Community-Wide Interventions Minimize the Opportunity for Superspreading of Sars-Cov-2

Abstract

Background & aims of study

High overdispersion in individual transmissibility and the resulting super-spreading events are believed to drive the SARS-CoV-2 transmission. Estimating these characteristics often requires detailed contact-tracing data, which is challenging to obtain during the pandemic. It is important to develop a simple yet flexible method that uses routinely reported data of confirmed cases to infer key characteristics of individual transmissibility and assess the impact of control measures.

Methods & results

We develop a likelihood-free inference framework using the approximate Bayesian computation-Sequential Monte Carlo approach, which allows us to evaluate the posterior distribution without using complex likelihood functions. We performed the inference by optimizing the similarity between simulated and observed time series of community cases and characteristics of transmission chains. We applied this framework to the COVID-19 case surveillance data from regional economic hubs including Singapore, Hong Kong, and Beijing. We show the ability of our framework in characterizing the changing dynamics of individual transmissibility, reconstructing transmission chains and clusters, and providing more accurate impact assessments of control measures.

Implications

Our analyses suggest that the super-spreading of SARS-CoV-2 in the community is driven by large transmission clusters with many generations of infections instead of super-spreading events alone. Community-wide interventions can help minimize the super-spreading of SARS-CoV-2.

Note: This conference abstract was presented at the 9th International Conference on Infectious Disease Dynamics organized by the journal Epidemics. This abstract has not been screened by SSRN for potential for public harm and should not be used to inform any clinical decision making. No competing interests or funding statements have been declared.