Titlebook: Dynamic Models of Infectious Diseases; Volume 2: Non Vector V. Sree Hari Rao,Ravi Durvasula Book 2013 Springer Science+Business Media New Y

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CANE

Control of Infectious Diseases: Dynamics and Informatics, two approaches for modeling the dynamics of epidemics, namely (1) dynamical mathematical modeling and (2) incidence data modeling. Recent mathematical models are helpful in understanding the spread (transmission) of the infectious diseases. Incidence data reveals progressively the number of suscept

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Percolation Methods for SEIR Epidemics on Graphs,proximating an epidemic model; in the second part we discuss the role of percolation in modelling the random networks on which the spread of the infectious diseases takes place. At the end, we propose a class of models which are mathematically treatable and at the same time incorporate most of the d

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Dynamics of Tuberculosis in a Developing Country: Nigeria as a Case Study,le for more than three million deaths worldwide annually. In order to control tuberculosis, reduce the number of cases and prevent the emergence of drug-resistant strains, the WHO proposed to implement the direct observation therapy strategy (DOTS) in heavy-burden countries. However, the effectivene

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Two-Component Signalling Systems of ,: Regulators of Pathogenicity and More,“white plague,” “consumption,” and “Yakshma.” The disease is characterized by slow “wasting” of internal systems in the body, primarily respiratory and immune systems hence the name “consumption,” which literally means to consume or use from inside. Clinically, the disease manifestations include per

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,,: Evolution, Host–Pathogen Interactions, and Implications for Tuberculosis Control,is resulting in 8.8 million new cases and 1.4 million deaths associated to tuberculosis in 2011 (WHO-fact sheet-2011). The emergence of multidrug-resistant (MDR) and other forms of extensive-drug-resistant (XDR) tuberculosis in many parts of the world is threatening to send us back to an era when tu

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