By Dongmei Chen, Bernard Moulin, Jianhong Wu
Features smooth examine and technique at the unfold of infectious illnesses and showcases a large diversity of multi-disciplinary and state of the art suggestions on geo-simulation, geo-visualization, distant sensing, metapopulation modeling, cloud computing, and development research Given the continued danger of infectious ailments world wide, it is important to improve applicable research tools, versions, and instruments to evaluate and expect the unfold of illness and assessment the danger. studying and Modeling Spatial and Temporal Dynamics of Infectious illnesses good points mathematical and spatial modeling techniques that combine purposes from quite a few fields akin to geo-computation and simulation, spatial analytics, arithmetic, records, epidemiology, and health and wellbeing coverage. additionally, the ebook captures the newest advances within the use of geographic info method (GIS), international positioning process (GPS), and different location-based applied sciences within the spatial and temporal examine of infectious ailments. Highlighting the present practices and technique through quite a few infectious affliction reviews, studying and Modeling Spatial and Temporal Dynamics of Infectious illnesses positive aspects: * ways to raised use infectious affliction info amassed from a variety of assets for research and modeling reasons * Examples of ailment spreading dynamics, together with West Nile virus, fowl flu, Lyme ailment, pandemic influenza (H1N1), and schistosomiasis * smooth suggestions equivalent to telephone use in spatio-temporal utilization info, cloud computing-enabled cluster detection, and communicable ailment geo-simulation in accordance with human mobility * an summary of other mathematical, statistical, spatial modeling, and geo-simulation options interpreting and Modeling Spatial and Temporal Dynamics of Infectious ailments is a wonderful source for researchers and scientists who use, deal with, or examine infectious affliction information, have to research numerous conventional and complex analytical tools and modeling options, and observe assorted concerns and demanding situations concerning infectious ailment modeling and simulation. The e-book is usually an invaluable textbook and/or complement for upper-undergraduate and graduate-level classes in bioinformatics, biostatistics, public overall healthiness and coverage, and epidemiology.
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Additional resources for Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases
Emerging Infectious Disease, 11(8):1167–1173. James L. (editor). (2001). A Dictionary of Epidemiology. New York: Oxford University Press. p. 185. MacKellar L. (2007). Pandemic influenza: a review. Population and Development Review, 33(3):429–451. Magnarelli L. , Anderson J. , and Fish D. (1987). Transovarial transmission of Borrelia burgdorferi in Ixodes dammini (Acari: Ixodidae). Journal of Infectious Diseases, 156(1):234–236. Moghadas S. , and Yan P. (2008). Managing public health crises: the role of models in pandemic preparedness, Influenza and Other Respiratory Viruses, 3:75–79.
The GSFS has been used to model the epidemic spread of influenza in Denton City, Texas, with much success. It has also been used to simulate what-if scenarios under different policies for infectious disease outbreaks (Armin et al. 2007). GSFS supports analysis of disease spread in heterogeneous environments and integrates geography, demography, environment, and migration patterns within its framework. While further research is necessary to assess the reliability and validity of this computational model with other applications, it shows great promise for surveillance, monitoring, prevention, and control of infectious diseases, and more effective and efficient utilization of public health resources.
The spatiotemporal characteristics of the outbreaks were mapped to determine outbreak clusters. These clusters were joined chronologically by polylines that are compared with potential bird migration paths. Detecting potential spatial and temporal disease aberration and clustering are important for disease control, especially for early warning of disease outbreaks (Chen et al. 2011). Spatial scan statistics are commonly used for detecting clusters of disease and other public health threats. Chapter 9 coauthored by Belanger and Moore presents two challenges identified in spatial scan statistics: determining appropriate circular scanning window size and dealing with high requirements on computational resources in order to detect both circular and arbitrarily shaped spatial clusters.
Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases by Dongmei Chen, Bernard Moulin, Jianhong Wu