Superspreading events in the transmission dynamics of SARS-CoV-2: Opportunities for interventions and control

Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) disease, has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used—appropriately and less appropriately—to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, often dominated by a small number of individuals, and heavily influenced by superspreading events (SSEs). The distinct transmission features of SARS-CoV-2, e.g., high stochasticity under low prevalence (as compared to other pathogens, such as influenza), and the central role played by SSEs on transmission dynamics cannot be overlooked. Many explosive SSEs have occurred in indoor settings, stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, produce processing facilities, fish factories, cruise ships, family gatherings, parties, and nightclubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity to effectively contain outbreaks with targeted interventions to eliminate SSEs. Here, we describe the different types of SSEs, how they influence transmission, empirical evidence for their role in the COVID-19 pandemic, and give recommendations for control of SARS-CoV-2.

Awareness-driven behavior changes can shift the shape of epidemics away from peaks and toward plateaus, shoulders, and oscillations

“…First, epidemic outbreaks include stochasticity of multiple kinds. Fluctuations could arise endogenously via process noise (especially at low levels of disease) or exogenously via time-varying parameters, Moreover, given the evidence for clustered transmission and superspreading events ( 18 – 21 ), extensions of the present model framework should explicitly account for awareness-driven behavior associated with risky gatherings ( 22 , 23 ). Next, the link between severity and behavior change depends on reporting of disease outcomes.…”

The role of asymptomatic and pre-symptomatic infection in SARS-CoV-2 transmission—a living systematic review

“…Given the importance of transmission heterogeneity in propagating the pandemic, it is important that we learn about the various factors that contribute to transmission. According to modelling and contact tracing studies, around 80% of secondary infections can be linked to 20% of cases which distinguishes SARS-CoV-2 from seasonal influenza, although a similar pattern was also observed in SARS-CoV and MERS-CoV [ [82] , [83] , [84] ]. While there are multiple factors (environmental factors, contact patterns and socioeconomic inequalities) that contribute to this heterogeneity, some evidence starting to emerge about the influence of individual’s infectiousness on transmission dynamics.…”

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

“…The second, the topics and references. Category Topic and references Change to medical practice Mental health care [254] , [257] , [261] , [273] , [274] , [275] ; Emergency care [276] , [277] ; Substance abuse [253] , [263] , [278] , [279] , [280] ; Oncological care [264] , [281] Health care practices [255] , [259] , [262] , [282] , [283] , [284] ; Pediatric care [283] , [285] Other care [256] , [260] , [268] , [286] , [287] , [288] Modeling Modeling approaches [28] , [265] , [289] , [290] , [291] , [292] , [293] ; Transmission dynamics [294] , [295] , [296] , [297] , [298] Mobile phone data [29] ; estimation [258] Effect of NPIs Effects on: children ...…”

“…Several comments and/or perspectives focus on different aspects of modeling COVID-19 dynamics and NPIs. In particular, we find articles discussing epidemic modeling efforts [28] , [265] , [289] , [290] , [291] , [292] , [293] , analyzing knowns and unknowns of transmission dynamics [294] , [295] , [296] , [297] , [298] , presenting opportunities to model and control the spreading of COVID-19 enabled by novel data streams such as mobile phone data [29] , and discussing the challenges in estimation [258] .…”

“…The importance of understanding and capturing super-spreading events to accurately model and suppress COVID-19 is also matter of discussion in Ref. [296] . Ref.…”