Supplement 1. R code containing the algorithms described in this paper, as applied to the constant, known food scenario.

<h2>File List</h2><div> <a href="deb_mcmc.R">deb_mcmc.R</a> (MD5: 24027cecfc5fc9158b906a8a1d96613f)  - R code implementing the MCMC itself<br> <a href="deb_model.R">deb_model.R</a> (MD5: 67486b9d95fe61db1ddb4add2f89a614)  - R code specifying the differential equation model<br> <a href="deb_plotting.R">deb_plotting.R</a> (MD5: 4d96e43bc3758b76a81a28c2165442a3)  - functions for plotting results <br> <a href="deb_post_prior.R">deb_post_prior.R</a> (MD5: 0244a22b8855fa8401538680126a2343)  - R code with implementation of the priors and posterior distribution<br> <a href="deb_solver.R">deb_solver.R</a> (MD5: ced3a9e359d168038c5c57d5aa93f3d3)  - R code with functions to solve the differential equations <br> <a href="run_mcmc_knowncf.R">run_mcmc_knowncf.R</a> (MD5: 3b4d3e1f6931b4bff0f55c216756faa3)  - R code that calls everything above and performs the MCMC<br> <a href="deb_infer.zip">deb_infer.zip</a> (MD5: 5fb070df6d56a385f21d883f38b1972a)  - All files at once</div><h2>Description</h2><div> The R code in the file run_mcmc_knowncf.R calls the other files to first simulate data with from the model described in the paper with known parameters (implemented in deb_model.R), and then perform Bayesian inference of the parameters using a Metropolis-within-Gibbs type Markov chain Monte Carlo method (described in the Appendices and implemented in deb_mcmc.R) for the likelihood and priors implemented in deb_post_prior.R. The code requires that the <a href="http://cran.r-project.org/web/packages/PBSddesolve/index.html">PBSddesolve</a> package <a href="http://cran.r-project.org/">cran</a>) be installed. </div

Appendix A. Derivation of Δ, details of MCMC, and plots of posterior distributions for all experiments detailed in Table 3.

Derivation of Δ, details of MCMC, and plots of posterior distributions for all experiments detailed in Table 3

Supplement 1. Summary of Akaike information criteria (AIC) for the constant and Gompertz mortality models for all Monte Carlo realizations.

<h2>File List</h2><div> <p><a href="SuppTable1_MC_AIC.csv">SuppTable1_MC_AIC.csv</a> (MD5: 5337de0f2586ea2edf03a5050d4e736c)</p> </div><h2>Description</h2><div> <p>Summary of Akaike information criteria (AIC) for the constant and Gompertz mortality models for all Monte Carlo realizations. </p> </div

Appendix A. A table giving P values of pairwise exact multinomial tests of differences in the physiological age distribution of An. gambiae recorded among monthly house-catches, and figures describing predicted An. gambiae survival at age (Sa), and Scaled individual vectorial capacity (Ci), the shape of ageing, (L/Λ), and age-dependent lab and wild An. gambiae mortality and the age-distribution model estimate, shown for increasing values of EIP (A:10, B:20, C:30 days).

A table giving P values of pairwise exact multinomial tests of differences in the physiological age distribution of An. gambiae recorded among monthly house-catches, and figures describing predicted An. gambiae survival at age (Sa), and Scaled individual vectorial capacity (Ci), the shape of ageing, (L/Λ), and age-dependent lab and wild An. gambiae mortality and the age-distribution model estimate, shown for increasing values of EIP (A:10, B:20, C:30 days)

Expected costs under model misspecification.

<p>Comparison of adaptive and nonadaptive policy costs when the inference model is misspecified. Even though the static policy is based on parameter estimates obtained after a completely observed epidemic, the costs associated with adaptive management are similar.</p

Stopping times under adaptive management.

<p>(2.5,50,97.5)-% quantiles for the policy stop time at each time step over 100 simulations of the epidemic under optimal adaptive management.</p

Online parameter estimates.

<p>Final posterior density estimates for the transmission rate (A), overdispersion parameter (B), recovery rate (C), and mortality rate (D). “True” parameter values are indicated by a dot, mean posterior values are indicated by an ‘x’, and the central 95% region of the distribution is shaded. Prior densities on the same regions are shown in red.</p

Expected costs under adaptive management.

<p>(2.5,50,97.5)-% quantiles for total cost accrued over 100 simulations of the epidemic under optimal adaptive management. The mean total cost is 1665 cost units, with quantile bounds (1450,1888).</p

Expected costs under static intervention.

<p>Costs under optimal (2.5, 50, 97.5)-% quantiles for the total cost accrued over 1000 simulations of the epidemic under the optimal variable stop time strategy based on true parameter values. The mean total cost is 1652 cost units, with quantile bounds (1440,1846).</p

Simulated epidemics under adaptive management.

<p>(2.5, 59, 97.5)-% quantiles for the numbers of susceptible, infected, recovered, and vaccinated individuals over 100 simulations of the epidemic under optimal adaptive management. The mean number of vaccine units dispensed is 428, with quantile bounds (351,536).</p