Taylor Anderson
Abstract
Since the onset of the COVID-19 pandemic, researchers in the SIGSPATIAL community have utilized computational solutions to better explain, predict, and respond to infectious disease outbreaks. Using spatial computing for pandemic preparedness has also been highlighted as a major application of mobility data science [16]. At the beginning of the COVID-19 pandemic, the SIGSPATIAL community rapidly published ideas to improve our understanding of the spread of the virus in two SIGSPATIAL Special Newsletter Issues in March and July 2020 [28, 29]. These efforts led to the 1st and 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology [5, 4] (formerly called Workshop on Modeling and Understanding the Spread of COVID-19 in 2020) which has provided authors of these newsletter articles a forum to present and discuss their solutions. Including both work published at the SIGSPATIAL Special Newsletter and regular peer-reviewed submissions, this workshop included topics such as the collection of large spatiotemporal datasets [20], leveraging data mining and spatial analysis techniques to analyze and visualize such data [2, 12, 25, 21, 11, 9, 8, 3], developing predictive spatial models and simulations [6, 1, 19, 13, 24, 10, 23, 14], and employing novel technologies towards contact tracing and surveillance [17, 26].
- H. A. Aboubakr and A. Magdy. On improving toll accuracy for covid-like epidemics in underserved communities using user-generated data. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 32--35, 2020.Google Scholar
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- R. Agarwal and A. Banerjee. Infection risk score: Identifying the risk of infection propagation based on human contact. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 1--10, 2020.Google ScholarDigital Library
- J. Ajayakumar, A. Curtis, and J. Curtis. A clustering environment for real-time tracking and analysis of covid-19 case clusters. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021), pages 1--9, 2021.Google ScholarDigital Library
- T. Anderson, J.-S. Kim, A. Roess, H. Kavak, J. Yu, and A. Züfle. Spatialepi' 21 workshop report: The 2nd acm sigspatial international workshop on spatial computing for epidemiology. SIGSPATIAL Special, 13(1):to appear, 2022.Google Scholar
- T. Anderson, J. Yu, and A. Züfle. The 1st acm sigspatial international workshop on modeling and understanding the spread of covid-19. SIGSPATIAL Special, 12(3):35--40, 2021.Google ScholarDigital Library
- G. Bobashev, I. Segovia-Dominguez, Y. R. Gel, J. Rineer, S. Rhea, and H. Sui. Geospatial forecasting of covid-19 spread and risk of reaching hospital capacity. SIGSPATIAL Special, 12(2):25--32, 2020.Google ScholarDigital Library
- E. Cabana, A. Lutu, E. Frias-Martinez, and N. Laoutaris. Using mobile network data to color epidemic risk maps. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology, pages 35--44, 2022.Google ScholarDigital Library
- E. Chen and G. McKenzie. Mobility response to covid-19-related restrictions in new york city. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021), pages 10--13, 2021.Google ScholarDigital Library
- T. Elsaka, I. Afyouni, I. Hashem, and Z. Al Aghbari. Correlation analysis of spatio-temporal arabic covid-19 tweets. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021), pages 14--17, 2021.Google ScholarDigital Library
- Z. Fan, X. Song, Y. Liu, Z. Zhang, C. Yang, Q. Chen, R. Jiang, and R. Shibasaki. Human mobility based individual-level epidemic simulation platform. SIGSPATIAL Special, 12(1):34--40, 2020.Google ScholarDigital Library
- S. Gao, J. Rao, Y. Kang, Y. Liang, and J. Kruse. Mapping county-level mobility pattern changes in the united states in response to covid-19. SIGSPATIAL Special, 12(1):16--26, 2020.Google ScholarDigital Library
- A. Hohl, E. Delmelle, and M. Desjardins. Rapid detection of covid-19 clusters in the united states using a prospective space-time scan statistic: an update. SIGSPATIAL Special, 12(1):27--33, 2020.Google ScholarDigital Library
