In a first set of optimizations, we define transmission investment to be a constant (c(t)=x, for all t) and determine the optimal time‐invariant conversion rate. Second, following Greischar et al. (2016a), we use cubic splines for the optimization of time‐varying conversion strategies, implemented in R with the “splines” package. Cubic splines require only four parameters to specify but allow considerable flexibility in the pattern of conversion over a 20‐day infection, and more complicated splines yield minimal fitness gains (Greischar et al., 2016a). Conversion rates must be constrained to vary between zero and one, so we take the complimentary log‐log of the value specified by the spline, that is, c(t)=exp(−exp(splinevalueattimet)). The starting values of the variables and the assumed value for each of the model parameters are given in Table 1, and each optimization is initiated by setting all spline parameters to an arbitrary starting guess of 0.5. Although no numerical optimization routine can guarantee finding a globally optimal solution, we sought to substantiate our findings by testing, for a given environment (i.e., drug dose), whether the putative optimal strategy for that environment outperformed the putative optimal strategies from other environments.