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Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users' daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either "potentially COVID-19 infected" or "no evident signs of infection". We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).

With the advent of COVID-19 arose the need for social distancing measures, including the imposition of far-reaching lockdowns in many countries. The lockdown has wreaked havoc on many aspects of daily life, but education has been particularly hard hit by this unprecedented situation. The closure of educational institutions brought along many changes, including the transition to more technology-based education. This is a systematic literature review that seeks to explore the transition, in the context of the pandemic, from traditional education that involves face-to-face interaction in physical classrooms to online distance education. It examines the ways in which this transition has impacted academia and students and looks at the potential long-term consequences it may have caused. It also presents some of the suggestions made by the studies included in the paper, which may help alleviate the negative impact of lockdown on education and promote a smoother transition to online learning.

Water scarcity has urged the need for adequate water demand forecasting to facilitate efficient planning of municipal infrastructure. However, the development of water consumption models is challenged by the rapid environmental and socio-economic changes, particularly during unforeseen events like the COVID-19 pandemic. This study investigated the impact of COVID-19 on the efficiency of water demand prediction models, considering the lockdown measures and various exogenous features, such as previous consumption (PC) and socio-demographic (SDF), seasonal (SF), and climatic (CF) factors. Multiple ensemble models, gradient-boosting machines (GBM), extreme-gradient-boosting (XGB), light-gradient-boosting, random forest (RF), and stack regressor (STK) were examined, compared to other machine-learning techniques, multiple-linear regression (MLR), decision trees, and neural networks. The models were tested using 3-year metering records for 128,000 consumers in Dubai. The feature importance analysis indicated that PC and SDF had a significant impact on consumption rates with correlation coefficients of 0.95 and 0.74, respectively, as opposed to SF and CF, which had negligible effect. The results showed that, before COVID, RF and STK outperformed other models with a coefficient-of-determination (R2) and root-mean-squared-error (RMSE) of 0.928 and 0.039, followed by XGB at 0.923 and 0.041, respectively. However, MLR achieved the highest prediction accuracy amid COVID with R2 and RMSE of 0.90 and 0.05, followed by GBM and XGB equally at 0.83 and 0.06, respectively. An ensemble-based error prediction model was applied, resulting in up to 9.2% improvement in predictions. Overall, this research emphasized the efficiency of ensemble models in handling fluctuating data with a high degree of nonlinearity. • Impact of COVID-19 on the performance of artificial intelligence models was evaluated. • Effect of multiple exogenous factors on water consumption patterns was investigated. • Socio-demographic factors had the greatest impact on water consumption. • Forecasting models achieved high accuracy prior to COVID-19 but declined afterward. • Ensemble models had the highest adaptability to irregular consumption amid COVID-19. [ABSTRACT FROM AUTHOR]