background): “For σ > 0 , non-Markovian properties enforce time-respecting paths across communities and thus mitigate the decelerating eﬀect of community structures on diﬀusion dynamics ( S ∗ ( T (2) ) 1 ) [23].”
Statistical physics of vaccination (2016, background): “Since nodes with large betweenness centrality are often bridges between different communities in the network [305, 315], the vaccination of these nodes has a good potential to break up a large network into”
Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts (2010, background): “Diverse network structures can affect disease spread [24–26]— one example being clustering (a high density of triangles).”