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Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1

Published: 30 October 2022 Publication History

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 eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. 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.

1 Introduction

Welcome to the first of two volumes of the ACM Transactions on Spatial Algorithms and Systems’s special issue on “Understanding the Spread of COVID-19.” Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals that interact and transmit viruses via spatiotemporal processes that manifest across and between scales [6, 9, 15]. The manifestation of individuals’ behavior in the space-time continuum is essential to the understanding, prediction, and efficient response when it comes to disease outbreaks [19, 20]. Despite its crucial importance in pandemic prevention and response, our understanding of how spatiotemporal behavior affects the spread of infectious diseases is limited [7].
To improve this understanding, newsletters, and workshops have been organized to bring together experts in spatiotemporal data, public health, epidemiology, and social sciences. Specifically, immediately upon the onset of the COVID-19 pandemic, in March 2020, the ACM SIGSPATIAL Newsletter released a call for papers for COVID-19 related articles that led to two special issues on “Understanding the Spread of COVID-19” [30, 31], published rapidly in July 2020 and November 2020. Newsletter articles included work on COVID-19 data sources [24], mapping mobility changes during COVID-19 [13], COVID-19 cluster detection [14, 17], epidemic simulation [12], contact tracing [21, 29], and spread forecasting [8, 18]. To discuss results, the “1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19” [5] has been co-located with ACM SIGSPATIAL’20 and the “2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology” [4] has been co-located with ACM SIGSPATIAL’21. These interdisciplinary workshops featured keynotes by experts in epidemiology, public health, and biostatistics and presented 11 more papers on related topics, including infection risk estimation [2, 16], disease monitoring [1], epidemic simulation [23, 26, 27], impact of COVID-19 on education [28], COVID-19 cluster detection [3], spatiotemporal analysis [10, 11], and spatiotemporal visualization [25]. Finally, in January 2022, Dagstuhl Seminar 22021 on Mobility Data Science [22] discussed the role of human mobility in the understanding of disease ecology among many other topics.
This plethora of research shows how many research were working on understanding the spread of COVID-19 in their respective communities. To provide a forum to publish research results that may be inspired from the discussions had in aforementioned newsletter articles and workshops, this special issue was announced in January 2021 with a submission deadline on May 31, 2021. We received 28 submissions, of which 13 manuscripts have been accepted for publication. The topics range from examining the impacts of city lockdown on human mobility and the association with COVID-19 spread to infection diagnosis using X-ray images. Of the 13 accepted manuscripts, 5 manuscripts were accepted after one round of revision and 8 manuscripts were accepted after two rounds of revision. Of the 15 rejected manuscripts 11 were rejected in the first round and 4 were rejected after a revision. We want to cordially thank the many reviewers around the world for their diligent work that has helped the authors to substantially improve their manuscripts.

