Newcastle disease (ND) is a real threat for commercial layer chicken farms in Bangladesh. However, only few studies have focused on exploring the epidemiology of this disease. A case-control study was conducted to identify determinants of Newcastle disease in commercial layer chicken farms in Kishoreganj and Gazipur district of Bangladesh between September 2019 and February 2020. Farms with birds diagnosed as ND positive based on clinical history, clinical signs and postmortem findings were considered as case and farms that did not have such ND positive chickens were the control for this study. Farmers of 56 case farms and 56 control farms were interviewed face to face using a structured questionnaire. The association between Farms’ ND status and determinants was assessed by multivariable logistic regression with backward elimination. In the final model, six variables were found to be associated with the risk for ND outbreak: age of the farmers (Odds Ratio [OR] 0.94; 95% Confidence Interval [CI] 0.87–0.99), distance from the nearest poultry farms (OR = 3.23, 95% CI 1.27–8.39), number of houses in the farms (OR = 3.06, 95% CI 1.06–8.83), surrounding environments (OR = 5.27, 95% CI 1.96–14.20), rearing different aged bird together (OR = 4.76, 95% CI 1.25–18.19), and no isolation of sick birds (OR = 2.85, 95% CI 1.07–7.55). Alteration of these determinants should reduce the ND burden in commercial layer chicken farms.

Bovine tuberculosis (bTB), a chronic zoonotic disease in cattle, has a substantial socio-economic and public health impact. This study was conducted to estimate the prevalence and geographical distribution of bTB in the Sylhet district of Bangladesh. A cross-sectional study was conducted at all 12 upazilas of Sylhet district, which included 512 randomly selected cattle from 48 farms. Selected animals were tested with the Caudal Fold Tuberculin (CFT) test to identify bTB-positive cattle. Out of 512 cattle, only one animal was identified as a reactor, providing an estimated prevalence of 0.19% (95% Confidence Interval; 0–0.58%). The only positive reactor was found in Zakiganj upazila. As the prevalence of bTB in cattle in Sylhet appears to be low, it indicates that most of the upazilas of Sylhet district are free of the bTB infection. This prevalence is lower than the reported prevalence in other parts of Bangladesh. Thus, attempts should be made to maintain the current situation of bTB infection in cattle of Sylhet district.

Background: Dengue fever has emerged as a major public health concern in Bangladesh, with increasing incidence and geographic spread of outbreaks in recent years. This study aimed to investigate the lagged and non-linear associations between climatic factors and dengue incidence across all eight administrative divisions of Bangladesh from 2014 to 2025. Materials and Methods: An ecological time-series design was employed using monthly dengue case data (n = 741,338) and meteorological variables. A generalized additive model (GAM) with a negative binomial distribution was applied to account for overdispersion and capture complex relationships. Descriptive analysis was conducted to assess spatial heterogeneity, and choropleth maps were constructed to visualize the spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis was performed to identify significant lagged associations between climatic variables and dengue incidence. Results: Descriptive analysis showed substantial spatial heterogeneity, with the highest incidence observed in Dhaka (6.53 per 100,000) and the lowest in Sylhet (0.21 per 100,000). Choropleth maps illustrated distinct spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis identified significant lagged associations for temperature and rainfall (lag 1–3 months), humidity (lag 1–2 months), and wind speed (lag 2–3 months). The final GAM explained 88.6% of the deviance in dengue incidence (AIC = 7404.15; dispersion = 0.767). The approximate significance of smooth terms revealed that temperature at a lag of 1 month (p < 0.001, edf = 12.28), rainfall at a lag of 3 months (p < 0.001, edf = 2.85), and wind speed at a lag of 2 months (p < 0.001, edf = 2.25) were highly significant non-linear predictors of dengue transmission. Relative humidity was not significantly associated with dengue incidence. Non-linear effects revealed peak dengue risk at temperatures between 25 and 30 °C and moderate rainfall (~10 mm), particularly during monsoon months (June–October). A strong autoregressive effect indicated that prior dengue incidence significantly influenced current transmission. Conclusions: Overall, dengue transmission in Bangladesh is driven by complex, lagged, and non-linear interactions between climatic variables, seasonality, and regional factors. These findings provide critical evidence for climate-based early warning systems, enhance outbreak prediction, and inform evidence-based vector control strategies.