PROJECT SUMMARY Designing long-term control strategies to slow COVID-19 epidemics requires understanding the impact of non-pharmaceutical interventions, their interaction with seasonal climate, and their economic costs. We developed an epidemiological model, based on an SEIR framework, that captures non-symptomatic and symptomatic transmission and time lags between infection, hospitalization, and death, to explore the impact of non-pharmaceutical interventions. We parameterized the model using publicly available data on biological rates and times and daily COVID-19 death data from two California Bay Area counties. We estimated that the basic reproduction number, R0, ranges from 2.6-4.3, and that current shelter-in-place orders have reduced the effective reproduction number, Re, below one. In this project, we will use the model to: 1) estimate transmission parameters and the impact of social distancing on long-term control strategies for every county in California; 2) understand the interaction between climate seasonality and control interventions; and 3) study the socio-economic factors underlying epidemiological disparities across California counties, and the economic costs and benefits of intervention strategies. This research will help to guide public health responses to the COVID-19 crisis and develop safe and effective exit strategies from stay-at-home orders.