ABSTRACT
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: Overall, ML algorithms seem to be an effective method in early detection and prediction of food insecurity. Future research would benefit from utilizing the proposed model in developing more complex and accurate models aiming to enhance granularity, with the ability to share data, to incorporate wide range of variables, and to make use of automation for effective prevention and intervention programs at the regional and individual levels.
Subject(s)
COVID-19 , Depressive DisorderABSTRACT
COVID-19 has affected all aspects of human life so far. From the outset of the pandemic, preventing the spread of COVID-19 through the observance of health protocols, especially the use of sanitizers and disinfectants was given more attention. Despite the effectiveness of disinfection chemicals in controlling and preventing COVID-19, there are critical concerns about their adverse effects on human health. This study aims to assess the health effects of sanitizers and disinfectants on a global scale. A total of 91056 participants from 154 countries participated in this cross-sectional study through an electronic questionnaire. Results implied that detergents (67%), alcohol-based materials (56%), and chlorinated compounds (32%) were the most commonly used types of sanitizers and disinfectants. Most frequently reported health issues include skin complications 48.8% and respiratory complications 29.8%. The Chi-square test showed a significant association between chlorinated compounds with all possible health complications under investigation (p-value < 0.001). Examination of risk factors based on multivariate regression analysis showed that alcohols-based materials were associated with skin complications (OR, 1.98; 95%CI, 1.87–2.09), per-chlorine was associated with eye complications (OR, 1.83; 95%CI, 1.74–1.93), and highly likely with itching and throat irritation (OR, 2.00; 95%CI, 1.90–2.11). Furthermore, formaldehyde was associated with a higher prevalence of neurological complications (OR, 2.17; 95%CI, 1.92–2.44). The findings of the current study suggest that health authorities need to implement more awareness programs about the side effects of using sanitizers and disinfectants during viral epidemics.