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

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. The competition aimed at developing data-driven 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, our models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. Our experience contributes to a necessary transition to more evidence-driven policy-making during a pandemic. © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

SSL-MedImNet: Self-Supervised Pre-training of Deep Neural Network for COVID-19 Diagnosis

This paper applies self-supervised learning to diagnose coronavirus disease (COVID-19) among other pneumonia and normal cases based on chest Computed Tomography (CT) images. Being aware that medical imaging in real-world scenarios lacks well-verified and explicitly labeled datasets, which is known as a big challenge for supervised learning, we utilize Momentum Contrast v2 (MoCo v2) algorithm to pre-train our proposed Self-Supervised Medical Imaging Network (SSL-MedImNet) with remarkable generalization from substantial unlabeled data. The proposed model achieves competitive and promising performance in COVIDx CT-2, which is a well-known and high-quality dataset for COVID-19 assessment. Besides, its pre-trained representations can be transferred well for the diagnosis task. Moreover, SSL-MedImNet approximately matches its supervised candidates COVID-Net CT-1 and COVID-Net CT-2 by small distinctions. In particular, with only some additional dense layers, the proposed model achieves COVID-19 accuracy of 88.3% and specificity of 98.4% approximately, and competitive results for normal and pneumonia cases. The results advocate the potential of self-supervised learning to accomplish highly generalized understanding from unlabeled medical images and then transfer it for relevant supervised tasks in real scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

DEI Student Ambassadors Program

In this paper we describe the Diversity, Equity, and Inclusion (DEI) Student Ambassadors program at Seattle University and the initiatives and activities that the ambassadors have been engaged in. This program drew inspiration from several Bias Busters groups created by industry and academia, especially the Bias Busters @ Carnegie Mellon University and the Bias Busters in the Electrical Engineering and Computer Science Department at University of California Berkeley. The student ambassadors were given broad objectives to improve the college community and educate the student population about diversity, equity, and inclusion. An initial planned project of the ambassadors was to organize a DEI Takeover Week during spring of 2020. This project had to be postponed due to the COVID-19 pandemic. The student ambassadors took this as an opportunity to instead develop programs focused on equity and inclusion issues that arose due to the pandemic and the transition to remote / virtual instruction. The DEI Student Ambassadors organized Zoom Town Halls that were open to all students, faculty, and staff in the college to engage in frank conversations about the challenges of the pandemic and how they connected to issues of diversity, equity, and inclusion. Additionally, the DEI Student Ambassadors developed theater-based workshops about microaggressions and bystander intervention. These workshops were administered in a number of classes, and highlighted real-world scenarios drawn from student surveys conducted in spring 2018 as well as the DEI Student Ambassadors' own experiences. Response to the DEI Student Ambassadors and the programs they have developed has been largely very positive. We conclude by discussing plans for how to continue, adapt, and expand this program. © 2022 American Society for Engineering Education.

Graph Convolutional Networks for Predicting State-wise Pandemic Incidence in India

In this paper, we analyze the performance of graph convolutional networks (GCNs) in predicting COVID-19 incidence in states and union territories (UTs) in India as a semi-supervised learning task. By training the model with data from a small number of states whose incidence is known, we analyze the accuracy in predicting incidence levels in the remaining states and UTs in India. We explore the effect of pre-existing factors such as foreign visitor count, senior citizen population and population density of states in predicting spread. To show the robustness of this model, we introduce a novel method to choose states for training that reduces bias through random sampling in five regions that cover India’s geography. We show that GCNs, on average, produce a 9% improvement in accuracy over the best performing non-graph-based model and discuss if the results are feasible for use in a real-world scenario. © 2022 IEEE.