Search

SummaryBackgroundUnderstanding the amount of tuberculosis managed by the private sector in India is crucial to understanding the true burden of the disease in the country, and thus globally. In the absence of quality surveillance data on privately treated patients, commercial drug sales data offer an empirical foundation for disease burden estimation.MethodsWe used a large, nationally representative commercial dataset on sales of 189 anti-tuberculosis products available in India to calculate the amount of anti-tuberculosis treatment in the private sector in 2013–14. We corrected estimates using validation studies that audited prescriptions against tuberculosis diagnosis, and estimated uncertainty using Monte Carlo simulation. To address implications for numbers of patients with tuberculosis, we explored varying assumptions for average duration of tuberculosis treatment and accuracy of private diagnosis.FindingsThere were 17·793 million patient-months (95% credible interval 16·709 million to 19·841 million) of anti-tuberculosis treatment in the private sector in 2014, twice as many as the public sector. If 40–60% of private-sector tuberculosis diagnoses are correct, and if private-sector tuberculosis treatment lasts on average 2–6 months, this implies that 1·19–5·34 million tuberculosis cases were treated in the private sector in 2014 alone. The midpoint of these ranges yields an estimate of 2·2 million cases, two to three times higher than currently assumed.InterpretationIndia's private sector is treating an enormous number of patients for tuberculosis, appreciably higher than has been previously recognised. Accordingly, there is a re-doubled need to address this burden and to strengthen surveillance. Tuberculosis burden estimates in India and worldwide require revision.FundingBill & Melinda Gates Foundation.

paper presents a fast and efficient video stabilization method based on the Speeded-up robust features (SURF). We adopted speeded-up robust features as feature descriptor, which are extracted and tracked in each frame .This extracted features are matched through SURF, matched features were used to estimate the geometric transformation between the frames. Finally estimated transformation is applied to the frames to produce a new stabilized frame pair. After the geometric transformation is carried out, the resultant frames are almost stable. But the boundary region of stabilized frames requires a lot more attention as they are said to be black and some sort of filtering and inpainted work needs to be performed for better results and reconstruction of the obtained stabilized frames. Hence in the obtained stabilized frames, we need to estimate the exact location of the regions at the boundary where inpainting work is to be carried out. Experimental results illustrate superior performance of the SURF based video stabilization in terms of accuracy and speed as compared to other state of art algorithms based stabilization method. KeywordsStabilization, Inpainting, SURF.