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The 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19

Published: 25 January 2021 Publication History

Abstract

In response to the COVID-19 pandemic, a number of spatially-explicit models have been developed to better explain the pathways of the disease, to predict the trajectory of the disease, and to test the effect of different health guidelines and policies on the number of cases and deaths. The 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19 workshop (COVID'2020) featured research efforts that aim to understand the spatial processes and patterns of COVID-19 spread using a variety of spatial modeling, simulation, and mining approaches. The goal of this workshop was to bring together a range of interdisciplinary researchers in the SIGSPATIAL community in the fields of computer science, spatial modeling, social sciences, and epidemiology. Also, this workshop was advertised for anyone interested in infectious disease data and modelling, including but not limited to COVID-19.

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Cited By

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  • (2024)Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease SpreadACM Transactions on Spatial Algorithms and Systems10.1145/366063110:2(1-22)Online publication date: 1-Jul-2024
  • (2023)Urban life: a model of people and placesComputational & Mathematical Organization Theory10.1007/s10588-021-09348-729:1(20-51)Online publication date: 1-Mar-2023
  • (2022)SpatialEpi'2022 Workshop Report: The 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for EpidemiologySIGSPATIAL Special10.1145/3632268.363227714:1(28-31)Online publication date: 1-Nov-2022
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Published In

cover image SIGSPATIAL Special
SIGSPATIAL Special  Volume 12, Issue 3
November 2020
47 pages
EISSN:1946-7729
DOI:10.1145/3447994
Issue’s Table of Contents
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 January 2021
Published in SIGSPATIAL Volume 12, Issue 3

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Cited By

View all
  • (2024)Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease SpreadACM Transactions on Spatial Algorithms and Systems10.1145/366063110:2(1-22)Online publication date: 1-Jul-2024
  • (2023)Urban life: a model of people and placesComputational & Mathematical Organization Theory10.1007/s10588-021-09348-729:1(20-51)Online publication date: 1-Mar-2023
  • (2022)SpatialEpi'2022 Workshop Report: The 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for EpidemiologySIGSPATIAL Special10.1145/3632268.363227714:1(28-31)Online publication date: 1-Nov-2022
  • (2022)Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1ACM Transactions on Spatial Algorithms and Systems10.1145/35686708:3(1-5)Online publication date: 30-Oct-2022
  • (2022)Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 2ACM Transactions on Spatial Algorithms and Systems10.1145/35686698:4(1-5)Online publication date: 26-Nov-2022

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