- M. Kiamari, G. Ramachandran, Q. Nguyen, E. Pereira, J. Holm, and B. Krishnamachari. Covid-19 risk estimation using a time-varying sir-model. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 36--42, 2020.Google ScholarDigital Library
- J.-S. Kim, H. Kavak, A. Züfle, and T. Anderson. Covid-19 ensemble models using representative clustering. SIGSPATIAL Special, 12(2):33--41, 2020.Google ScholarDigital Library
- G. R. Lopes, A. C. Delbem, R. F. da Silva, C. B. Júnior, S. H. V. L. de Mattos, D. Scatolini, F. Ghiglieno, and A. M. Saraiva. Multimaps: a tool for decision-making support in the analyzes of multiple epidemics. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology, pages 22--25, 2022.Google ScholarDigital Library
- M. Mokbel, M. Sakr, L. Xiong, A. Züfle, J. Almeida, T. Anderson, W. Aref, G. Andrienko, N. Andrienko, Y. Cao, et al. Mobility data science (dagstuhl seminar 22021). In Dagstuhl reports, volume 12. Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2022.Google Scholar
- M. F. Mokbel, S. Abbar, and R. Stanojevic. Contact tracing: Beyond the apps. arXiv preprint arXiv:2006.04585, 2020.Google Scholar
- T. Pechlivanoglou, G. Alix, N. Yanin, J. Li, F. Heidari, and M. Papagelis. Microscopic modeling of spatiotemporal epidemic dynamics. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology, pages 11--21, 2022.Google ScholarDigital Library
- B. Pejó and G. Biczók. Corona games: Masks, social distancing and mechanism design. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 24--31, 2020.Google ScholarDigital Library
- U. Qazi, M. Imran, and F. Ofli. Geocov19: a dataset of hundreds of millions of multilingual covid-19 tweets with location information. SIGSPATIAL Special, 12(1):6--15, 2020.Google ScholarDigital Library
- H. Samet, Y. Han, J. Kastner, and H. Wei. Using animation to visualize spatio-temporal varying covid-19 data. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 53--62, 2020.Google ScholarDigital Library
- A. Susarla, A. Liu, D. H. Thai, M. T. Le, and A. Züfle. Spatiotemporal disease case prediction using contrastive predictive coding. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology, pages 26--34, 2022.Google ScholarDigital Library
- G. Thakur, K. Sparks, A. Berres, V. Tansakul, S. Chinthavali, M. Whitehead, E. Schmidt, H. Xu, J. Fan, D. Spears, et al. Covid-19 joint pandemic modeling and analysis platform. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 43--52, 2020.Google ScholarDigital Library
- Z. Wang and O. Aydin. Sensitivity analysis for covid-19 epidemiological models within a geographic framework. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 11--14, 2020.Google ScholarDigital Library
- Z. Wang and I. F. Cruz. Analysis of the impact of covid-19 on education based on geotagged twitter. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19, pages 15--23, 2020.Google ScholarDigital Library
- L. Xiong, C. Shahabi, Y. Da, R. Ahuja, V. Hertzberg, L. Waller, X. Jiang, and A. Franklin. React: real-time contact tracing and risk monitoring using privacy-enhanced mobile tracking. SIGSPATIAL Special, 12(2):3--14, 2020.Google ScholarDigital Library
- W. Ye and S. Gao. Understanding the spatiotemporal heterogeneities in the associations between covid-19 infections and both human mobility and close contacts in the united states. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology, pages 1--9, 2022.Google ScholarDigital Library
- A. Züfle. Introduction to this special issue: Modeling and understanding the spread of covid-19: (part i). SIGSPATIAL Special, 12(1):1--2, 2020.Google ScholarDigital Library
- A. Züfle and T. Anderson. Introduction to this special issue: Modeling and understanding the spread of covid-19: (part ii). SIGSPATIAL Special, 12(2):1--2, 2020.Google ScholarDigital Library
- A. Züfle, T. Anderson, and S. Gao. Introduction to the special issue on understanding the spread of covid-19, part 1, 2022.Google Scholar
- A. Züfle, S. Gao, and T. Anderson. Introduction to the special issue on understanding the spread of covid-19, part 2, 2022.Google Scholar
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