ABSTRACT
Disease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially early or late in epidemics. Anticipating and preventing superspreading events can produce large reductions in overall transmission rates. Here, we present a compartmental (SEIR) epidemiological model framework for estimating transmission parameters from multiple imperfectly observed data streams, including reported cases, deaths, and mobile phone-based mobility that incorporates individual-level heterogeneity in transmission using previous estimates for SARS-CoV-1 and SARS-CoV-2. We parameterize the model for COVID-19 epidemic dynamics by estimating a time-varying transmission rate that incorporates the impact of non-pharmaceutical intervention strategies that change over time, in five epidemiologically distinct settings--Los Angeles and Santa Clara Counties, California; Seattle (King County), Washington; Atlanta (Dekalb and Fulton Counties), Georgia; and Miami (Miami-Dade County), Florida. We find the effective reproduction number [R]E dropped below 1 rapidly following social distancing orders in mid-March, 2020 and remained there into June in Santa Clara County and Seattle, but climbed above 1 in late May in Los Angeles, Miami, and Atlanta, and has trended upward in all locations since April. With the fitted model, we ask: how does truncating the tail of the individual-level transmission rate distribution affect epidemic dynamics and control? We find interventions that truncate the transmission rate distribution while partially relaxing social distancing are broadly effective, with impacts on epidemic growth on par with the strongest population-wide social distancing observed in April, 2020. Given that social distancing interventions will be needed to maintain epidemic control until a vaccine becomes widely available, "chopping off the tail" to reduce the probability of superspreading events presents a promising option to alleviate the need for extreme general social distancing.
ABSTRACT
As of April 5th 2020, SARS-CoV-2 has resulted in over 273,000 confirmed infections in the United States of America. Incidence continues to rise. As the epidemic threatens to overwhelm health care systems, identifying regions where the expected disease burden is likely to be high relative to the rest of the country is critical for enabling prudent and effective distribution of emergency resources. Across all global regions affected by the pandemic, an elevated risk of severe outcomes has consistently been observed in older age groups. Using age-specific mortality patterns in tandem with demographic data, we map a projection of the cumulative burden of COVID-19 and the associated cumulative burden on the healthcare system at the county-scale in the United States for a scenario in which 20% of the population of each county acquires infection. We identify regions that may be particularly impacted relative to the rest of the country, and observe a general trend that per capita disease burden and relative healthcare system demand may be highest away from major population centers.
