ShareTrace: Contact Tracing with the Actor Model

Proximity-based contact tracing relies on mobile-device interaction to estimate the spread of disease. ShareTrace is one such approach that improves the efficacy of tracking disease spread by considering direct and indirect forms of contact. In this work, we utilize the actor model to provide an efficient and scalable formulation of ShareTrace with asynchronous, concurrent message passing on a temporal contact network. We also introduce message reachability, an extension of temporal reachability that accounts for network topology and message-passing semantics. Our evaluation on both synthetic and real-world contact networks indicates that correct parameter values optimize for algorithmic accuracy and efficiency. In addition, we demonstrate that message reachability can accurately estimate the risk a user poses to their contacts. © 2022 IEEE.

A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2.

Although the coronavirus epidemic spread rapidly with the Omicron variant, it lost its lethality rate with the effect of vaccine and immunity. The hospitalization and intense demand decreased. However, there is no definite information about when this disease will end or how dangerous the different variants could be. In addition, it is not possible to end the risk of variants that will continue to circulate among animals in nature. After this stage, drug-virus interactions should be examined in order to be able to prepare against possible new types of viruses and variants and to rapidly-produce drugs or vaccines against possible viruses. Despite experimental methods that are expensive, laborious, and time-consuming, geometric deep learning(GDL) is an alternative method that can be used to make this process faster and cheaper. In this study, we propose a new model based on geometric deep learning for the prediction of drug-virus interaction against COVID-19. First, we use the antiviral drug data in the SMILES molecular structure representation to generate too many features and better describe the structure of chemical species. Then the data is converted into a molecular representation and then into a graphical structure that the GDL model can understand. The node feature vectors are transferred to a different space with the Message Passing Neural Network (MPNN) for the training process to take place. We develop a geometric neural network architecture where the graph embedding values are passed through the fully connected layer and the prediction is actualized. The results indicate that the proposed method outperforms existing methods with 97% accuracy in predicting drug-virus interactions.

Deep Molecular Representation Learning via Fusing Physical and Chemical Information

Molecular representation learning is the first yet vital step in combining deep learning and molecular science. To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules. PhysChem is composed of a physicist network (PhysNet) and a chemist network (ChemNet). PhysNet is a neural physical engine that learns molecular conformations through simulating molecular dynamics with parameterized forces;ChemNet implements geometry-aware deep message-passing to learn chemical/biomedical properties of molecules. Two networks specialize in their own tasks and cooperate by providing expertise to each other. By fusing physical and chemical information, PhysChem achieved state-of-the-art performances on MoleculeNet, a standard molecular machine learning benchmark. The effectiveness of PhysChem was further corroborated on cutting-edge datasets of SARS-CoV-2. © 2021 Neural information processing systems foundation. All rights reserved.

DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks-a Case Study for Covid-19 Spread and Interventions

We introduce DeepABM, a computational framework for agent-based modeling that leverages geometric message passing for simulating action and interactions over large agent populations. Using DeepABM allows scaling simulations to large agent populations in real-time and running them efficiently on GPU architectures. Using the DeepABM framework, we build DeepABM-COVID simulator to provide support for various non-pharmaceutical interventions (quarantine, exposure notification, vaccination, testing) for the COVID-19 pandemic, and can scale to populations of representative size in real-time on a GPU. DeepABM-COVID can model 200 million interactions (over 100,000 agents across 180 time-steps) in 90 seconds, and is made available online to help researchers with modeling and analysis of various interventions. We explain various components of the framework and discuss results from one research study to evaluate the impact of delaying the second dose of the COVID-19 vaccine in collaboration with clinical and public health experts. © 2021 IEEE.

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan. IEEE

LISA - Lingua Italiana dei Segni Accessibile: A Progressive Web App to Support Communication Between Deaf People and Public Administrations

Most deaf people use Sign Language (SL) to communicate. This usually requires the presence of an SL interpreter to mediate and decode the communication with a non-deaf person. However, the presence of an SL interpreter to support a deaf person can be very difficult, expensive and not always possible, for example during the COVID-19 pandemic which requires limiting contact between people in presence. This work proposes a Progressive Web Application (PWA), called LISA, as a solution to facilitate communication between a deaf citizen and a non-deaf person, thanks to a remote Sign Language Interpreting Service (SLIS). The LISA prototype is designed to promote the communication of deaf citizens with the Public Administrations (PA). This real-time SLIS can be used flexibly on different types of devices (i.e. mobile and desk). This allows PA operators to easily respond to the needs of deaf citizens. Furthermore, to facilitate written communication and to overcome the difficulties encountered by deaf people in writing text messages, the LISA system integrates a text/SL gateway. The user selects items from a gallery of GIF images that represent simple pre-set phrases and words in SL, and the system can also convert them into text. This improves accessibility by offering a more suitable messaging tool than a text chat for the needs of the target population. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Learning through social distancing: Whatsapp as a community of inquiry

Once upon a time, researchers believed that the effective use of an online social media network to support a virtual community is dependent on the participants’ interest in the context within which the community exists and the willingness of the participants to be part of mobile instant messaging groups. But I thought that interacting via WhatsApp groups will enable them to accept differing views and opinions as part of the group activities. This could ensure effective group engagement and co-creation of learning. I taught a 45 to 60 minute lesson every week to first-year students. The group was divided into smaller sub-groups and assigned individual and group tasks. I analysed the messages that they sent in the form of answers, responses and feedbacks. Four questions aligned to the community of inquiry framework, form part of this study: (1) Social presence-How has WhatsApp contributed to student’s learning? (2) Teaching presence-Has the selected mode of engagement attracted students? (3) Cognitive presence-What kind of messages were conveyed? (4) Academic performance-Has it been beneficial towards their learning and in achieving learning outcomes? Data were collected during weekly lectures to first-year students using WhatsApp as a mobile instant messaging (MIM) platform and were analysed through WhatsAnalyzer. Finally, a matrix was proposed for the analysis of various aspects of communities of practice. I discovered that WhatsApp facilitated high levels of interactivity within the groups during the COVID19 lockdown, which will change the future of remote or online teaching. However, more research needs to be carried out to understand the reasons why some students learn better than others. © the authors, 2021. All Rights Reserved.