Background Vitamin A deficiency (VAD) is a prominent and widespread public health problem in developing countries, including Bangladesh. About 2% of all deaths among under-five children are attributable to VAD. Evidence-based information is required to understand the influential factors to increase vitamin A supplementation (VAS) coverage and reduce VAD. We investigated the potential factors affecting VAS coverage and its significant predictors among Bangladeshi children aged 6 to 59 months using the VAS clustered data extracted from the latest Bangladesh Demographic and Health Survey 2014. Methods Data were analysed using mixed logistic regression (MLR) modelling approach in the generalised linear mixed model framework. The MLR model performs better than logistic regression for analysing the clustered data because of its minimum Akaike information criterion value. The likelihood ratio test showed that the variance component was significant. Therefore, the clustering effect among children was inevitable to use. Results VAS coverage among under-five children was 63.6%, which is not optimal and below the WHO’s recommendation and the country’s target of 90%. Children aged 25 to 36 months (AOR = 2.07, 95% CI: 1.711 to 2.513), who had higher educated mothers (AOR = 1.37, p = 0.033, 95% CI: 1.026–1.820) and fathers (AOR = 1.32, p = 0.027, 95% CI: 1.032–1.683), whose mothers had media exposure (AOR = 1.22, p = 0.006, 95% CI: 1.059–1.408) and NGO membership (AOR = 1.24, p = 0.002, 95% CI: 1.089–1.422) were more likely to consume VAS. Conclusion The relevant authorities should create proactive awareness programs for highly vulnerable local communities, specifically targeted to educate the children’s mothers about the necessity and benefits of childhood nutrition.

Aim To estimate the prevalence of COVID‐19 pandemic and its transmission rates among people in both community and household levels of Bangladesh. Methods We use the cross‐sectional online survey data of 2080 individuals, collected from 442 households during June to September 2020 in Bangladesh. The Longini and Koopman stochastic epidemic modelling approach was adapted for analysing the data. To validate the results, a simulation study was conducted using the Markov Chain Monte Carlo (MCMC) method via the Metropolis‐Hastings algorithm in the context of the Bayesian framework. Results Overall, the prevalence of COVID‐19 pandemic was 15.1% (315 out of 2080) among people in Bangladesh. This proportion was higher in smaller households (size one: 40.0%, two: 35.7% and three: 25.9%) than larger (four: 15.8%, five: 13.3%, six: 14.1%, seven: 12.5% eight: 8.7%, nine: 14.8% and ten or eleven: 5.7%). The transmission rate of COVID‐19 in community people was higher (12.0%, 95% CI: 10.0% to 13.0%) than household members (9.0%, 95% CI: 6.0% to 11.0%). Conclusion The susceptible individuals have a higher risk of community infection than the household and the community transmission is more responsible than the household for COVID‐19 pandemic in Bangladesh.