the malaria infectious population and on malaria mortality, which depends on the efficacy and coverage of case management. The malaria infectious population results from the vulnerable population and the malaria infection rate, i.e., the rate at which the vulnerable population is infected with malaria. The vulnerable population is determined based on the estimated proportion of the population living in risk areas, and on the effective coverage of malaria prevention, i.e., of IVM interventions. Climatic conditions play an important role for malaria transmission: more suitable climate conditions may facilitate malaria transmission. The extension of the malaria risk areas, and thus the vulnerable population, depends on climatic conditions such as temperature, rainfall and humidity. We use the Malaria Transmission Climate Suitability Index (MTCSI, e.g., [30], which defines the level of risk for six disaggregated sub regions in SSA, to determine the vulnerable population. The model does not explicitly represent the parasite cycle in the vector since these are rapid processes (in the order of a few weeks) whose dynamics would not be relevant for long-term simulation. Instead, the model repre- sents the acquisition and persistence of the parasite in the human body, which is a fundamental process driving the diffusion of the disease. N IVM interventions: This subsector represents the implemen- tation mechanisms and related costs of selected IVM interventions. IVM interventions include various protective measures which we summarize into insecticide-treated bed nets (ITN), indoor residual spraying (IRS), and environmental management (EM). To determine IVM coverage, we consider the cumulative units of intervention deployed and their depreciation over time (which is quite long, for example, for bed nets, while quite short for IRS treatment). We consider all IVM interventions and their individual effectiveness, and then use an estimated 50% overlapping factor among IVM interventions to account for the fact that no intervention alone can ultimately eliminate malaria (e.g., [31]). Possible develop- ments regarding vaccination are not considered, as the time required for developing such vaccination, its potential effectiveness, and the resources involved remain highly uncertain. The use of ITN has turned out to be very effective, especially when bed nets are properly used. The big advantage of this protective measure is that the bed nets are relatively cheap and that they guarantee protection for three or four years if properly maintained. However, if misused, these benefits are almost entirely canceled. In the MMM model this is reflected by the average education level which determines the effectiveness of ITN. IRS is a predominantly chemical vector control method consisting of the indoor spraying of insecticides to kill or repel mosquitoes. The different insecticides used for IRS have different unit costs and different residual times on walls. We integrate the average annual costs per person into the model by calculating the unit costs of the current mix of insecticides and the costs of a mix with no DDT (Table S1; Table 1). The costs covered by the MMM include all costs associated with spray operations, management and administration, and technical assistance [32]. One of the significant limiting factors of IRS is that it is labor intensive and that mosquitoes develop resistances [11]. The MMM model considers different resistance factors for the different insecticides [11]. Environmental management consists of environ- mental manipulation (e.g., periodic removal of aquatic weeds or riverine vegetation, alternating cycles of irrigation and dry farming), environmental modification (e.g., capital-intensive investments that lead to permanent changes such as landscap- DDT versus Non-DDT Malaria Control ing, drainage, land reclamation and filling), and strategies that reduce contacts between mosquitoes and humans (e.g., house screening) [33], [34], [35]. We also include larviciding, i.e., the direct application of larval control agents to larval habitats for killing the mosquito larvae, in the environmental management subsector [6], [7]. N Case management: This subsector keeps track of treatment coverage and costs for the malaria infected population. Treatment coverage depends on specific malaria treatment expenditure, but also on generic health expenditure per capita, since this determines coverage of basic health services. The effective treatment coverage is determined by the average efficacy of the malaria treatments (which can be reduced by increasing drug resistance) and by the percentage of infected people who attend formal health services. This percentage increases with improvements in the general education level. Effective treatment coverage is a good indicator to estimate malaria mortality among the infected population. The malaria transmission, IVM interventions, and the case management sectors are visualized in Figure S2. N DDT concentrations: This sub-sector represents the process of DDT production-distribution-use-dispersion at the global scale. It keeps track of the production and use of DDT for agriculture and malaria control. Global volumes of trade and concentrations of DDT in the environment are also repre- sented in this sector. Tracking DDT concentrations in soil, air, oceans or fish allows the assessment of possible DDT impacts on human health and the environment. N Cost accounting: The cost accounting subsector summarizes the economic costs of malaria (effect of malaria on productiv- ity) and of the implemented prevention and case management interventions (case management and prevention expenditures) depicted in the simulation model. It also calculates the long- term impacts of malaria on human health (effect of malaria on life expectancy, effect of DDT on life expectancy, fraction of the population affected by malaria) and the environment (DDT concentrations). Additional costs such as monitoring of human health and environmental exposure levels, repatriation of waste, repatriation of unused stocks or safe disposal of unused stocks of DDT are not included in our cost accounting. Data and assumptions The MMM model is long-term in scope and covers the historical period between 1970 and 2010 as well as projections into the future until the year 2050. The long historical period provides a means for calibrating and validating the simulation model while the long projection period into the future allows a long-term perspective on malaria and socio-economic dynamics. The simulation model is applied to the aggregate SSA region. This global scope allows estimating the budget requirements for eliminating malaria under different assumptions concerning the relevant time horizon and the mix of vector control interventions. An important step in the model-building process is to specify mathematical equations for each of the relationships in the model and to quantify the model’s parameters. One set of parameters are the initial values for the stocks. Another set are the constants in the model. Data is also used for the calibration of the simulation model. In this case, the time series for a particular variable which is produced by the model is compared to a time series perceived in the system. Parameters with no available data source can be estimated or calibrated such that the simulated time series for other variables matches the observed data for that variable as closely as possible. Table S1 provides an overview of the data used PLoS ONE | www.plosone.org 4 November 2011 | Volume 6 | Issue 11 | e27771