Highlights • Branching process outbreak models can be extended to include age-dependent factors. • The risk that an introduced case initiates an outbreak depends on that case’s age. • The effectiveness of interventions depends on the age groups that are targeted. • Combinations of contact-reducing interventions can mitigate the outbreak risk. • Effective case detection reduces the need for population-wide interventions. During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) including school closures, workplace closures and social distancing policies have been employed worldwide to reduce transmission and prevent local outbreaks. However, transmission and the effectiveness of NPIs depend strongly on age-related factors including heterogeneities in contact patterns and pathophysiology. Here, using SARS-CoV-2 as a case study, we develop a branching process model for assessing the risk that an infectious case arriving in a new location will initiate a local outbreak, accounting for the age distribution of the host population. We show that the risk of a local outbreak depends on the age of the index case, and we explore the effects of NPIs targeting individuals of different ages. Social distancing policies that reduce contacts outside of schools and workplaces and target individuals of all ages are predicted to reduce local outbreak risks substantially, whereas school closures have a more limited impact. In the scenarios considered here, when different NPIs are used in combination the risk of local outbreaks can be eliminated. We also show that heightened surveillance of infectious individuals reduces the level of NPIs required to prevent local outbreaks, particularly if enhanced surveillance of symptomatic cases is combined with efforts to find and isolate nonsymptomatic infected individuals. Our results reflect real-world experience of the COVID-19 pandemic, during which combinations of intense NPIs have reduced transmission and the risk of local outbreaks. The general modelling framework that we present can be used to estimate local outbreak risks during future epidemics of a range of pathogens, accounting fully for age-related factors.
【저자키워드】 COVID-19, SARS-CoV-2, Non-pharmaceutical interventions, Mathematical modelling, infectious disease epidemiology, Outbreak probability, Age-structured models, 【초록키워드】 social distancing, COVID-19 pandemic, Epidemics, risk, Intervention, Local, Transmission, heterogeneity, Surveillance, pathophysiology, outbreak, Effectiveness, NPIs, Factors, age, Pathogens, School, group, distribution, Combination, branching process, NPI, Contact, index case, Factor, infected individuals, individual, effort, local outbreaks, symptomatic case, Host, Effect, mitigate, Prevent, predicted, develop, include, required, reduced, can be used, introduced, reduce, initiate, Branching, eliminated, local outbreak, 【제목키워드】 risk, Intervention, Local, outbreak, Effect,