Large-Scale Spatial-Transmission Models of Infectious Disease

By S. Riley. Science, 2007

608 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Epidemiology, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/3f43637091fbc2655e772e2ee80007cfd35c2a34

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Citations in the corpus

Multiscale mobility networks and the spatial spreading of infectious diseases (2009, background): “C approaches to the realistic modeling of spatial epidemic spread make use of a wide array of simulation schemes (1) ranging from very detailed agent-based approaches (2–6) to structured metapopulation models based on data-driven mobility schemes at the interpopulation level (7–10).”
Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models (2008, background): “In practice, DE modelers add compartments to capture heterogeneity in individuals and their contact networks, for example, disaggregating by biological or behavioral attributes (e.g., differences in age or contact frequencies), or by location (as in patch models; see, e.g., Riley 2007).”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “, behavioral, demographic, or social space), and they can almost always be modeled in similar fashion to geographic space (see, for example, [40,41]).”
Natural human mobility patterns and spatial spread of infectious diseases (2011, background): “[7] S Riley, “Large-scale spatial-transmission models of infectious disease,” Science, 316, 1298 (2007).”
Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. (2007, background): “Recently, the metapopulation approach is being revamped in computational approaches for the large scale forecast of infectious disease spreading (Grais et al., 2004; Hufnagel et al., 2004; Colizza et al., 2006a; Cooper et al., 2006; Colizza et al., 2007a; Hollingsworth et al., 2006; Riley, 2007).”
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees (2011, background): “Knowledge of contact patterns is crucial for building and informing computational models of infectious disease transmission [14-23].”
EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks (2008, methodology): “Several recent reviews on currently used models can be found in [23], [24].”
Understanding the Spreading Patterns of Mobile Phone Viruses (2009, background): “First, a Bluetooth virus can infect all Bluetooth-activated phones within a distance from 10 to 30m, resulting in a spatially localized spreading pattern similar to the one observed in the case of influenza (3, 6, 7), SARS (8, 9) and other contact-based diseases (10) (Fig.”
Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti (2011, background): “Human mobility is extremely important for the spread of communicable diseases [28,29], and early containment of epidemic outbreaks is often a key factor in preventing spread [30].”
Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model (2010, background): “The increasing computational and data integration capabilities witnessed in recent years have enabled the development of computational epidemic models of great complexity and realism [36].”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, background): “Background Computational approaches for the detailed modeling of epidemic spread in spatially-structured environments make use of a wide array of simulation schemes [1,2].”