Purpose: Mortality due to tuberculosis (TB) remained a challenge in Malaysia. Its mortality burden has been persistent and the highest among all notifiable infectious diseases for the last thirty years. We made use of the nationwide TB surveillance data to develop a mortality predictive model for TB patients managed in Malaysia.Methods & Materials: A content analytic exercise was conducted by using a two-year notification data in 2013 and 2014. The dataset was processed and the characteristics of mortality were explored.Results: Out of 48,780 notified TB cases, there was 6.01% mortality cases whereby 32.82% (n = 931) had died directly due to TB. The mean age was 50.84 (SD = 18.67) years old, male (69.1%), Malaysian (88.5%), Malay (46.5%), with low education (95.1%) and without permanent income (65%), non-diabetic (81.5%), non-smoker (62.9%) and without HIV co-infection (88.1%). The majority of cases were new cases (90.9%), pulmonary smear positive (70.7%) and with BCG scars (71.4%) who died during the first two months of intensive treatment phase (87.8%). Further analysis revealed that age, late presentation, pulmonary severity, presence of secondary infection and meningitis were significant predictors of mortality for TB patients in Malaysia [p < 0.05]. The risk of mortality significantly increased with late presentation, worsening of pulmonary severity, presence of meningitis and secondary infection respectively [Adjusted OR 95%CI: 16.078% (11.796%-21.913%); 2.022% (1.682%-2.431%); 1.634% (1.101%-2.426%); 1.46% (1.182%-1.792%) respectively]. Age was also found to be a significant predictor, however, at a small and low risk of 2.4% with increase in age [Adjusted OR 95%CI: 0.986% (0.981%-0.991%)].Conclusion: This is a significant TB mortality model that represented TB patients managed in Malaysia with strong predictive probability [95%CI: ROC AUC 73.6% (71.5%; 75.7%); Hosmer and Lemeshow chi-squared = 7.313 (p > 0.503)]. Further work on risk scoring and characterization of TB mortality is warranted to develop a predictive mortality checklist tool for early and targeted interventions. Further analyses on mortality according to high risk subpopulations such as TB-diabetes, TB-elderly and TB-healthcare workers are also highly recommended for in-depth understanding to formulate and implement subpopulation-targeted interventions.Purpose: On October 09 th 2017 few suspected cases of AIV H9N2 were reported to provincial reference laboratory for poultry diseases, Poultry Research Institute (PRI), Rawalpindi from Badhana Kalan, Islamabad. The cases were pressented with complaint of respiratory distress, loss of production and unusually high mortality.The objectives of this investigation were to know the magnitude, reasons of the outbreak and to control it through targeted interventions.Methods & Materials: The team from PRI visited the affected village and adjoining areas on October 13 th , 2017. Active cases finding was done in the area using a standard case definition. Meeting sessions were conducted with poultry farmer...

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