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.
As the first wave of stay-at-home orders begins to show signs of slowing COVID-19 epidemics across the United States, developing safe and efficacious exit strategies that maintain epidemic control but mitigate economic costs is an urgent priority. We use an epidemiological model of transmission dynamics to study the impact of non-pharmaceutical intervention strategies in all counties of California and across different seasonal climate regimes, and use it to understand the correlates of epidemiological disparities and the economic costs and benefits of interventions. This research will help to guide public health responses to the COVID-19 pandemic.
