An exponential distribution of rate has density for and zero for , with survival probability . For , its memoryless property follows from
For the minimum, independence gives . Hence
The density is zero for negative . The comparison probability follows from identical continuous laws and zero tie probability; directly it is . These are competing exponential clocks.
For the last density, substitute into the normalizing integral to obtain , the Gaussian integral. Equivalently, each variable has a gamma distribution with shape and rate one. Independence permits convolution of the densities. For ,
Putting turns the integral into . Consequently
This is also the additivity of independent gamma distributions with a common rate: their shapes add to one. Density values at the single endpoint zero are immaterial.
Initially, the infection and recovery transition intensities are and . By competing exponential clocks, the first-event waiting time has distribution
The first event causes extinction precisely when it is recovery of the sole initially infectious individual. An infection would instead give two infectious individuals. The competing exponential clocks calculation therefore gives the first-event extinction probability
The competing exponential clocks calculation gives
so is exponential with rate . Also
Put . Since the variables have continuous distributions, the minimum is unique almost surely. Integrating over its possible value gives
This is the joint tail calculation for competing exponential clocks.