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Summary: The recent pandemic caused by COVID‐19 has globally affected air transport mobility as well as the airline industry in general. Numerous restrictions have been implemented in airline transport, which is potentially leading toward severe long‐term impacts on the global airline industry. In this paper, air transport mobility was analyzed regarding Europe (EU) based on the available data from the relevant sources associated with the airline industry. Data were analyzed in specific periods from January to April of 2020, which corresponded with the initialization of the pandemic in the EU and later in its full development. Specifically, two airports were selected in Croatia as case studies to analyze the impact of COVID‐19 more thoroughly on mobility together with the estimation of carbon footprint during the pandemic and the year before the pandemic state. The results revealed that COVID‐19 gradually affected air transport mobility in the EU where a peak was reached in April with a reduction in the number of flights in the EU region reaching more than 89%. Cargo traffic was not significantly affected by the pandemic, and was even increased in some cases due to the supply of medical equipment in the fight against the disease. The analyzed case study revealed the reduction in air transport mobility for selected airports to be more than 96%, which directly affected the reduction of CO2 emissions to factor 1.81 for the commercial airport of Zagreb and 3.49 for the seasonal airport of Split. A normalization of air transport mobility is expected to be reached through a 1‐year period with a continued reduction in the number of flights ranging from 15% to 25%, based on the projections and expectations from relevant EU associations. [ABSTRACT FROM AUTHOR]

The global pandemic, COVID-19, is an acute respiratory infectious disease caused by the 2019 novel coronavirus. Building the online epidemic supervising system to provide COVID-19 dynamic prediction and analysis has attracted the attention of the industry and applications community. In previous studies, the compartmental models and deep neural networks (DNNs) played important roles in predicting and analyzing the dynamics of the pandemic. Nevertheless, the compartmental model has limited ability to fit historical data and thus leads to unsatisfactory prediction accuracy due to the difficulty in parameter estimation. For DNNs, the lack of interpretability makes it difficult to explain the prediction results; thus, it cannot provide an in-depth understanding of the transmission mechanism of the pandemic. We propose a fusion model to leverage the merits of both models and resolve their shortcomings. The fusion model extracts epidemic-related knowledge from the state-of-the-art SEIDR compartmental model to guide the training of the GRU model, which can preserve the interpretability and achieve a good performance in predicting epidemic dynamics. This model can help to enhance the online epidemic supervising system by providing more accurate prediction results and deeper analysis. Our extensive experiments across multiple epidemic datasets from six European countries demonstrate that our model outperforms existing state-of-the-art baselines in predicting the active confirmed cases. More importantly, by analyzing the effective reproductive number, our method can reveal the risk of the second wave of the epidemic in Europe and justify the importance of social distancing to control the outbreak of the epidemic. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic is a new reality, and it severely affects the modus vivendi of the international community. In this context, governments are rushing to devise or embrace novel surveillance mechanisms and monitoring systems to fight the outbreak. The development of digital tracing apps, which among others are aimed at automatising and globalising the prompt alerting of individuals at risk in a privacy-preserving manner, is a prominent example of this ongoing effort. Very promptly, a number of digital contact tracing architectures have been sprouted, followed by relevant app implementations adopted by governments worldwide. Bluetooth, specifically its Low Energy (BLE) power-conserving variant, has emerged as the most promising short-range wireless network technology to implement the contact tracing service. This work offers the first to our knowledge full-fledged review of the most concrete contact tracing architectures proposed so far in a global scale. This endeavour does not only embrace the diverse types of architectures and systems, namely, centralised, decentralised, or hybrid, but also equally addresses the client side, i.e., the apps that have been already deployed in Europe by each country. There is also a full-spectrum adversary model section, which does not only amalgamate the previous work in the topic but also brings new insights and angles to contemplate upon. [ABSTRACT FROM AUTHOR]