Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge

Abstract

In this paper, we describe the deep learning-based COVID-19 cases predictor and the Pareto-optimal Non-Pharmaceutical Intervention (NPI) prescriptor developed by the winning team of the 500k XPRIZE Pandemic Response Challenge, a four-month global competition organized by the XPRIZE Foundation. The competition aimed at developing datadriven AI models to predict COVID-19 infection rates and to prescribe NPI Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. In addition to the validation performed by XPRIZE with real data, the winning models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. We believe that this experience contributes to the necessary transition to more evidencedriven policy-making, particularly during a pandemic.

Inferring the fractional nature of Wu Baleanu trajectories

“…We will consider trajectories of length between 10 and 50, which are the most frequent in experiments and the hardest to be classified [25]. Such architecture has been successfully applied for analyzing trajectories [7] and time series [20]. It consists of three parts:…”

FedComm: Federated Learning as a Medium for Covert Communication

“…Deep Learning (DL) is the key factor for an increased interest in research and development in the area of Artificial Intelligence (AI), resulting in a surge of Machine Learning (ML) based applications that are reshaping entire fields and seedling new ones. Variations of Deep Neural Networks (DNN), the algorithms residing at the core of DL, have successfully been implemented in a plethora of domains, including here but not limited to image classification [17,35,80], natural language processing [6,22,73], speech recognition [33,37], data (image, text, audio) generation [7,40,44,61], cybersecurity [18,21,59], and even aiding with the COVID-19 pandemic [52,55].…”

Stability Analysis of Delayed COVID-19 Models

“…Their results show the appropriateness of the model, in particular with respect to the number of quarantined/hospitalized (confirmed and infected) and recovered individuals. Alternative approaches based on SIR-type models but that combine machine learning methods have also been developed; see, e.g., [32,33].…”