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RAPID: Collaborative Research: COVID-19 Preparedness in Remote Fishing Communities in Rural Alaska

RAPID: Collaborative Research: COVID-19 Preparedness in Remote Fishing Communities in Rural Alaska
RAPID:合作研究:阿拉斯加农村偏远渔业社区的 COVID-19 准备情况
批准号:
2033493
负责人:
Davin Holen
金额:
$6.74万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-06-15 至 2022-05-31

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中文摘要
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英文摘要
This time-sensitive RAPID project focuses on perception of COVID-19 risk in the Bristol Bay region of rural western Alaska. Every spring, the town of Dillingham, the hub of Bristol Bay fisheries, experiences massive in-migration, as an estimated 13,000 workers arrive for the sockeye salmon fishing season. Local health care resources and access to transportation are extremely limited. Leveraging existing partnerships, project investigators will conduct surveys with local residents, decision makers, fishermen and processors to characterize risk perception, evaluate social distancing compliance, and assess perceived costs and benefits of various pandemic mitigation plans. These data will inform epidemiological modeling of possible scenarios in the region and help stakeholders understand the dynamics of COVID-19 in a transient population working in the close quarters of fishing vessels and the crowded conditions of fish processing plants. This project will contribute to the emerging body of research on the perception and effects of COVID-19 across the country, and more specifically, to understanding pandemic impacts on a key Alaska industry.Standard ethnographic methods, including qualitative key informant interviews, two online surveys, and collection of cell phone mobility data, will be employed to characterize risk perception and response and evaluate compliance with social distancing guidelines. Surveys will be designed collaboratively, and will leverage existing networks developed as part of an ongoing Navigating the New Arctic project (ICER 1927827). Surveys will be conducted at the beginning of the 2020 fishing season and again in February/March 2021. Analysis will evaluate risk perception and response by occupation, socioeconomic status, age, and sex. Epidemiological modeling will be employed to predict disease dynamics (i.e., likelihood of infection and mortality) under different scenarios; these estimates will be used to project demand for limited health care resources in the region, such as ventilators.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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