Materials and Methods This paper adopts a system dynamics approach. System dynamics is a computer aided approach to policy analysis and design. It applies to dynamic problems (problems that involve change over time) arising in complex social, economic, or ecological systems, i.e., in systems characterized by feedback loops, delays, and nonlinearities [16], [17]. An example for a feedback loop in the context of this paper is the circular causality between economic production and malaria where increases in economic production enable higher malaria expenditures, which help reducing malaria prevalence. This, in turn has a beneficial effect on economic production as the labor force becomes more productive and thus increases economic production even further. Delays are omnipresent, also in this example of a feedback loop, as time elapses between increases in malaria expenditures and observable increases in labor force productivity. Examples for nonlinearities are the elasticities with which total factor produc- tivity (an element of the economic production function) changes in accordance with changes in the education or health levels of the population. The system dynamics approach involves the development of a simulation model. Experimentation with computer simulation models allows the design and analysis of different scenarios and policies in a risk free environment [9]. Dynamic simulation models are sets of equations that describe the behavior of dynamic systems. The models study cause and effect. Given specified initial conditions and assumed behavioral parameters, the models trace the changes in key variables over time and allow seeing the dynamic implications of the assumptions [18]. Mathematically, the basic structure of a formal dynamic simulation model consists of a system of coupled, nonlinear, first-order differential (or integral) equations. Simulation of such systems is accomplished by partitioning simulated time into discrete time intervals of length dt and stepping the system through time one dt at a time. By breaking the simulated time into discrete intervals dt simulation makes possible the creation and use of models that cannot be solved in closed form. Simulation thus expands the range and complexity of problems that can be modeled [16]. Structure of the simulation model The Malaria Management Model contains an aggregated representation of the malaria transmission process, integrated vector management with a special focus on DDT, and malaria case management (diagnosis and treatment) in sub Saharan Africa. This malaria sector of the model is based on research carried out in the context of the Community Level Model project [19] and it is integrated into a broader socio-economic development framework that traces the most important social and economic aspects of development in the SSA region. This framework is based on the Threshold21 (T21) simulation model structure developed by the Millennium Institute [20]. T21 provides an integrated represen- tation of the fundamental socio-economic and environmental development mechanisms [20]. It is a scenario analysis tool designed to support national development planning and has been successfully applied in over 25 countries (e.g., [21], [22]). In this pilot stage, we apply the simulation model to the aggregate sub Saharan African region (SSA), where the vast majority of malaria infections occur [2]. The boundaries of SSA provide a reasonably homogenous malaria region for the generic analysis of costs and benefits of the continued use of DDT for IRS. The resulting Malaria Management Model (MMM) keeps the broad and integrated approach that characterizes the T21 framework. At the same time, it is specifically developed with an DDT versus Non-DDT Malaria Control emphasis on representing and analyzing the dynamics of the malaria problem. The model thus supports an integrated assessment of malaria control policies and their long-term development implications. Figure S1 provides an aggregated representation of the structure of the MMM. 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 Figure S1 describe the directions of these interactions. The population sector calculates the total population in the SSA region based on births, deaths and migration [23], [24]. For the calculations, population is divided into one-year age cohorts and differentiated by gender. Fertility, and thus the number of births, depends on income and education [25]. Mortality, and thus the number of deaths, is determined by life expectancy (which is calculated in the health sector) and malaria. Under-five mortality is used as an input to calculate fertility, so that with higher under- five mortality rate (of which malaria deaths are an important component) fertility rate is also higher. This is an important development mechanism that tends to balance the increase in population due to lower mortality with a decrease in fertility. The education sector describes the process of acquiring education and thus becoming literate through public and private systems. Access to education and thus eventually the average adult literacy rate depends on the expenditure for education. The health sector reproduces how access to health care and thus life expectancy change over time depending on the level of income and on the expenditure for health care [26], [27], [28]. The impact of malaria on deaths is calculated in the population sector. The production sector calculates the economic production of goods and services by using an extended Cobb-Douglas production function. Due to its simplicity and flexibility, the extended Cobb- Douglas production function has been extensively used for long- term development analysis in a variety of developing countries [21], [29], [22]. It is especially well suited for our application, in which we focus on long-term economic trends, where we only need a low level of detail with respect to the process of economic production. Total factor productivity (TFP) is determined based on the overall levels of education and health. The effect of malaria prevalence on productivity is also taken into account. We do not explicitly consider distributional issues, that is, we use average figures (e.g., average income, average level of education, average life expectancy) as elements affecting economic, social, and epidemiological trends. In reality, the poorest, least educated, and physically weaker individuals and families are the most affected by malaria. By working with average figures, we do not neglect that those poorest households are the most affected by malaria, but we assume that inequality of distribution will not fundamentally change over the time horizon of the simulation. Since our policy analysis is not concerned with distributional issues and redistribution policies, this assumption seems consistent with the scope of our work. Nevertheless, the inclusion of inequality dynamics in the analysis of malaria diffusion is a fascinating area for further research. The malaria sector occupies a central role in the framework since it fundamentally affects development in the other sectors and is at the same time also affected by the development in the other sectors (Figure S1). The malaria sector itself is split into five subsectors (Figure S2): malaria transmission, IVM interventions, case management, cost accounting, and DDT concentrations, which are described in more detail below. N Malaria transmission: This subsector calculates the number of malaria deaths per year. This calculation is based on the size of PLoS ONE | www.plosone.org 3 November 2011 | Volume 6 | Issue 11 | e27771