Year: 2023
Type: article
Abstract: Disasters like the COVID-19 pandemic highlight the importance and difficulties of using data science to craft and implement effective disaster responses. While data science solutions are vital to the effectiveness and, importantly, the scaling of any frontline disaster response, lack of accessibility, training, and flexibility of these solutions can be extremely problematic. Here we outline our vision of Frontline Data Science and reflect on our successes and failures as frontline data scientists who pivoted from traditional research roles at a midsized nonmedical institution to build tools directly for the pandemic effort. We provide a summary of our experiences in building an interdisciplinary team founded on, and structured by, data pipelines and data scientists’ skills. We present ‘lessons learned’ from our experience building a COVID-19 Monitoring and Assessment Program (MAP), which provides SARS-CoV-2 information and testing to our university and large parts of our state. We further present ‘lessons learned’ from expanding disaster responses to traditionally unreached and disproportionately impacted groups. Finally, we provide a set of ‘principles for success’ that we hope will guide future efforts by data scientists wishing to immediately impact our society and provide information to organizations curious as to how data scientists can contribute to emergency response and disaster recovery.
Source: Harvard Data Science Review
Authors: Emily A. Beck, Hannah Tavalire, Jake Searcy
Institution: University of Oregon
Language: English
Subfield: Emergency Medical Services
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4367323043