PhyloView: A System to Visualize the Ecology of Infectious Diseases Using Phylogenetic Data

Since the onset of the COVID-19 pandemic, mil-lions of coronavirus sequences have been rapidly deposited in publicly available repositories. The sequences have been used primarily to monitor the evolution and transmission of the virus. In addition, the data can be combined with spatiotemporal information and mapped over space and time to understand transmission dynamics further. For example, the first COVID-19 cases in Australia were genetically related to the dominant strain in Wuhan, China, and spread via international travel. These data are currently available through the Global Initiative on Sharing Avian Influenza Data (GISAID) yet generally remains an untapped resource for data scientists to analyze such multi-dimensional data. Therefore, in this study, we demonstrate a system named Phyloview, a highly interactive visual environment that can be used to examine the spatiotemporal evolution of COVID-19 (from-to) over time using the case study of Louisiana, USA. PhyloView (powered by ArcGIsInsights) facilitates the visualization and exploration of the different dimensions of the phylogenetic data and can be layered with other types of spatiotemporal data for further investigation. Our system has the potential to be shared as a model to be used by health officials that can access relevant data through GISAID, visualize, and analyze it. Such data is essential for a better understanding, predicting, and responding to infectious diseases. © 2022 IEEE.

Toward COVID-19 Decision Support: Modeling of Transmission Dynamics Extended with a Comprehensive Mitigation Protocol to Predict Health, Cost and Productivity Outcomes

This paper reports on the development of a model of COVID-19 transmission dynamics that takes into account a comprehensive mitigation protocol. This is necessary for public health decision support and making actionable recommendations on COVID-19 response. The comprehensive mitigation protocol includes (1) personal protection and social distancing, (2) use of smart applications for symptom reporting and contact tracing, (3) targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing, (4) surveillance testing, and (5) shelter, quarantine and isolation procedures. The proposed model (1) extends a common epidemiological discrete dynamic model with the comprehensive mitigation protocol, (2) uses Bayesian probability analysis to estimate the conditional probabilities of being in non-circulating epidemiological sub-compartments as a function of the mitigation protocol parameters, based on which it (3) estimates transition ratios among the compartments, and (4) computes a range of key performance indicators including health outcomes, mitigation cost and productivity loss. The proposed model can serve as a critical component for COVID-19 mitigation decision support and recommender systems, as part of a broader effort to support urgent pandemic response. © 2021 IEEE.

Using Wi-Fi Infrastructure to Predict Contacts during Pandemics

Wi-Fi log data can be used as an alternative to Bluetooth and other protocols to detect possible exposure to an infected person without requiring installation of specialized apps or changes to infrastructure. This paper summarizes examination of five increasingly complex approaches to predict individuals' locations indoors and consequently predict exposure to infections: (N1) Common Wi-Fi access point;(N2) In the building at the same time;(PI) Location predicted from Wi-Fi coverage;(P2) Location predicted from Wi-Fi coverage and previous location;and (P3) Movement predicted from floorplan graph. Data for 12 study participants completing 158 simulated scenarios were collected. The data were further used to create synthetic exposure datasets with up to 1000 people in a building over an 8-hour period. Accuracy of five algorithms applied to predicting locations and contacts was examined. The best results were obtained from method P3 that achieved: recall 0.898 and confidence 0.24 in predicting locations, and AUC 0.86 in predicting contacts. These results indicated that for environments with enterprise Wi-Fi infrastructure through a building, predicting movement using Wi-Fi log data along with known floorplans can accurately predict exposures. © 2021 IEEE.

A Decision Guidance System for COVID-19 Comprehensive Mitigation with Pareto-Optimal Health, Cost and Productivity Outcomes

This paper reports on the design and development of a decision guidance system to make actionable recommendations on a COVID-19 comprehensive mitigation protocol that is Pareto-Optimal in terms of health outcomes, mitigation cost and productivity loss. The comprehensive mitigation protocol includes personal protection and social distancing;use of smart applications for symptom reporting and contact tracing;targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing;random surveillance testing, and;shelter, quarantine and isolation procedures. The decision guidance system (1) gets, as input, expert-generated configurations of epidemiological parameters and assumptions on population behavior, (2) precomputes a database of discretized Pareto-optimal mitigation protocol alternatives based on which it (3) provides decision makers an iterative methodology of (a) Pareto-optimal KPI graphing and trade-off analysis (between health, cost and productivity outcomes), (b) detailed comparison of selected Pareto-optimal mitigation protocol alternatives, and (c) what-if analysis for selected protocol alternatives, including disease progression over the time horizon and sensitivity analysis to refine and converge on the mitigation protocol to be used. © 2021 IEEE.