The 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19 workshop (COVID'2020) will focus on all aspects of modeling, simulating, mining, and understanding the spatial processes and patterns of the spread of COVID-19 and other infectious diseases. This cross-disciplinary workshop is a forum to bring together researchers in the SIGSPATIAL community as well as researchers in epidemiology. Also, this workshop is of interest to everyone who works with infectious disease data and models (not necessarily COVID19).
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Infection Risk Score: Identifying the risk of infection propagation based on human contact
A wide range of approaches have been applied to manage the spread of global pandemic events such as COVID-19, which have met with varying degrees of success. Given the large-scale social and economic impact coupled with the increasing time span of the ...
Sensitivity Analysis for COVID-19 Epidemiological Models within a Geographic Framework
Spatial sciences and geography have been integral to the modeling of and communicating information pertaining to the COVID-19 pandemic. Epidemiological models are being used within a geographic context to map the spread of the novel SARS-CoV-2 virus and ...
Analysis of the Impact of COVID-19 on Education Based on Geotagged Twitter
More than 150 colleges have reported hundreds of COVID-19 confirmed cases over all the states as the campuses have reopened and the schools have resumed in-person classes, after switching overnight to online teaching in the spring. We conduct a large ...
Corona Games: Masks, Social Distancing and Mechanism Design
Pandemic response is a complex affair. Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing and contact tracing. We argue that some non-trivial factors behind the varying ...
On Improving Toll Accuracy for COVID-like Epidemics in Underserved Communities Using User-generated Data
This paper envisions using user-generated data as a cheap way to improve accuracy of epidemic tolls in underserved communities. The global widespread of COVID-19 pandemic has imposed several unprecedented challenges. One of these challenges is ...
COVID-19 Risk Estimation using a Time-varying SIR-model
Policy-makers require data-driven tools to assess the spread of COVID-19 and inform the public of their risk of infection on an ongoing basis. We propose a rigorous hybrid model-and-data-driven approach to risk scoring based on a time-varying SIR ...
COVID-19 Joint Pandemic Modeling and Analysis Platform
- Gautam Thakur,
- Kevin Sparks,
- Anne Berres,
- Varisara Tansakul,
- Supriya Chinthavali,
- Matthew Whitehead,
- Erik Schmidt,
- Haowen Xu,
- Junchuan Fan,
- Dustin Spears,
- Elton Cranfill
The non-pharmaceutical intervention to reduce the impact and spread of COVID-19 requires the development of policies and guidance through a collaborative effort among government, academia, medicine, and citizens. To operationalize this effort, we have ...
Using Animation to Visualize Spatio-Temporal Varying COVID-19 Data
CoronaViz (http://coronaviz.umiacs.io) is a research prototype developed by us to enable the dynamic map visualization of COVID-19 related variables including the number of confirmed cases, active cases, recoveries, and deaths all on a daily basis from ...
- Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19