Abstract:
To slow the spread of COVID-19, most countries implemented stay-at-home orders, social
distancing, and other nonpharmaceutical mitigation strategies. To understand individual
preferences for mitigation strategies, we piloted a web-based Respondent Driven Sampling
(RDS) approach to recruit participants from four universities in three countries to complete
a computer-based Discrete Choice Experiment (DCE). Use of these methods, in combina tion, can serve to increase the external validity of a study by enabling recruitment of popu lations underrepresented in sampling frames, thus allowing preference results to be more
generalizable to targeted subpopulations. A total of 99 students or staf members were
invited to complete the survey, of which 72% started the survey (n=71). Sixty-three partic ipants (89% of starters) completed all tasks in the DCE. A rank-ordered mixed logit model
was used to estimate preferences for COVID-19 nonpharmaceutical mitigation strategies.
The model estimates indicated that participants preferred mitigation strategies that resulted
in lower COVID-19 risk (i.e. sheltering-in-place more days a week), fnancial compensa tion from the government, fewer health (mental and physical) problems, and fewer fnan cial problems. The high response rate and survey engagement provide proof of concept that
RDS and DCE can be implemented as web-based applications, with the potential for scale
up to produce nationally-representative preference estimates.