Analyzing and Modeling Spatial and Temporal Dynamics of by Dongmei Chen, Bernard Moulin, Jianhong Wu

By Dongmei Chen, Bernard Moulin, Jianhong Wu

Features smooth learn and method at the unfold of infectious ailments and showcases a large variety of multi-disciplinary and cutting-edge concepts on geo-simulation, geo-visualization, distant sensing, metapopulation modeling, cloud computing, and development analysis

Given the continued chance of infectious illnesses all over the world, it can be crucial to advance applicable research equipment, types, and instruments to evaluate and expect the unfold of illness and review the chance. Analyzing and Modeling Spatial and Temporal Dynamics of Infectious ailments features mathematical and spatial modeling methods that combine functions from numerous fields reminiscent of geo-computation and simulation, spatial analytics, arithmetic, information, epidemiology, and health and wellbeing coverage. additionally, the publication captures the newest advances within the use of geographic info procedure (GIS), worldwide positioning procedure (GPS), and different location-based applied sciences within the spatial and temporal learn of infectious diseases.

Highlighting the present practices and technique through a variety of infectious sickness stories, Analyzing and Modeling Spatial and Temporal Dynamics of Infectious ailments features:

  • Approaches to raised use infectious disorder info amassed from numerous assets for research and modeling purposes
  • Examples of affliction spreading dynamics, together with West Nile virus, poultry flu, Lyme disorder, pandemic influenza (H1N1), and schistosomiasis
  • Modern innovations corresponding to phone use in spatio-temporal utilization facts, cloud computing-enabled cluster detection, and communicable illness geo-simulation in line with human mobility
  • An review of other mathematical, statistical, spatial modeling, and geo-simulation techniques

Analyzing and Modeling Spatial and Temporal Dynamics of Infectious ailments is an outstanding source for researchers and scientists who use, deal with, or learn infectious illness facts, have to examine a number of conventional and complex analytical equipment and modeling thoughts, and notice assorted matters and demanding situations concerning infectious illness modeling and simulation. The publication is additionally an invaluable textbook and/or complement for upper-undergraduate and graduate-level classes in bioinformatics, biostatistics, public wellbeing and fitness and coverage, and epidemiology.

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Additional info for Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases (Wiley Series in Probability and Statistics)

Example text

The elements of V are the vertices (or nodes), and the elements of E are the edges (or lines) of the graph G. The vertex set of a graph is referred to as V(G), and its edge set as E(G). The properties of nodes and links, and the topology of the graph, can be assigned multiple parameters in order to describe epidemics over space and time. The number of connections of an individual represents the number of links of a node and is useful for describing the topology of a network (Watts and Strogatz, 1998; Albert et al.

The three parameters refer to (1) number of connections of an individual, (2) degree of interconnection between family members and coworkers, and (3) ratio between workplace connections and family connections. A two-level (community level and urban level) and two-population (home-grouped population and work-grouped population) framework is thus constructed to simulate the epidemic dynamics. In the end, the vulnerability of the communities to disease is evaluated based on the deterministic estimation of parameters from multiple data sources.

It can be easily used to simulate and visualize the spreading and impact of infectious diseases on the population. Furthermore, the population can be divided into subgroups, which enables the simulation of different impacts of the disease on each individual. CA for simulating infectious diseases has been used to discover the behavior of the disease or to work out contingency plans for different diseases (Sloot et al. 2002; Beauchemin et al. 2005; Doran and Laffan 2005; Xiao et al. 2006; Pfeifer et al.

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