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. Issues of scientific uncertainty can be subjected to 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. The integrated nature of our approach necessarily entails simplifications and uncertainties in many ways. Malaria epidemiology 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. Unit costs can be subject to economies of scale, diminishing returns or increasing costs e.
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 limitations 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.
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 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 different climate change scenarios. The model in its current form already incorporates features climate suitability index that allow for such analyses. 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. Aggregated representation of the MMM model. Aggregated representation of the structure of the Malaria Management Model. 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. 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. 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 s 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. Values in brackets describe the additional malaria expenditures as percentage of GDP required to eliminate malaria by Comparison of model results for DDT on dwellings for baseline black solid line , current mix dashed grey line and NoDDT simulations.
DDT concentrations on dwellings in three different scenarios. In the case of a continued use of DDT for IRS CurrentMix simulation , DDT concentrations increase steadily and considerably above the baseline values, where no malaria elimination is reached by 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 as target elimination year. 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 or , respectively. 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. 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. See references [49] — [66]. Technical appendix with all model equations. The technical appendix lists all the equations used in the Malaria Management Model. Initial values and parameter values are those from the baseline simulation.
The simulation model is also available as online supporting information Dataset S1 and Dataset S2. The model has to be completed with the data file Dataset S2. This supporting information dataset contains the data for the historical period of the simulation, i. We gratefully acknowledge the very helpful discussions with and comments from Michael Brander, Prof. Martin Herren and experts from icipe provided invaluable support during data collection.
Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Introduction DDT is considered to be the most cost-effective insecticide for combating malaria.
Methods In this paper we develop a computer-based simulation model to assess some of the costs and benefits of the continued use of DDT for Indoor Residual Spraying IRS versus its rapid phase out.
Results Our simulation results confirm that the current mix of integrated vector management interventions with DDT as the main insecticide is cheaper than the same mix with alternative insecticides when only direct costs are considered.
Conclusions The prototype simulation model illustrates how a computer-based scenario analysis tool can inform debates on malaria control policies in general and on the continued use of DDT for IRS versus its rapid phase out in specific. Introduction Malaria is one of the world's most deadly diseases, and it is especially dangerous for children and pregnant women. With this pilot application of the MMM we thus address the following research questions: What is the amount of resources necessary to gradually scale up the current combination of vector control interventions for achieving malaria elimination in or in , respectively?
How does this amount compare to the gain in GDP that could be achieved through malaria elimination? The indirect or external costs of DDT, i.
For this reason we only calculate the direct costs such as the price per DDT-intervention and the direct benefits such as impacts on gross domestic production GDP. We add some 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.
What is the amount of resources necessary to gradually scale up alternative combinations of vector control interventions for achieving malaria elimination in or , respectively? How do these amounts compare to the gain in GDP that could be achieved through malaria elimination? Materials and Methods This paper adopts a system dynamics approach.
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. Malaria transmission: This subsector calculates the number of malaria deaths per year.
This calculation is based on the size of 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. 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.
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.
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 represents the acquisition and persistence of the parasite in the human body, which is a fundamental process driving the diffusion of the disease. IVM interventions: This subsector represents the implementation mechanisms and related costs of selected IVM interventions.
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. Possible developments 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. 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 environmental manipulation e. We also include larviciding, i. 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. 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 represented 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. Cost accounting: The cost accounting subsector summarizes the economic costs of malaria effect of malaria on productivity 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.
Download: PPT. Table 1. Assumptions used for the mix of IVM interventions in the policies. Data and assumptions The MMM model is long-term in scope and covers the historical period between and as well as projections into the future until the year Policies and scenarios For testing different policies and scenarios, a baseline has to be established. Results This section describes results obtained from simulating our baseline scenario and comparing different policy and scenario analyses to this baseline.
Baseline and model validation Within this section we discuss simulation results and historical data for the baseline scenario. Economic costs of malaria For calculating the economic costs of malaria, we ran a simulation that assumed no malaria as of Costs and benefits of rapidly phasing out DDT In this section we address the central research question of this paper, i. Indications of risks associated with the continued use of DDT. Indications of external effects of the continued use of DDT.
Analyses of different intervention mixes and target years For putting the key question about the costs and benefits of a continued use of DDT for IRS versus its rapid phase out into perspective, we calculate the same costs and benefits for alternative combinations of IVM interventions. Summary of results in light of the research questions At the beginning of this paper we addressed one central research question and four additional questions necessary for putting the central question about the costs and benefits of a continued use of DDT for IRS versus its rapid phase out into perspective.
Additional questions: What is the amount of resources necessary to gradually scale up the current or alternative combinations of vector control interventions for achieving malaria elimination in or in , respectively?
Limitations of the approach The integrated nature of our approach necessarily entails simplifications and uncertainties in many ways. Further developments of the approach Complementing the costs and benefits calculated by the simulation model requires further research. Supporting Information. Figure S1. Figure S2. Figure S3.
Figure S4. Figure S5. Figure S6. Figure S7. Table S1. Text S1. Dataset S1. Dataset S2. Acknowledgments We gratefully acknowledge the very helpful discussions with and comments from Michael Brander, Prof. References 1. PLoS Medicine 8: e View Article Google Scholar 2.
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