| Application to real outbreaks | Our estimate of R for MERS-CoV was 0.73 (0.54—0.96), whereas in a single-type branching process model R = 0.63 (0.49—0.85). |
| Discussion | Using a multi-type branching process , we developed an inference framework to make better use of age-structured outbreak size data. |
| Discussion | In a single-type branching process framework, the threshold is a single number: the total size of the outbreak [31, 16]. |
| Estimating transmissibility and pre-existing immunity | We simulated outbreaks using a multi-type branching process with two groups, then used the outbreak size distribution to infer R0 and relative immunity in older individuals. |
| Estimating transmissibility and pre-existing immunity | We compared these values with estimates from an inference framework based on a single-type branching process [15, 16, 17, 18]. |
| Estimating transmissibility and pre-existing immunity | This bias is the result of our assumption that introductions occur randomly across the susceptible population, and illustrates an important caveat to inference of R from the mean outbreak size in a single-type branching process model. |
| Introduction | However, existing techniques for estimating transmission potential from outbreak size data generally represent transmission in the host population using single-type branching process [15, 16, 17, 18]. |
| Introduction | We made use of this observation by developing a novel age-structured model of stuttering transmission chains, which combined reported social contact data with a multi-type branching process [23, 24]. |
| Offspring distribution | We used a multi-type branching process to model secondary infections (see Text 81 for details). |
| Supporting Information | Estimates of R0 and relative susceptibility, S, when simulation model is a multi-type branching process with 15 age groups. |