Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 2

Author:

Andreas Züfle1ORCID,Gao Song2ORCID,Anderson Taylor3ORCID

Affiliation:

1. Emory University, Georgia, USA

2. University of Wisconsin-Madison, Wisconsin, USA

3. George Mason University, Virginia, USA

Abstract

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the five articles included in the second volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to close contact modeling, infectious diseases spread prediction, mobility analysis, effective testing and intervention strategies. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.

Publisher

Association for Computing Machinery (ACM)

Subject

Discrete Mathematics and Combinatorics,Geometry and Topology,Computer Science Applications,Modeling and Simulation,Information Systems,Signal Processing

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Mobility Data Science: Perspectives and Challenges;ACM Transactions on Spatial Algorithms and Systems;2024-06-30

2. How information propagation in hybrid spaces affects decision-making: using ABM to simulate Covid-19 vaccine uptake;International Journal of Geographical Information Science;2024-04-03

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