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Using Demographic Pattern Analysis to Predict COVID-19 Fatalities on the US County Level

Authors:

Eric PapenhausenAuthors Info & Claims

Digital Government: Research and Practice, Volume 2, Issue 1

Article No.: 12, Pages 1 - 11

https://doi.org/10.1145/3430196

Published: 03 December 2020 Publication History

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Abstract

Unlike pandemics in the past, COVID-19 has hit us in the midst of the information age. We have built vast capabilities to collect and store data of any kind that can be analyzed in myriad ways to help us mitigate the impact of this catastrophic disease. Specifically for COVID-19, data analysis can help local governments to plan the allocation of testing kits, testing stations, and primary care units, and it can help them in setting guidelines for residents, such as the need for social distancing, the use of face masks, and when to open local businesses that enable human contact. Further, it can also lead to a better understanding of pandemics in general and so inform policy makers on the regional and national level. All of this can save both cost and lives. In this article, we show the results of an ongoing study we conducted using a prominent regularly updated dataset. We used a pattern mining engine we developed to find specific characteristics of US counties that appear to expose them to higher COVID-19 mortality. Furthermore, we also show that these characteristics can be used to predict future COVID-19 mortality.

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Cited By

Wu JNiu ZLiu X(2024)Understanding epidemic spread patterns: a visual analysis approachHealth Systems10.1080/20476965.2024.2308286(1-17)Online publication date: 25-Jan-2024
https://doi.org/10.1080/20476965.2024.2308286
Silva JFerreira NAlmeida Filho A(2023)A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID-19 PandemicMathematical Problems in Engineering10.1155/2023/95247532023(1-16)Online publication date: 20-Feb-2023
https://doi.org/10.1155/2023/9524753
Jain KBhatnagar VKaur S(2023)Epidemic dynamics in census-calibrated modular contact networkNetwork Modeling Analysis in Health Informatics and Bioinformatics10.1007/s13721-022-00402-112:1Online publication date: 10-Jan-2023
https://doi.org/10.1007/s13721-022-00402-1
Show More Cited By

Index Terms

Using Demographic Pattern Analysis to Predict COVID-19 Fatalities on the US County Level
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
  2. Visualization
    1. Visualization application domains
      1. Visual analytics
    2. Visualization systems and tools

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Information & Contributors

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Published In

cover image Digital Government: Research and Practice

Digital Government: Research and Practice Volume 2, Issue 1

COVID-19 Commentaries

January 2021

116 pages

EISSN:2639-0175

DOI:10.1145/3434277

Editors:
Soon Ae Chun
City University of New York, USA
,
Beth Simone Noveck
The Governance Lab and New York University, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2020

Online AM: 30 October 2020

Accepted: 01 October 2020

Revised: 01 August 2020

Received: 01 June 2020

Published in DGOV Volume 2, Issue 1

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NSF SBIR
New York State SPIR program, and the Stony Brook University Sensor CAT program

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Cited By

Wu JNiu ZLiu X(2024)Understanding epidemic spread patterns: a visual analysis approachHealth Systems10.1080/20476965.2024.2308286(1-17)Online publication date: 25-Jan-2024
https://doi.org/10.1080/20476965.2024.2308286
Silva JFerreira NAlmeida Filho A(2023)A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID-19 PandemicMathematical Problems in Engineering10.1155/2023/95247532023(1-16)Online publication date: 20-Feb-2023
https://doi.org/10.1155/2023/9524753
Jain KBhatnagar VKaur S(2023)Epidemic dynamics in census-calibrated modular contact networkNetwork Modeling Analysis in Health Informatics and Bioinformatics10.1007/s13721-022-00402-112:1Online publication date: 10-Jan-2023
https://doi.org/10.1007/s13721-022-00402-1
Yu SCarroll F(2023)Securing Privacy During a World Health Emergency: Exploring How to Create a Balance Between the Need to Save the World and People’s Right to PrivacyData Protection in a Post-Pandemic Society10.1007/978-3-031-34006-2_5(145-167)Online publication date: 8-May-2023
https://doi.org/10.1007/978-3-031-34006-2_5
Srabanti SMarai GMiranda F(2021)COVID-19 EnsembleVis: Visual Analysis of County-Level Ensemble Forecast Models2021 IEEE Workshop on Visual Analytics in Healthcare (VAHC)10.1109/VAHC53616.2021.00005(1-5)Online publication date: Oct-2021
https://doi.org/10.1109/VAHC53616.2021.00005
Alnemari ARaj RRomanowski CMishra S(2021)Interactive Range Queries for Healthcare Data under Differential Privacy2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI52183.2021.00044(228-237)Online publication date: Aug-2021
https://doi.org/10.1109/ICHI52183.2021.00044

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