Objectives: The study aimed to determine the associated factors of household food security (HFS) and household dietary diversity (HDD) during the COVID−19 pandemic in Bangladesh. Design: Both online survey and face-to-face interviews were employed in this cross-sectional study. The Household Food Security Scale and Household Dietary Diversity Score were used to access HFS and HDD, respectively. The HDD scores were derived from a 24-h recall of food intake from 12 groups. Setting: Bangladesh. Participants: A total sample of 1876 households were recruited. Results: The overall mean scores of HFS and HDD were 31·86 (sd 2·52) and 6·22 (sd 5·49), respectively. Being a rural resident, having no formal education, occupation of household head other than government job and low monthly income were potential determinants of lower HFS and HDD. Approximately 45 % and 61 % of Bangladeshi households did not get the same quantity and same type of food, respectively, as they got before the pandemic. Over 10 % of respondents reported that they lost their job or had to close their businesses, and income reduction was reported by over 70 % of household income earners during the COVID-19 pandemic, which in turn was negatively associated with HFS and HDD. Conclusion: Household socio-economic variables and COVID-19 effects on occupation and income are potential predictors of lower HFS and HDD scores. HFS and HDD deserve more attention during this pandemic particularly with reference to low-earning households and the households whose earning persons’ occupation has been negatively impacted during the COVID-19 pandemic.

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The recent COVID-19 pandemic has imposed threats on both physical and mental health since its outbreak. This study aims to explore the impact of the COVID-19 pandemic on mental health among a representative sample of home-quarantined Bangladeshi adults. A cross-sectional design was used with an online survey completed by a convenience sample recruited via social media. 1,427 respondents were recruited & assessed by DASS-21 measure. Chi-square tests and multivariable logistic regressions were performed to examine the association of variables. The prevalence of stress, anxiety, and depression was 59.7%, 33.7%, and 57.9%, respectively. Chi-square tests suggest that age, gender, marital status, education, income, residence, and presence of an elderly family member are associated with mental health outcomes. Perceptions that the pandemic disrupted life events, affected mental health, jobs, the economy and education, predictions of a worsening situation, and uncertainty of the health care system capacities were significantly associated with poor mental health outcomes. Multivariable logistic regressions showed that sociodemographic factors and perceptions of COVID-19 significantly predict mental health outcomes. These findings warrant consideration of easily accessible low-intensity mental health interventions during and beyond this pandemic.

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Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

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ObjectivesThis study aimed to estimate the prevalence of childhood diarrhoeal diseases (CDDs) and acute respiratory infections (ARIs) and also to determine the factors associated with these conditions at the population level in Bangladesh.SettingThe study entailed an analysis of nationally representative cross-sectional secondary data from the most recent Bangladesh Demographic and Health Survey conducted in 2017–2018.ParticipantsA total of 7222 children aged below 5 years for CDDs and 7215 children aged below 5 years for ARIs during the survey from mothers aged between 15 and 49 years were the participants of this study. In the bivariate and multivariable analyses, we used Pearson χ2 test and binary logistic regression, respectively, for both outcomes.ResultsThe overall prevalence of CDD and ARI among children aged below 5 years was found to be 4.91% and 3.03%, respectively. Younger children were more likely to develop both CDDs and ARIs compared with their older counterparts. Children belonging to households classified as poorest and with unimproved floor materials had a higher prevalence of diarrhoea than those from households identified as richest and with improved floor material, respectively. Stunted children had 40.8% higher odds of diarrhoea than normal children. Being male and having mothers aged below 20 years were 48.9% and two times more likely to develop ARI than female counterparts and children of mothers aged 20–34 years, respectively. Children whose mothers had no formal education or had primary and secondary education had higher odds of ARI compared with children of mothers having higher education.ConclusionThis study found that children aged below 24 months were at higher risk of having CDDs and ARIs. Thus, programmes targeting these groups should be designed and emphasis should be given to those from poorest wealth quintile to reduce CDDs and ARIs.

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