Collaborative Zoom Coding—A Novel Approach to Qualitative Analysis

During the current coronavirus (COVID-19) pandemic, web conferencing became a staple in professional communication, with new and evolving applications amidst unique social distancing measures mandated across the globe. In this article, we describe Collaborative Zoom Coding (CZC) as an adaptive approach to qualitative data analysis that our research team developed in light of social distancing measures imposed due to the COVID-19 pandemic. CZC uses the web conferencing platform Zoom, to help analyze data. Our team used CZC to develop a code book for the community-based research (CBR) project, Sexual Health and Diasporic Experiences of Shadeism (SHADES). CZC enabled all team members to participate in data analysis by providing opportunities for group training and real-time collaborative data analysis, irrespective of team members’ location and level of experience with research. This article describes our specific processes for CZC and outlines its advantages as well as challenges. We conclude with a discussion of how researchers can conduct collaborative coding using Zoom and other conferencing technologies to further democratize the research process, particularly for community-based research endeavors. © The Author(s) 2022.

DeepSEAS: Smartphone-based Early Ailment Sensing Using Coupled LSTM AutoEncoders

Infectious diseases epidemics such as the current COVID-19 pandemic have an immense impact on all facets of life. Consequently, the current dearth of effective and timely public health surveillance methods, especially at the individual level, have been accentuated, prompting research into supplementary methods. Sensor-rich, ubiquitously owned smartphones can now gather large volumes of data that has been utilized for passive and continuous physical and mental health assessment. In this paper, we propose a Deep learning based Smartphone Early Ailment Sensing (DeepSEAS) framework that predicts a smart-phone user's future manifestation of influenza-like biological symptoms (e.g. coughing and sneezing) a day early while they are still asymptomatic. DeepSEAS works by analyzing a subject's historical one-day smartphone sensor and mobility data. First, we utilize the mean shift clustering algorithm to create clusters of users with similar social and behavioral traits such as their socialization levels, social media presence, eating and working out habits. Then, DeepSEAS employs an end-to-end trainable LSTM Autoencoder (LSTM AE) coupled with a Feed Forward Neural network classifier, a chieving a sensitivity of 7 8% i n correctly identifying users who will manifest biological symptoms a day later. DeepSEAS facilitates up-to-date influenza s urveillance at the individual level, which could transform the current healthcare system. Early detection can enable asymptomatic users to be alerted, notified and isolated, which could reduce disease transmission. © 2020 IEEE.