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Background: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results: The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions: The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally. [ABSTRACT FROM AUTHOR]

Background: Neonatal death accounts for one fifth of all under-five mortality in Uganda. Suboptimal newborn care practices resulting from hypothermia, poor hygiene and delayed initiation of breastfeeding are leading predisposing factors. Evidence suggests focused educational prenatal care messages to mitigate these problems. However, there is a paucity of data on the interaction between the service provider and the prenatal service user. This study aims to understand the scope of educational information and current practices on newborn care from the perspectives of prenatal mothers and health workers.Methods: A qualitative descriptive methodology was used. In-depth interviews were conducted with lactating mothers (n = 31) of babies younger than five months old across Masindi in western Uganda. Additional interviews with health workers (n = 17) and their employers or trainers (n = 5) were conducted to strengthen our findings. Data were audio-taped and transcribed verbatim. A thematic content analysis was performed using NVivo 8.Results: Vertical programmes received more attention than education for newborn care during prenatal sessions. In addition, attitudinal and communication problems existed among health workers thereby largely ignoring the fundamental principles of patient autonomy and patient-centred care. The current newborn care practices were largely influenced by relatives' cultural beliefs rather than by information provided during prenatal sessions. There is a variation in the training curriculum for health workers deployed to offer recommended prenatal and immediate newborn care in the different tiers of health care.Conclusions: Findings revealed serious deficiencies in prenatal care organisations in Masindi. Pregnant mothers remain inadequately prepared for childbirth and newborn care, despite their initiative to follow prenatal sessions. These findings call for realignment of prenatal care by integrating education on newborn care practices into routine antenatal care services and be based on principles of patient-centred care. [ABSTRACT FROM AUTHOR]