Derek Ouyang

All Publications

• Science Translation During the COVID-19 Pandemic: An Academic-Public Health Partnership to Assess Capacity Limits in California. American journal of public health Maldonado, P., Peng, A., Ouyang, D., Suckale, J., Ho, D. E. 1800; 112 (2): 308-315

  Abstract

  On the basis of an extensive academic-public health partnership around COVID-19 response, we illustrate the challenge of science-policy translation by examining one of the most common nonpharmaceutical interventions: capacity limits. We study the implementation of a 20% capacity limit in retail facilities in the California Bay Area. Through a difference-in-differences analysis, we show that the intervention caused no material reduction in visits, using the same large-scale mobile device data on human movements (mobility data) originally used in the academic literature to support such limits. We show that the lack of effectiveness stems from a mismatch between the academic metric of capacity relative to peak visits and the policy metric of capacity relative to building code. The disconnect in metrics is amplified by mobility data losing predictive power after the early months of the pandemic, weakening the policy relevance of mobility-based interventions. Nonetheless, the data suggest that a better-grounded rationale for capacity limits is to reduce risk specifically during peak hours. To enhance the connection between science, policy, and public health in future times of crisis, we spell out 3 strategies: living models, coproduction, and shared metrics. (Am J Public Health. 2022;112(2):308-315. https://doi.org/10.2105/AJPH.2021.306576).

  View details for DOI 10.2105/AJPH.2021.306576

  View details for PubMedID 35080959

• A language-matching model to improve equity and efficiency of COVID-19 contact tracing. Proceedings of the National Academy of Sciences of the United States of America Lu, L., Anderson, B., Ha, R., D'Agostino, A., Rudman, S. L., Ouyang, D., Ho, D. E. 2021; 118 (43)

  Abstract

  Contact tracing is a pillar of COVID-19 response, but language access and equity have posed major obstacles. COVID-19 has disproportionately affected minority communities with many non-English-speaking members. Language discordance can increase processing times and hamper the trust building necessary for effective contact tracing. We demonstrate how matching predicted patient language with contact tracer language can enhance contact tracing. First, we show how to use machine learning to combine information from sparse laboratory reports with richer census data to predict the language of an incoming case. Second, we embed this method in the highly demanding environment of actual contact tracing with high volumes of cases in Santa Clara County, CA. Third, we evaluate this language-matching intervention in a randomized controlled trial. We show that this low-touch intervention results in 1) significant time savings, shortening the time from opening of cases to completion of the initial interview by nearly 14 h and increasing same-day completion by 12%, and 2) improved engagement, reducing the refusal to interview by 4%. These findings have important implications for reducing social disparities in COVID-19; improving equity in healthcare access; and, more broadly, leveling language differences in public services.

  View details for DOI 10.1073/pnas.2109443118

  View details for PubMedID 34686604

• Rising Seas, Rising Inequity? Communities at Risk in the San Francisco Bay Area and Implications for Adaptation Policy Earth's Future Bick, I., Santiago Tate, A. F., Serafin, K. A., Miltenberger, A., Ayansi, I., Evans, M., Ortolano, L., Ouyang, D., Suckale, J. 2021; 9 (7)

  View details for DOI 10.1029/2020EF001963

• When floods hit the road: Resilience to flood-related traffic disruption in the San Francisco Bay Area and beyond. Science advances Kasmalkar, I. G., Serafin, K. A., Miao, Y., Bick, I. A., Ortolano, L., Ouyang, D., Suckale, J. 2020; 6 (32): eaba2423

  Abstract

  As sea level rises, urban traffic networks in low-lying coastal areas face increasing risks of flood disruptions. Closure of flooded roads causes employee absences and delays, creating cascading impacts to communities. We integrate a traffic model with flood maps that represent potential combinations of storm surges, tides, seasonal cycles, interannual anomalies driven by large-scale climate variability such as the El Nino Southern Oscillation, and sea level rise. When identifying inundated roads, we propose corrections for potential biases arising from model integration. Our results for the San Francisco Bay Area show that employee absences are limited to the homes and workplaces within the areas of inundation, while delays propagate far inland. Communities with limited availability of alternate roads experience long delays irrespective of their proximity to the areas of inundation. We show that metric reach, a measure of road network density, is a better proxy for delays than flood exposure.

  View details for DOI 10.1126/sciadv.aba2423

  View details for PubMedID 32821823