COVID-19 transmission in a resource dependent community with heterogeneous populations: An agent-based modeling approach

被引:1
|
作者
Wood, Aaron D. [2 ]
Berry, Kevin [1 ]
机构
[1] Univ Alaska Anchorage, Dept Econ, 3211 Providence Dr, Anchorage, AK 99508 USA
[2] Univ Tampa, John H Sykes Coll Business, Dept Econ, 401 W Kennedy Blvd,Box O, Tampa, FL 33606 USA
关键词
Agent-based modeling; Resource dependent community; Fishery; Heterogeneous populations; Virus transmission; Public health; Public policy;
D O I
10.1016/j.ehb.2023.101314
中图分类号
F [经济];
学科分类号
02 ;
摘要
Outbreaks of COVID-19 in crowded work locations led to mass infection events during the pandemic that stressed health capacity in rural communities. This led to disparate responses - either isolating and restricting workers to facilities and potentially amplifying spread between them, more intense community wide restrictions, or an acceptance of higher disease spread. An extreme case is the salmon fishery in Bristol Bay, Alaska, where fish-ermen, factory workers, and residents all interact during the summer fishing season. During the pandemic, policy measures were debated, including community mask mandates, restricting workers to their boats and factories, and even closing the valuable seasonal fishery. We develop an agent-based SIR model (ABM) to examine COVID-19 transmission in a resource-dependent community populated by distinct subgroups. The model includes a virus spreading within and between three heterogenous populations who interact with other members of their type in their home location, and with different types of agents when out in the community. We simulate various non-pharmaceutical interventions and vaccination rates across these groups. Results demonstrate the efficacy of non-pharmaceutical interventions and vaccinations, as well as tradeoffs between duration and intensity and tradeoffs between groups impacted by the outbreak. This ABM demonstrates the impact of public policy mechanisms on health outcomes in resource-dependent communities with distinct populations.
引用
收藏
页数:11
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