(Table 1). For scaling up this mix and also for the subsequent analyses we assume an adaptation delay of five years during which the absorption capacity for the interventions is gradually implemented. The final share of malaria budget on total GDP is reached in the year 2017 and maintained either through 2025 or 2035, depending on the scenario. Cost calculations only consider the direct costs such as the price per IVM intervention per person per year (Table S1) and the direct benefits such as impacts on gross domestic production (GDP). Indirect or external costs of DDT, i.e., costs related to environmental and health impacts are difficult to assess. This is due both to the lack of sufficient data on these impacts and also to the fact that the trade off between short term epidemiological improvements due to DDT and the long term external costs involves value judgments and depends on risk attitudes [7]. We add indications of external costs and risks to highlight how they can affect evaluations of the effectiveness of the continued use of DDT for IRS versus its rapid phase out. The use of DDT in IRS is for example often linked to fears that DDT can threaten agricultural exports (e.g., [36]) either because of actual contam- ination of food crops or because of consumer concerns mainly in European countries. Although DDT is applied indoors, dwellings in rural settings are often very close to farmed areas so that DDT spillovers to crops become possible and unavoidable if one considers the eventual decay of sprayed walls. The risks of actual contamination of food crops are very difficult to quantify. As an alternative to quantifying these risks we compare the difference in the costs of the continued use of DDT for IRS versus its rapid phase out to the value of the agricultural exports in SSA. The simulation model also contains a climate-suitability index (see Figure S2) that allows analyzing different climate change scenarios. In this paper, however, we focus on the policy-scenario combinations summarized Table 1 and retain climate change analyses for further research. Results This section describes results obtained from simulating our baseline scenario and comparing different policy and scenario analyses to this baseline. It is important to note that the simulation results presented for malaria and other key socio-economic variables cannot be interpreted as precise forecasts. Instead, they are indications of the likely development of the system under the current and under modified policy frameworks. Baseline and model validation Within this section we discuss simulation results and historical data for the baseline scenario. The baseline simulation shows a close fit between historical data and model results for the major socio-economic indicators (R2 of almost 1 for population development, 0.995 for average adult literacy rate, 0.79 for average life expectancy and 0.969 for GDP). The simulated value of economic production (GDP) very well matches historical data, indicating that the extended Cobb-Douglas production function can reasonably explain observed economic growth. A further indication of model validity is that the simulated under-five mortality rate in the future is in line with the United Nations’ population projections [37]. Baseline projections into the future depict an s-shaped increase in population and a continuous, albeit slow growth in literacy rate and life expectancy which is partly due to the steadily increasing population that makes it more difficult for education and health interventions to reach the entire target population. GDP is projected to increase continuously as a consequence of improve- DDT versus Non-DDT Malaria Control ments in education and health. The growth in per capita GDP is slower as the total GDP has to be distributed among a steadily increasing population. Key malaria indicators such as the total estimated malaria cases (Figure S3) depend on the ecological as well as socio-economic context and on the available funding for malaria control (government funding plus external funding). These funds were 0.27% of total GDP in 2008, the last year with available statistical data, and are assumed to grow by 1% each year for baseline simulations into the future. Historical development of malaria cases, i.e., of the number of malaria infected people per year, is not well documented. Official data from the WHO covering most of the SSA region is available only starting in the 1990s (World Malaria Reports from several years). Even for that period, data is characterized by several holes (missing data from reporting countries), and it only represents reported cases, which are estimated to be a small fraction of actual cases [38]. Estimated malaria cases seem to have steadily increased until the 2000s and have since experienced a considerable decline that can be explained by the large investments – especially concerning the distribution of bed nets – operated by the major malaria programs between 2000 and 2010. The model replicates this trend (Figure S3). The steady increase in malaria cases prior to 2000 is a consequence of the growing population in SSA and the fact that IVM coverage between 1970 and 2000 was approximately constant. The oscillations in the historical time period, both for total estimated malaria cases as well as the population fraction affected by malaria, is a consequence of identical oscillatory patterns in the climate suitability index. For the next two decades, the model projects a fairly stable number of malaria cases (around 360 Million), followed by a significant decline which brings the number of cases close to 200 Million in 2050 (Figure S3). The overall decrease in malaria cases is due to the continuation of current malaria control policies, most importantly of prevention policies. A continuous increase in the coverage of IVM interventions first leads to a stabilization of the (otherwise rapidly growing) vulnerable population, and eventually to its decline. This is also reflected in the declining fraction of the population that is affected by malaria (Figure S3, solid black line). Economic costs of malaria For calculating the economic costs of malaria, we ran a simulation that assumed no malaria as of 2012. In this counterfactual exercise, development would only be determined by the interaction between the four non-malaria model sectors population, education, health, and production. By comparing this simulation to the baseline values we can estimate the losses in GDP due to malaria (difference between the no malaria scenario and the baseline) According to this calculation, the losses in GDP due to malaria in 2012 are approximately 11 Billion US $. This value is almost identical to the costs of malaria calculated in the Global Malaria Action Plan [39]. At the end of the simulation period, the GDP loss is approximately 22 billion US $, assuming a discount rate of 3%. Costs and benefits of rapidly phasing out DDT In this section we address the central research question of this paper, i.e., the costs and benefits of a continued use of DDT for IRS versus its rapid phase out. Direct economic costs and benefits of rapidly phasing out DDT for IRS. The direct economic costs of non-DDT insecticides are approximately 50% higher than those of DDT (Table S1 and quoted sources). Figure S4 compares the total average yearly expenditures for the current mix policy (2035- PLoS ONE | www.plosone.org 6 November 2011 | Volume 6 | Issue 11 | e27771