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The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse. [ABSTRACT FROM AUTHOR]

Digital contact tracing (DCT) is one of the weapons to be used against the COVID-19 pandemic, especially in a post-lockdown phase, to prevent or block foci of infection. As DCT systems can handle highly private information about people, great care must be taken to prevent misuse of the system and actions detrimental to people's privacy, up to mass surveillance. This paper presents a new centralized DCT protocol, called ZE2-P3T (Zero Ephemeral Exchanging Privacy-Preserving Proximity Protocol), which relies on smartphone localization but does not give any information about the user's location and identity to the server. Importantly, the fact that no exchange of ephemeral identities among users is required is the basis of the strong security of the protocol, which is proven to be more secure than the state-of-the-art protocol DP-3T/GAEN. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has upended every aspect of American life. State governments responded quickly to protect public health and stabilize overwhelmed hospital systems. The most restrictive policy, the stay-at-home order, was seen by public health officials as a cornerstone of successful state mitigation strategies. But like many aspects of contemporary politics, support for these efforts took on a distinctly partisan hue. In this paper, I argue that party politics significantly affected state policy responses to COVID-19, which in turn limited mitigation efforts. To this point, I first demonstrate that Democratic governors were faster and more likely to adopt stay-at-home orders than Republicans. Next, using a synthetic control approach, I show that these orders caused residents to practice greater social distancing. Finally, I find that greater social distancing worked to "flattened the curve" by limiting the growth of COVID-19 cases. Together these findings show how party politics affected state pandemic responses and have important long-term implications as states begin lifting restrictions. [ABSTRACT FROM AUTHOR]