By V. Sree Hari Rao, Ravi Durvasula
Despite nice advances in public well-being around the globe, insect vector-borne infectious illnesses stay a number one explanation for morbidity and mortality. illnesses which are transmitted by means of arthropods equivalent to mosquitoes, sand flies, fleas, and ticks impact hundreds and hundreds of hundreds of thousands of individuals and account for almost 3 million deaths worldwide. some time past there has been little or no wish of controlling the epidemics attributable to those ailments, yet sleek developments in technological know-how and expertise are supplying numerous ways that those illnesses will be dealt with. essentially, the method of transmission of an infectious sickness is a nonlinear (not inevitably linear) dynamic method which are understood simply via accurately quantifying the very important parameters that govern those dynamics.
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Extra resources for Dynamic Models of Infectious Diseases: Volume 1: Vector-Borne Diseases
5 million new cases of cutaneous leishmaniasis each year (WHO). Several months or years after an initial episode of cutaneous leishmaniasis, some patients suffer from more severe ulcers that do not spontaneously heal (Chappuis et al. 2007) and can partially or completely destroy the mucous membranes of the nose, mouth, throat cavities, and surrounding tissues (WHO). This more severe clinical manifestation is called muco-cutaneous leishmaniasis (Chappuis et al. 2007, WHO). The most dangerous manifestation of leishmaniasis is visceral leishmaniasis, which is fatal if untreated (Chappuis et al.
The results are discussed in Sect. 5 of this chapter. 9 Standard Classification Methods Standard machine learning classiﬁers such as RBFNetworks (RBF) (Haykins 1994), Bayes Network (BNT) (Friedman et al. 5 (Quinlan 1993) have been utilized in Sree Hari Rao and Naresh Kumar (2011c) to benchmark the performances of RNIADT and its efﬁcacy in extracting knowledge from dengue data set. 10 Performance Metrics for Comparing Machine Classifiers To evaluate the models generated by the decision trees, we employed a k-fold cross validation algorithm (k = 10) as it is considered a powerful methodology to overcome data over-ﬁtting (Kothari and Dong 2000).
2006; Ramos et al. 2009; Tanner et al. 2008) would yield more accurate results. 36 V. Sree Hari Rao and M. 17 Comparison of our results with (Tanner et al. 2008) Method True positive False positive False negatives 259 Tanner et al. 18 Comparison of performance measures of our methodology with Tanner et al. (2008) Sree Hari Rao and Measure Tanner et al. 99 Conclusions and Discussion In this chapter, we have presented several methodologies that help in the effective diagnosis of the dengue illness.