EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

Xu, Haowen; Berres, Anne; Thakur, Gautam; Sanyal, Jibonananda; Chinthavali, Supriya

doi:10.1016/j.jbi.2021.103941

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Afzal et al [70] created a visual analytics prototype that gives public health professionals the ability to simulate and model the spread of COVID-19 by providing county-level data on the populace, demographics, and hospitalizations. Xu et al [44] presented a web-based visual analytics tool, EPIsembleVis, for conducting a comparative visual analysis on the consistency of COVID-19 ensemble predictions under model uncertainties. Inspired by these studies, we began to work with domain experts to collect the most relevant data and extract the most salient features from those data for further visual analysis.…”

Section: Ai4vis-aided Decision-makingmentioning

confidence: 99%

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

Yu¹,

Liu²,

Cao³

et al. 2023

Preprint

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Clinic testing plays a critical role in containing infectious diseases such as COVID-19. However, one of the key research questions in fighting such pandemics is how to optimize testing capacities across clinics. In particular, domain experts expect to know exactly how to adjust the features that may affect testing capacities, given that dynamics and uncertainty make this a highly challenging problem. Hence, as a tool to support both policymakers and clinicians, we collaborated with domain experts to build ClinicLens, an interactive visual analytics system for exploring and optimizing the testing capacities of clinics. ClinicLens houses a range of features based on an aggregated set of COVID-19 data. It comprises Back-end Engine and Front-end Visualization that take users through an iterative exploration chain of extracting, training, and predicting testing-sensitive features and visual representations. It also combines AI4VIS and visual analytics to demonstrate how a clinic might optimize its testing capacity given the impacts of a range of features. Three qualitative case studies along with feedback from subject-matter experts validate that ClinicLens is both a useful and effective tool for exploring the trends in COVID-19 and optimizing clinic testing capacities across regions. The entire approach has been open-sourced online: https://github.com/YuDong5018/clinic-lens.

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Section: Ai4vis-aided Decision-makingmentioning

confidence: 99%

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

Yu¹,

Liu²,

Cao³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, many of the studies in this special issue were based upon a relatively small amount of real-world data, so follow-up research to replicate and extend the findings as more data become available will be important. Another challenge with forecasting studies can be the comparison of different models and Xu et al [26] present a web-based tool for comparative visual analysis of multiple COVID-19 prediction models. In summary, the forecasting studies in this special issue provide evidence that statistical and ML models can provide accurate short-term forecasts of multiple epidemiological indicators of COVID-19.…”

Section: Forecasting and Epidemic Modelingmentioning

confidence: 99%

“… Original Research [24] A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction Safari, A. Original Research [25] COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps JING, M. Original Research [26] EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models Xu, H. Special Communication [27] Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates Huang T. Original Research [28] An integrated framework for modelling quantitative effects of entry restrictions and travel quarantine on importation risk of COVID-19 Chen, T. Original Research [29] Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription Ubaru, S. Original Research Text mining of literature, social media, and trial documents (7) [30] Searching for scientific evidence in a pandemic: An overview of TREC-COVID Roberts, K. Special Communication [31] A comparative analysis of system features used in the TREC-COVID information retrieval challenge Chen, J. Original Research [32] Drug repurposing for COVID-19 via knowledge graph completion Zhang, R. Original Research [33] Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media Wu, J.…”

Section: Introductionmentioning

confidence: 99%

Novel informatics approaches to COVID-19 Research: From methods to applications

Buckeridge

Wang

et al. 2022

Journal of Biomedical Informatics

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A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient’s body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people’s behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended.

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EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

Cited by 6 publications

References 33 publications

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

Novel informatics approaches to COVID-19 Research: From methods to applications

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

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