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MIDAS Webinar: Modeling Potential Long-Term Intervention Strategies for COVID-19

May 1, 2020
online

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DATES:
May 1, 2020
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online
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The MIDAS Webinar Series features research by MIDAS members, and is open to the public. 

Date/time: Friday May 1, 12:00 – 1:00pm, EDT

TopicModeling Potential Long-Term Intervention Strategies for COVID-19

Speaker: Dr. Erin Mordecai, Assistant Professor of Biology, Stanford University
My research focuses on the ecology of infectious disease. I am interested in how climate, species interactions, and global change drive infectious disease dynamics in humans and natural ecosystems. This research combines mathematical modeling and empirical work. I finished my PhD in 2012 at the University of California Santa Barbara in Ecology, Evolution, and Marine Biology. I then completed a 2-year NSF postdoctoral research fellowship in the Intersection of Biology and Mathematical and Physical Sciences and Engineering at the University of North Carolina at Chapel Hill and North Carolina State University. I have been at Stanford since January 2015.

Abstract: With Shelter in Place and similar social distancing orders taking effect throughout the US, and hospitalizations and deaths beginning to plateau in some regions, an urgent question is what set of exit strategies may allow some form of normal public life to resume without allowing a major epidemic resurgence. We built an SEIR model for COVID-19 epidemic dynamics that includes compartments for susceptible, exposed, infectious but pre-symptomatic, asymptomatic, mildly symptomatic, severely symptomatic, hospitalized, dead, and recovered people. The model captures key time lags between exposure, symptom onset, hospitalization, and death and pathways of transmission that include non-symptomatic infectious people, and is parameterized using values from the MIDAS dashboard. First, we investigated possible non-pharmaceutical intervention scenarios, including first- and second-waves of social distancing ranging in start date, duration, and intensity, as well as test-and-quarantine and contact tracing scenarios. By allowing the transmission parameter, beta, to vary over time, we can implement a wide range of social distancing intervention strategies. We show that even with strong social distancing (60% reduction in social contacts) for five months or more, a resurgence is highly likely when the intervention is lifted, without further interventions such as adaptive social distancing (the “lightswitch” method) or increased testing and quarantine of symptomatic people. Next, we fit the model to COVID-19 daily reported death data from the Santa Clara County and others in California. We show that Santa Clara County has an estimated R0 = 3.39 (95% CI: 2.83 – 4.22), that reducing social contacts by 71% on average would reduce R0 to 1, and that a Shelter in Place order as the only intervention would have to remain in place for approximately five months to end the epidemic (without considering new epidemics sparked by imported cases). We are now working with Bay Area, California public health departments to fit the model to county-level COVID-19 death data in order to inform intervention decision-making.

 

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