NSF Award # OPP-2032790

Pandemic Impacts on Fishing Communities

COVID-19 Preparedness in Remote Fishing Communities in Rural Alaska: An NSF-Funded Project ($200,000)

Team members: Lance Howe (PI), Guangqing Chi (PI), Davin Holen (PI), Kevin Berry, Hannah Hennighausen, Luke Smith

5
Continents being analyzed
10+
Long-term, active projects
200+
Publications

This time-sensitive RAPID project focuses on the 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 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 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 contributes 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.

Surveys are designed collaboratively, and leverage existing networks developed as part of the POLARIS project. Standard ethnographic methods, including qualitative key informant interviews, two online surveys, and collection of cell phone mobility data, are employed to characterize risk perception and response and evaluate compliance with social distancing guidelines. Surveys were conducted at the beginning of the 2020 fishing season and again in February/March 2021. Analysis evaluates risk perception and response by occupation, socioeconomic status, age, and sex. Epidemiological modeling is employed to predict disease dynamics (i.e., likelihood of infection and mortality) under different scenarios; these estimates are used to project demand for limited health care resources in the region, such as ventilators.

Results have been distributed directly back to these local stakeholders to aid in developing planning scenarios. Survey results from participants in the commercial and subsistence fisheries can inform future cost-benefit analysis for communities facing similar decisions.