Highlights A new compartmental model, forced-SIR, quantifies COVID-19 pandemics impact. Effectiveness of COVID-19 intervention measures is linked to models parameters. Application of the model to 10 countries reveals a wide range of COVID-19 impacts.

The application of machine learning (ML) algorithms in the management of data are transforming the landscape of different scientific fields, including clinical medicine. 1 ML has the potential to radically change the way we practice cardiovascular medicine by providing new tools for interpreting data and making clinical decisions. While still a new player in cardiology, ML has already made its mark in

Objectives: Hypertension is a major risk factor for cardiovascular disease (CVD), which often escapes the diagnosis or should be confirmed by several office visits. The ECG is one of the most widely used diagnostic tools and could be of paramount importance in patients' initial evaluation.Methods: We used machine learning techniques based on clinical parameters and features derived from the ECG, to detect hypertension in a population without CVD. We enrolled 1091 individuals who were classified as hypertensive or normotensive, and trained a Random Forest model, to detect the existence of hypertension. We then calculated the values for the Shapley additive explanations (SHAP), a sophisticated feature importance analysis, to interpret each feature's role in the Random Forest's results.Results: Our Random Forest model was able to distinguish hypertensive from normotensive patients with accuracy 84.2%, specificity 78.0%, sensitivity 84.0% and area under the receiver-operating curve 0.89, using a decision threshold of 0.6. Age, BMI, BMI-adjusted Cornell criteria (BMI multiplied by RaVLRSV 3 ), R wave amplitude in aVL and BMI-modified Sokolow-Lyon voltage (BMI divided by SV 1 RRV 5 ), were the most important anthropometric and ECG-derived features in terms of the success of our model. Conclusion:Our machine learning algorithm is effective in the detection of hypertension in patients using ECGderived and basic anthropometric criteria. Our findings open new horizon in the detection of many undiagnosed hypertensive individuals who have an increased CVD risk.