Limitations and Future Directions
EVENTS, EVENT
Our study had limitations. Improvements to our site proposal algorithm could enhance usability. Several sites were considered untenable due to resource constraints (e.g., no bathroom access, not suitable for high traffic) that were not considered in the model. Our current model considered only average drive time and neglected other travel modalities such as public transit that may be particularly important in other populations particularly in urban areas (Salonen & Toivonen, Our FLP algorithm also neglects the time dependence of distances which may change throughout the day as people travel to work, school, or other locations. A potential consequence was the addition of several commercial sites at place of employment by community partners as seen in Fig. While our community partners were able to leverage the candidate sites to select proximal sites, site selection could be improved to reduce the number of site relocations required. Our model proposed sites that served very small outlier communities, and these sites were often deemed untenable because the cost and effort of running an event was considered a poor use of resources because of a low number of potential participants. This can be addressed with a common formulation of FLP algorithms that includes facility startup costs in the objective. Importantly, our model challenged status-quo assumptions during the site selection process. A key area of future research is whether algorithms should try to replicate existing decision processes or if deliberately championing different values during the site selection processes will lead to more thoughtful decisions.We found a strong correlation between increased testing and reduced drive time. However, it is important to note that this observational analysis does not provide causal proof and does not rule out alternative explanations such as community-level characteristics. For example, individuals do not sort themselves into rural versus urban areas of residence at random, and care-seeking behavior may differ geographically based on a variety of unobserved reasons that could non-causally correlate drivetime with the likelihood of a participant seeking a test. This study did not collect individual level data on all attendees at the testing events. In addition, Latinx communities vary significantly across factors such as country of origin and primary language, which may limit generalizations drawn from this data. Demographic variation in age and gender across testing participants was also not considered. While we do not have data available on the testing team, our study was intentional about hiring individuals who were bilingual in English and Spanish and who identified as Latinx. Anecdotally, many of our testing team members were students or recently graduated from our institution, and we infer that they were mostly in their twenties.Limitations notwithstanding, there are important contributions from our work that necessitate future research. We observed an association between reduced travel time and an increase in Latinx community members engaging in SARS-CoV-2 testing. While this provides evidence that algorithms that optimize based on drive time could be a useful tool for reaching focal populations, it is still un-proven if utilization of algorithmic proposals in collaboration with community partners resulted in significantly better optimizations compared to proposals generated by a solely community-driven process. Rigorous testing of these algorithmic proposals and their benefit is an important future direction. Doing so requires research methodologies that are designed from the outset to rigorously compare alternative optimization algorithms that are scientifically based, such as randomized comparative effectiveness or within a multiphase optimization strategy (MOST) design.