Chopping the tail: How preventing superspreading can help to maintain COVID-19 control

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 stochastic 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 that the effective reproduction number (RE) dropped below 1 rapidly in all five locations following social distancing orders in mid-March, 2020, but that gradually increasing mobility starting around mid-April led to an REonce again above 1 in late May (Los Angeles, Miami, and Atlanta) or early June (Santa Clara County and Seattle). However, we find that increased social distancing starting in mid-July in response to epidemic resurgence once again dropped REbelow 1 in all locations by August 14. We next used the fitted model to ask: how does truncating the individual-level transmission rate distribution (which removes periods of time with especially high individual transmission rates and thus models superspreading events) affect epidemic dynamics and control? We find that 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 licence: CC BY-NC

1. 6.

   Plug-and-play inference for disease dynamics: measles in large and small populations as a case study · 2009

2. 18.

   Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2) · 2020

3. 19.

   Identifying and Interrupting Superspreading Events—Implications for Control of Severe Acute Respiratory Syndrome Coronavirus 2 · 2020

4. 21.

   Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing · 2020

5. 24.
   Corrected

   Estimates of the severity of coronavirus disease 2019: a model-based analysis · 2020

6. 25.

   High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2 · 2020

7. 26.

   COVID-19 Outbreak Associated with Air Conditioning in Restaurant, Guangzhou, China, 2020 · 2020

8. 27.

   Presymptomatic Transmission of SARS-CoV-2 — Singapore, January 23–March 16, 2020 · 2020

9. 30.

   Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China · 2020

10. 32.

    Aerosol and Surface Distribution of Severe Acute Respiratory Syndrome Coronavirus 2 in Hospital Wards, Wuhan, China, 2020 · 2020

11. 39.

    The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission · 2020

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