2 Overview of the Articles Featured in This First Volume

The first paper, titled “Building Occupancy Simulation and Analysis under Virus Scenarios,” by Sydora et al., presents a systematic simulation-based methodology for estimating the infection risk among a building’s occupants under different scenarios of building usage. The authors evaluate their simulations against real-world building-usage data from a university campus building to demonstrate the realism of their simulations. Based on this simulation, the authors develop a virus-transmission model that estimates the potential infection-transmission risk given the behaviors of a building’s occupants. This methodology enables building managers to simulate alternative building-usage scenarios and estimate their relative infection-transmission risk that is useful in making decisions about alternative building-usage options.
In the second paper, titled “COVID-19 and Minimizing Micro-Spatial Interactions,” Burtner and Murray evaluate the potential of reducing the spread of COVID-19 in an office setting through the minimization of micro-spatial interactions. The authors propose a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. The proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments and (2) that current social distancing measures may be inadequate within office settings.
In the third paper, titled “Human Mobility-based Individual-level Epidemic Simulation Platform,” Fan et al. propose a simulation using a novel fine-grained transmission model based on real-world human mobility data. The authors develop a platform that helps researchers and decision-makers to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform also helps determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential infectious individuals.
The fourth paper, titled “A High-resolution Global-scale Model for COVID-19 Infection Rate,” by Coro and Bove, presents a high-resolution global-scale probability map of low and high-infection-rates of COVID-19 that uses annual-average surface air temperature, precipitation, and CO2 as environmental parameters and Italian provinces as training locations. A risk index calculated on this map correctly identifies 87% of the World countries that reported high infection rates in 2020 and 80% of the low and high infection-rate countries overall. The proposed model estimates the base environmental inertia that a geographical place opposes to COVID-19 when mobility restrictions are not in place and can support how much the monthly weather favors or penalizes infection increase. Its high resolution and extent make it consistently usable in global and regional-scale analyses.
In the fifth paper, titled “Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response,” Pejó and Biczók approach the problem of modeling the spread of COVID-19 from a game-theoretic perspective. Through simple games, they show the effect of individual incentives on the decisions made with respect to mask wearing, social distancing, and vaccination and how these may result in sub-optimal outcomes. This work demonstrates the responsibility of national authorities in designing these games properly regarding data transparency, the chosen policies, and their influence on the preferred outcome. The authors promote a mechanism design approach: It is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism; moreover, there is no one-size-fits-all solution when designing an effective solution
In the sixth paper, titled “Analyzing the Impact of COVID-19 Control Policies on Campus Occupancy and Mobility via WiFi Sensing,” Zakaria et al. conjecture that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions monitor and maintain safety compliance according to the public health guidelines. Using smartphones as a proxy for user location, the authors demonstrate how coarse-grained WiFi data can sufficiently reflect the indoor occupancy spectrum when different COVID-19 policies were enacted. This work analyzes staff and students’ mobility data from three university campuses. Two of these campuses are in Singapore, and the third is in the northeastern United States. Results show that online learning, split-team, and other space management policies effectively lower occupancy. However, they do not change the mobility for individuals transitioning between spaces. The authors demonstrate how this data source can be a practical application for institutional crowd control.
The seventh paper, titled “Data-driven Mobility Analysis and Modeling: Typical and Confined Life of a Metropolitan Population,” by Fanticelli et al. conducts a detailed spatio-temporal analysis of population mobility flows using mobile phone data between 326 aggregated zones in the the Paris metropolitan area during the France lockdown period (from March 17, 2020, to May 11, 2020). The mobile phone data-derived visit temporal patterns for each zone is used for automatic land use inference (i.e., residential, activity, or outlier area). This allows the observation of how the usage of any given area changes in real time once the lockdown is established. Utilizing the centrally metrics on mobility flow graphs, the authors analyze the importance factor to understand the zonal differences of mobility flow changes before and during COVID-19 lockdowns in Paris.
The eighth and final papers, titled “COVID-19 along with Other Chest Infections Diagnosis Using Faster R-CNN and Generative Adversarial Network,” by Mostafiz et al., presents a novel approach to the diagnosis (classification) of COVID-19 along with other chest infections using Faster R-CNN based on a mix of 3,933 original X-ray images and 6,406 synthetic images generated from Generative Adversarial Networks. This approach addressed the training labeled data imbalance problem. The experiment gets a high COVID-19 diagnosis accuracy of 99.16%.
We hope that readers enjoy these papers and find them interesting. Thanks are due to all the authors who have submitted their papers to this special issue and to the reviewers who have helped select this interesting set of papers and for providing high-quality reviews in a timely fashion to enhance the quality of the accepted papers. Finally, we hope that this collection of papers will facilitate further research in this exciting area of spatial algorithms and systems.

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  • (2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024

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cover image ACM Transactions on Spatial Algorithms and Systems
ACM Transactions on Spatial Algorithms and Systems  Volume 8, Issue 3
September 2022
185 pages
ISSN:2374-0353
EISSN:2374-0361
DOI:10.1145/3512350
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2022
Accepted: 06 October 2022
Revised: 05 October 2022
Received: 05 October 2022
Published in TSAS Volume 8, Issue 3

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  1. Infectious diseases
  2. pandemic preparedness
  3. spatiotemporal systems
  4. geographical information systems
  5. health informatics

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  • (2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024

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