of malaria diffusion in sub Saharan Africa, the implications for socio-economic development, and it compares the cost and effectiveness of alternative malaria control strategies in the long run. Our simulations showed that the direct economic costs of a continued use of DDT for IRS are lower than for a rapid phase out of DDT for IRS. However, we were also able to quantify indications about external costs of DDT and economic risks that can be avoided with a rapid phase out of DDT for IRS. Simulation runs with alternative combinations of IVM interven- tions also demonstrated that comparatively less costly combina- tions exist to the current combination of IVM interventions or a combination with an even stronger focus on IRS. The use of malaria models has been advocated repeatedly as tools for strategic planning, development of management plans, impact assessment, technical feasibility assessments, and opera- tional feasibility assessments [46]. Our simulation model comple- ments models serving the first purpose, strategic planning. It provides an innovative, integrated approach to study malaria diffusion and control strategies for the aggregated SSA region in the long run. Our simulations complement existing findings with the feature of testing several policy-scenario combinations and thus putting the central question of the costs and benefits of phasing out DDT for IRS into perspective. Such tests allow exploring the range of possible outcomes, in our case particularly the costs for eliminating malaria, and the stability of the costs for different intervention mixes under different scenarios (target years for eliminating malaria). The endogenous representation of variables such as population and GDP allows calculating aspects of costs and benefits of the policy-scenario combinations over time. This is particularly important as the target (malaria elimination) becomes more difficult to achieve, the later interventions are implemented and thus the more the population that needs to be covered by the interventions has grown (see also [21]. The MMM with its capacity to compare some of the costs and benefits of different policy-scenario combinations is helpful to policymakers, e.g., in providing orientation and stimulating discussions when the continued need for DDT in IRS is re- evaluated by the Stockholm Convention every two years. Comparing the costs for eliminating malaria and the economic benefits of doing so (i.e., the gain in GDP) for different combinations of vector control interventions provides important decision support for actors in global health activities. The MMM can also contribute to raising and maintaining awareness with development partners that all strategies aiming at eliminating and eventually eradicating malaria need to be sustained over time periods that exceed the usual organizational or political planning horizons. Awareness raising also concerns the danger involved in premature reductions in expenditures for malaria elimination and eradication which would severely compromise improvements in the malaria situation achieved until this point in time. In addition to providing quantitative results, the MMM is also an analytical framework that allows careful analyses of policy alternatives proposed by actors in the field or resulting from further research. Simulations and sensitivity analyses allow decision makers to explore effects of such policies on a range of outcomes over time. They also identify competing objectives such as, in the context of this paper, the minimization of direct costs of IVM interventions versus the minimization of adverse long term effects of the use of DDT. Simulation models help decision makers to confront such competing objectives or trade offs by separating issues of scientific uncertainty (e.g., the impact of DDT on human health and the environment) from disagreement over competing objectives. Issues of scientific uncertainty can be subjected to DDT versus Non-DDT Malaria Control sensitivity analysis so that the impact of different assumptions can be visualized. The MMM as an example of a simulation model thus provides a user-friendly tool that creates more systematic mechanisms for analyzing alternative interventions and making informed trade offs. Limitations of the approach The integrated nature of our approach necessarily entails simplifications and uncertainties in many ways. Malaria epidemi- ology is highly aggregated and the model cannot evaluate the most effective mix for eliminating malaria. The most severe data limitations are the data on malaria cases which have a high degree of uncertainty. This makes it difficult to calibrate the entire model and particularly the malaria transmission subsector to the data. There is also some uncertainty about the unit costs of the IVM interventions, particularly for EM and IRS. Unit costs can be subject to economies of scale, diminishing returns or increasing costs e.g., for reaching more remote population in rural areas. In the case of IRS, the current evidence is even insufficient to quantify properly the effect of IRS in high transmission settings [47]. The model also does not consider the effect of combinations of ITN. However, as the difference between the costs for implementing scaled up IVM interventions and the gains in GDP is very big, uncertainty about the unit costs of IVM interventions does not affect the conclusions that can be drawn from the simulation results. All our policy-scenario combinations are based on the assumption that current IVM interventions are continuously improved and further developed so that IVM can in fact be scaled up to the degree necessary for eliminating malaria. We also assume that (cost-effective) alternatives to DDT are not only feasible but that they can be scaled up to the level necessary for malaria elimination. While we explicitly represent constraints in the absorptive capacity of scaled up IVM interventions, our model does not address possible inefficiencies in the implementation of IVM interventions and it only describes a very aggregated process of building capacity for the effective implementation of IVM interventions and case management measures. The model can therefore not answer the question whether malaria elimination is really possible. It can only calculate the costs required for elimination in case the described assumptions hold. Notwithstanding these uncertainties, simplifications and limita- tions of our approach, the costs estimated by our simulation model are in line with the costs estimated by the World Health Organization (WHO) [4] and our simulation model also calculated losses in GDP due to malaria that are almost identical to the estimates of Roll Back Malaria [2]. This is a strong indication of the validity of our results. The close fit between simulated data and historical data as recorded in statistical data sources further supports the validity of the simulation results. Further developments of the approach Complementing the costs and benefits calculated by the simulation model requires further research. This concerns improvements of our database for malaria-related indicators and strengthening of our estimations of the effects of DDT on health and the environment. Such data would allow for more complete cost-benefit analyses and thus for more detailed decision support. Future applications of the MMM should also focus on climate change analyses which is particularly relevant because changing rain patterns will considerably affect malaria occurrence until 2050. They will also affect migration of people and land use and as such further alter the occurrence of malaria. Such analyses could test the robustness of the calculations presented in this paper for PLoS ONE | www.plosone.org 9 November 2011 | Volume 6 | Issue 11 | e27771