A Location-Centric Network Approach to Analyzing Epidemic Dynamics

Abstract

Recent health threats, such as the SARS, H1N1, and Ebola pandemics, have stimulated great interest in network models to study the transmission of communicable diseases through human interaction and mobility. Most current network models have focused on an individual-centric perspective where individuals are represented as nodes, and the interactions among them as edges. Few of these models are concerned with the discovery of the spatial patterns and dynamics of epidemics. We propose a location-centric, transmission network approach, in which nodes denote locations and edges denote possible disease transmissions between locations. We then identify the dynamics of transmission flows, the dynamics of critical locations, and the spatial-temporal extent of transmission pathways to assess the impact of these spatial dynamics on the evolution of an epidemic. Results show that transmission flows shift from elementary schools to middle schools and finally universities and professional schools at different phases of an epidemic. Critical locations, identified using network analysis, are responsible for the upsurge in transmission flows during the peaks of the epidemic. The length of transmission pathways shows a power law distribution and their spatial extent is rather small. Insights gained from this study will help devise spatially sensitive strategies to control communicable diseases.

A large-scale location-based social network to understanding the impact of human geo-social interaction patterns on vaccination strategies in an urbanized area

“…Traditional network models are individual-centric approaches in which nodes represent individuals and links represent physical contacts ( Meyers 2007 ). Disease simulations based on network models primarily focus on health outcomes (e.g., the number of infected cases) ( Mao & Bian 2010 ; Salathé & Jones 2010 ), but do not sufficiently consider the role of location in disease transmission and control ( Zhong & Bian 2016 ); for example, what are the critical locations in the transmission? Are location-based control strategies more effective than individual-based control strategies?…”

“…Previous research has shown the effectiveness of the above prevention measures in reducing disease transmission with agent-based modeling ( Ferguson et al 2005 ; Ferguson et al 2006 ; Halloran et al 2008 ), but those agent-based modelings are individual-centric without capturing explicitly spatial dynamics of the disease transmission for designing control strategy purpose. Spatial locations, one attribute of each agent, are treated as spatial stamps of the transmission process in the individual-centric agent-based modeling ( Zhong & Bian 2016 ). Such representations suffer from a number of drawbacks regarding transmission and control design.…”

Evidence and future potential of mobile phone data for disease disaster management

“…These sources of ''Big Mobile Data" (Ahas et al, 2014, p. 5) could be particularly effective in global health emergencies, such as communicable disease outbreaks, because of the importance of tracking population movements (Zhong and Bian, 2016) and rapidly responding to needs in this context, e.g. to minimize contact between infected and uninfected populations.…”

Predicting Influenza Dynamics using a Deep Learning Approach

“…Among the prevailing approaches to predicting the dynamics of influenza epidemics, few have focused on the transmission at a location-specific scale. Location networks, where locations are nodes and disease flows between them are links, provide a promising basis for such dynamic analyses (Zhong and Bian 2016), but also present challenges as each location might have peaks and troughs of different magnitudes throughout the epidemic (Bian et al 2012). Conventional approaches are not adequate to capture such complex, dynamic patterns.…”