different climate change scenarios. The model in its current form already incorporates features (climate suitability index) that allow for such analyses. In this paper we have described the pilot application of the MMM approach to the aggregated SSA region. Further applications of the MMM model should focus on country-specific analyses. This requires more detailed data about the malaria context, environmental conditions and social factors. Given the availability of data, the MMM can easily be applied to the national level where it is possible to provide much more detailed and specific decision support in the assessment and evaluation of different malaria control interventions. Supporting Information Figure S1 Aggregated representation of the MMM model. Aggregated representation of the structure of the Malaria Management Model. The structure is composed of five sectors: population, production, education, health, and malaria. All sectors interact with each other and the arrows between the sectors describe the directions of these interactions. The malaria sector is itself split into five subsectors. (TIF) Figure S2 Malaria subsectors describing transmission, IVM and case management. Overview of the malaria subsectors in the malaria management model. Variables in italics are variables that enter the malaria subsectors from the four socio-economic sectors. The transmission subsector describes the process during which the vulnerable population can actually be infected with and die from malaria. Infections depend on the coverage with IVM interventions and the model assumes that no infections occur when the entire vulnerable population is effectively covered by IVM interventions (subsector IVM interventions). Malaria deaths can be prevented by covering the infected population with effective treatment measures (subsector case management). (TIF) Figure S3 Baseline simulations for total estimated malaria cases (solid grey line) and population fraction affected by malaria (solid black line). Baseline simulations for malaria cases. Total estimated malaria cases (grey line, million people) increased steadily until the 2000s (with oscillations that follow oscillations in the climate suitability index) and have since experienced a considerable decline that can be attributed to large investments made as a consequence renewed interest in malaria eradication. Total malaria cases are projected to stabilize and decline as a consequence of increases in IVM coverage that come with increases in GDP as well as improvements in education and health. The population fraction affected by malaria (black line; i.e., the proportion of the total population affected by malaria) is projected to decline even more as total malaria cases stabilize while the total population experiences further growth in the baseline projections for the future. (TIF) Figure S4 Average yearly costs for a continued use of DDT for IRS versus a rapid phase out of DDT for IRS. Average yearly costs for a continued use of DDT for IRS versus its rapid phase out. When only direct costs such as the price per IVM intervention per year are considered the average necessary yearly expenditure for eliminating malaria by 2035 is lower for the current combination of IVM interventions using DDT for IRS (2035-CurrentMix) than for the same combination but with a rapid phase out of DDT for IRS (2035-NoDDT). Values in DDT versus Non-DDT Malaria Control brackets describe the additional malaria expenditures as percent- age of GDP required to eliminate malaria by 2035. (TIF) Figure S5 Costs and benefits of rapidly phasing out DDT for IRS. Costs and benefits of rapidly phasing out DDT for IRS. The direct costs of a rapid phase out of DDT are higher than the direct costs for the continued use of DDT for IRS (additional costs of the 2035-NoDDT policy; dark grey bar). The value of this difference is equivalent to something between 1% and 5% of the total agricultural exports outside sub Saharan Africa (light grey bars). This comparison can inform an assessment of the risk of losing agricultural exports because it provides an estimate of how the additional direct costs of rapidly phasing out DDT relate to the value of agricultural exports outside SSA. (TIF) Figure S6 Comparison of model results for DDT on dwellings for baseline (black solid line), 2035-current mix (dashed grey line) and 2035-NoDDT simulations. DDT concentrations on dwellings in three different scenarios. The most direct health threat of DDT in IRS results from the concentration of DDT on dwellings’ walls. In the case of a continued use of DDT for IRS (2035-CurrentMix simulation), DDT concentrations increase steadily and considerably above the baseline values, where no malaria elimination is reached by 2035. In the case of a rapid phase out of DDT for IRS (2035-NoDDT simulation), DDT concentrations on dwellings decrease and approach levels close to zero after an extended adaptation delay. (TIF) Figure S7 Average yearly costs for eliminating malaria with the different combinations of IVM interventions and for the two target years. Average yearly costs for eliminating malaria with the different combinations of IVM interventions. The average yearly costs for eliminating malaria are higher in the case of 2025 as target elimination year. For both target elimination years, IVM interventions with a strong focus on ITN are the least costly combination of IVM interventions and interventions with a strong focus on IRS are the most expensive. This can be explained by the size of the capital component in the different combinations. Higher capital components require higher initial investments but then only need to be maintained. Recurrent expenditure as in the case of IRS, on the other hand, is equally high every year. Values in brackets describe the additional malaria expenditures as percentage of GDP required to eliminate malaria by 2025 or 2035, respectively. (TIF) Table S1 Parameter values, assumptions and data sources for IVM interventions. Notes: The number of people effectively covered by IVM interventions can be calculated as follows: - In the case of ITN: malaria prevention expenditure for ITN divided by the unit costs (the cost of one net) and multiplied by coverage (the number of people covered by one net). This term is adjusted for the effectiveness of the bed nets which depends in a linear way on the average years of schooling. 100% effectiveness would require that the average adult person has completed nine years of schooling. The effectiveness in 2010 is estimated to be 58%. - In the case of IRS: malaria prevention expenditure for IRS divided by unit costs and multiplied by effectiveness. As the current evidence is insufficient to quantify properly the effect of IRS in high transmission settings [47], we subject the cost- effectiveness assumptions to sensitivity analysis. - In the case of EM: malaria prevention expenditure for EM divided by the unit costs (costs per square kilometer) and multiplied by coverage PLoS ONE | www.plosone.org 10 November 2011 | Volume 6 | Issue 11 | e27771