Since 2015, the End TB Strategy and the Regional Framework for Action on Implementation of the End TB Strategy in the Western Pacific 2016–2020 have guided national tuberculosis (TB) responses in countries and areas of the Region. This paper provides an overview of the TB epidemiological situation in the Western Pacific Region and of progress towards the 2020 milestones of the Strategy. A descriptive analysis was conducted of TB surveillance and programme data reported to WHO and estimates of the TB burden generated by WHO for the period 2000–2018. An estimated 1.8 million people developed TB and 90 000 people died from it in the Region in 2018. Since 2015, the estimated TB incidence rate and the estimated number of TB deaths in the Region decreased by 3% and 10%, with annual reduction rates of 1.0% and 3.4%, respectively. With current efforts, the Region is unlikely to achieve the 2020 milestones and other targets of the Strategy. Major challenges include: (1) wide variation in the geographical distribution and rate of TB incidence among countries; (2) a substantial proportion (23%) of TB cases that remain unreached, undiagnosed or unreported; (3) insufficient coverage of drug susceptibility testing (51%) for bacteriologically confirmed cases and limited use of WHO-recommended rapid diagnostics (11 countries reported <60% coverage); (4) suboptimal treatment outcomes of TB (60% of countries reported <85% success), of TB/HIV co-infection (79%) and of multidrug- or rifampicin-resistant TB (59%); (5) limited coverage of TB preventive treatment among people living with HIV (39%) and child contacts (12%); and (6) substantial proportions (35–70%) of TB-affected families facing catastrophic costs. For the Region to stay on track to achieve the End TB Strategy targets, an accelerated multisectoral response to TB is required in every country.
Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios
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