民族
心理干预
2019年冠状病毒病(COVID-19)
人口普查
人口
种族(生物学)
公共卫生
检疫
地理
干预(咨询)
计算机科学
疾病
医学
环境卫生
政治学
社会学
传染病(医学专业)
护理部
性别研究
病理
法学
作者
Erik Rosenstrom,Julie S. Ivy,María E. Mayorga,Julie Swann
出处
期刊:Epidemics
[Elsevier]
日期:2024-03-01
卷期号:46: 100752-100752
被引量:3
标识
DOI:10.1016/j.epidem.2024.100752
摘要
We document the evolution and use of the stochastic agent-based COVID-19 SIMu-lation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.
科研通智能强力驱动
Strongly Powered by AbleSci AI