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Escherichia coli O157:H7 is a foodborne pathogen, which causes various health conditions in humans, including fatigue, nausea, bloody diarrhoea and in some cases, even death. In 2017, 15.71% of the total imported food products in Saudi Arabia (SA) were meat-based. India and Brazil are two of the top five countries from where SA imports meat. According to the Saudi Food and Drug Authority, in 2017, at least 562, 280, and 50 samples of imported beef, chicken and sheep meat, respectively, were tested for the presence of E. coli O157:H7. Amongst these, E. coli O157:H7 was detected in respectively 6.80% and 2.20% of the tested beef meat samples imported from India and Brazil as well as in respectively 6.96% and 3.57% of the tested chicken samples imported from Brazil and Ukraine. Moreover, the pathogen was detected in 2.13% of the tested sheep meat samples imported from India. The present report provides evidence that imported meat can serve as the carrier of E. coli O157:H7, which may lead to epidemics within the Kingdom of Saudi Arabia. [ABSTRACT FROM AUTHOR]

This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread. [ABSTRACT FROM AUTHOR]