TY - GEN
T1 - Agent-based modeling of COVID-19 vaccine uptake in New York State
T2 - 7th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GEOSIM 2024
AU - Yin, Fuzhen
AU - Jiang, Na
AU - Crooks, Andrew
AU - Laurian, Lucie
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/10/29
Y1 - 2024/10/29
N2 - During the COVID-19 pandemic, social media become an important hub for public discussions on vaccination. However, it is unclear how the rise of cyber space (i.e., social media) combined with traditional relational spaces (i.e., social circles), and physical space (i.e., spatial proximity) together affect the diffusion of vaccination opinions and produce different impacts on urban and rural population’s vaccination uptake. This research builds an agent-based model utilizing the Mesa framework to simulate individuals’ opinion dynamics towards COVID-19 vaccines, their vaccination uptake and the emergent vaccination rates at a macro level for New York State (NYS). By using a spatially explicit synthetic population, our model can accurately simulate the vaccination rates for NYS (mean absolute error=6.93) and for the majority of counties within it (81%). This research contributes to the modeling literature by simulating individuals vaccination behaviors which are important for disease spread and transmission studies. Our study extends geo-simulations into hybrid-space settings (i.e., physical, relational, and cyber spaces).
AB - During the COVID-19 pandemic, social media become an important hub for public discussions on vaccination. However, it is unclear how the rise of cyber space (i.e., social media) combined with traditional relational spaces (i.e., social circles), and physical space (i.e., spatial proximity) together affect the diffusion of vaccination opinions and produce different impacts on urban and rural population’s vaccination uptake. This research builds an agent-based model utilizing the Mesa framework to simulate individuals’ opinion dynamics towards COVID-19 vaccines, their vaccination uptake and the emergent vaccination rates at a macro level for New York State (NYS). By using a spatially explicit synthetic population, our model can accurately simulate the vaccination rates for NYS (mean absolute error=6.93) and for the majority of counties within it (81%). This research contributes to the modeling literature by simulating individuals vaccination behaviors which are important for disease spread and transmission studies. Our study extends geo-simulations into hybrid-space settings (i.e., physical, relational, and cyber spaces).
KW - Agent-based modeling
KW - COVID-19
KW - GIS
KW - Health informatics
KW - Hybrid spaces
KW - Information diffusion
KW - Social networks
KW - Vaccines
UR - https://www.scopus.com/pages/publications/85211592312
U2 - 10.1145/3681770.3698571
DO - 10.1145/3681770.3698571
M3 - Conference contribution
AN - SCOPUS:85211592312
T3 - GEOSIM 2024 - Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation
SP - 11
EP - 20
BT - GEOSIM 2024 - Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation
A2 - Kim, Joon-Seok
A2 - Anderson, Taylor
A2 - Shashidharan, Ashwin
A2 - Zufle, Andreas
PB - Association for Computing Machinery, Inc
Y2 - 29 October 2024
ER -