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.

First, we examined two infections with the same R0 = 0.2, but different levels of immunity in the over 20 age group.

We simulated 50 spillover events, and found the maximum likelihood estimate of R0 and S. We repeated this process for 1000 sets of outbreaks, obtaining reliable estimates of both R0 and 8 (Figs.

We used the outbreak size distribution to identify what constitutes an anomalously large outbreak for a particular R0 .

If R0 = 0.7, a chain of at least 8 cases was not unusual if some of the secondary cases are children, yet it is if the secondary cases are all adults.

2A, when R0 = 0.7 an outbreak of size 7 was anomalously large if all secondary cases were in the youngest group, but an outbreak of size 10 was not unusual if between 2—8 secondary cases were in the eldest group.

We assumed a fully susceptible population, which meant that the average number of secondary cases generated by a typical infectious individual was equal to the basic reproduction number, R0 [9].

where r0 is the baseline firing rate, 0 g m g 1 is the modulation index, and f0 is the oscillation frequency.

The magnitude of the peak power and its relation to the baseline power are dependent on multiple factors; namely r0 , T, m. Thus, a measure that is independent of subjective properties is required Which we term the modulation index (161).

However, when there is no underlying oscillation in that frequency, the result will tend to be zero, as the value of SPT(f;£ f0) approaches r0 , as shown in Eq 5.