[Paper Review] Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2

Althouse Bm, Wenger Ea+5 more|arXiv (Cornell University)|May 27, 2020

COVID-19 epidemiological studies48 references71 citations

TL;DR

The paper shows that SARS-CoV-2 transmission is highly stochastic and overdispersed, dominated by superspreading events (SSEs), and that targeting SSEs can effectively control outbreaks.

ABSTRACT

SARS-CoV-2 causing 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 and misused to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, is dominated by a small number of individuals, and is driven by super-spreading events (SSEs). The distinct transmission features, such as high stochasticity under low prevalence, and the central role played by SSEs on transmission dynamics, should not 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, fish factories, cruise ships, family gatherings, parties and night clubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity that outbreak can be effectively contained with targeted interventions to eliminate SSEs. Here, we describe the potential types of SSEs, how they influence transmission, and give recommendations for control of SARS-CoV-2.

Motivation & Objective

• Highlight the role of stochasticity and heterogeneity in SARS-CoV-2 transmission.
• Characterize superspreading events and the offspring distribution of infections.
• Assess how reducing tail transmission (SSEs) affects outbreak control.
• Provide recommendations for hotspot-focused surveillance and interventions.

Proposed method

• Model offspring infections per infected individual using a negative binomial distribution with mean R0 and dispersion parameter k.
• Compare NB versus Poisson offspring distributions in stochastic branching processes.
• Simulate outbreak trajectories across population sizes to illustrate extinction probabilities and explosive growth.
• Analyze how truncating the tail of the NB distribution (cutting large SSEs) impacts R_eff and outbreak risk.
• Discuss types and settings of SSEs and potential targeted interventions.

Experimental results

Research questions

• RQ1 What is the impact of transmission heterogeneity and SSEs on early outbreak dynamics?
• RQ2 How does an overdispersed NB offspring distribution alter extinction probabilities and apparent growth compared to Poisson?
• RQ3 Can targeted reduction of tail transmission (SSEs) meaningfully reduce R_eff and outbreak risk?
• RQ4 Where do SSEs occur (settings) and how can interventions mitigate their impact?

Key findings

• SSEs drive transmission, leading to high stochasticity at low prevalence and explosive growth when outbreaks take off.
• SARS-CoV-2 shows overdispersion with a small k value, causing a long tail of high secondary infections that dominates transmission in some outbreaks.
• Eliminating or truncating large SSEs can substantially reduce R_eff and the probability of large outbreaks, often more effectively than uniform population-wide measures.
• Hotspots and indoor, poorly ventilated settings frequently serve as SSEs, presenting targets for surveillance and intervention.
• Early outbreak dynamics exhibit higher randomness and extinction risk, while established outbreaks proceed toward exponential growth similar to Poisson models once they pass SSE thresholds.
• Targeted interventions that cut the tail (reduce large secondary infections) can render the transmission chain more likely to collapse.