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 Niño 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.

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Significance Language discordance has been shown to contribute to social disparities in healthcare. Contact tracing is essential to combating COVID-19, but language differences between contact tracers and patients have hindered its efficacy. We demonstrate a general method for leveraging machine learning and administrative data to maximize the impact of bilingual contact tracers and level language differences. We evaluate in a randomized controlled trial the impact of language matching on high-volume contact tracing in Santa Clara County, CA, and show that it reduces time spent and improves engagement with contact tracers. These results illustrate the advantages of utilizing bilingual personnel over third-party interpreters in improving social services.

Natural hazards have always shaped our planet, but global climate change alters the nature of the risk they pose. Climate change is expected to both intensify existing hazards and create new, previously unknown types of hazards (Oppenheimer et al., 2014). Even when the nature of the new threat is known, such as in the case of rising sea level, there is deep uncertainty associated with the overall magnitude of the hazard

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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 )

COVID-19 exposed and exacerbated health disparities, and a core challenge has been how to adapt pandemic response and public health in light of these disproportionate health burdens. Responding to this challenge, the County of Santa Clara Public Health Department designed a model of “high-touch” contact tracing that integrated social services with disease investigation, providing continued support and resource linkage for clients from structurally vulnerable communities. We report results from a cluster randomized trial of 5,430 cases from February to May 2021 to assess the ability of high-touch contact tracing to aid with isolation and quarantine. Using individual-level data on resource referral and uptake outcomes, we find that the intervention, randomized assignment to the high-touch program, increased the referral rate to social services by 8.4% (95% confidence interval, 0.8%-15.9%) and the uptake rate by 4.9% (-0.2%-10.0%), with the most pronounced increases in referrals and uptake of food assistance. These findings demonstrate that social services can be effectively combined with contact tracing to better promote health equity, demonstrating a novel path for the future of public health.

COVID-19 success stories from countries using contact tracing as an intervention tool for the pandemic have motivated US counties to pilot opt-in contact tracing applications. Contact tracing involves identifying individuals who came into physical contact with infected individuals. Recent studies show the effectiveness of contact tracing scales with the number of people using the applications. We hypothesize that the effectiveness of contact tracing also depends on the occupation of the user with a large-scale adoption in certain at risk occupations being particularly valuable for identifying emerging outbreaks. We build on an agent-based epidemiological simulator that resolves spatiotemporal dynamics to model San Francisco, CA, USA.Census, OpenStreetMap, SafeGraph, and Bureau of Labor Statistics data inform the agent dynamics and site characteristics in our simulator. We test different agent occupations that create the contact network, e.g. educators, office workers, restaurant workers, and grocery workers. We use Bayesian Optimization to determine transmission rates in San Francisco, which we validate with transmission rate studies that were recently conducted for COVID-19 in restaurants, homes and grocery stores. Our sensitivity analysis of different sights show that the practices that impact the transmission rate at schools have the greatest impact on the infection rate in San Francisco. The addition of occupation dynamics into our simulator increases the spreading rate of the virus, because each occupation has a different impact on the contact network of a city. We quantify the positive benefits of contact tracing adopted by at risk occupation workers on the community and distinguish the specific benefits on at risk occupation workers. We classify to which degree a certain occupation is at risk by quantifying the impact (a) the number of unique contacts and (b) the total number of contacts an individual has for any given work day on the virus spreading rate. We also attempt to constrain if, when, and for how long certain sites should be shut down once exposed to positive cases. Through our research, we are able to identify the occupations, like educators, that are at greatest risk. We use common geophysical data analysis techniques to bring a different set of insights into COVID-19 and policy research.