Costs and Benefits of Malaria Elimination in Kenya by Means of IVM Implementation

Introduction: Integrated Vector Management (IVM) (Beier et al., 2008) is considered to be one of the most cost-effective tools to reduce malaria transmission. This rational decision-making tool supports the design of an intelligent and optimal management of resources meant for malaria prevention and vector control. Some of the most widespread components of IVM include the distribution of bed nets, spraying operations and environmental management to reduce mosquito density. IVM was launched in Kenya in 2001, and during the last two decades the country has advocated for the use of this tool to combat malaria. IVM, however, requires a customized choice of interventions in order to achieve the optimal use of resources for vector control in the area of interest. Methods: In this article, we present a malaria simulation model that helps us assess the potential impact of different future IVM strategies to achieve optimal malaria reduction results in Kenya. The model used for this analysis is inspired by the Malaria Management Model (MMM) (Pedercini, Movilla Blanco, & Kopainsky, 2011), which has been developed by means of the System Dynamics methodology. The version of the model presented in this paper is adapted for Kenya, and the study includes the assessment of the cost and main socio-economic benefits from eliminating malaria in the country. The model also integrates the main impacts of malaria transmission into the socio-economic development, evaluating the effect of malaria on GDP production, literacy rate and life expectancy, among others. Results: Our model includes data collected from 1980 until 2018, before the RTS,S/AS01 vaccine was piloted in Kenya, and the simulation results confirm that the IVM programs in Kenya would not achieve malaria elimination within this decade. In addition to a substantial increase in the malaria budget, a reallocation of such a budget across interventions would be necessary in order to reach a malaria-free country by 2030 (Kenya Malaria Strategic Plan). The results also confirm that, despite the need for further efforts, malaria reduction in Kenya has provided sufficient economic benefits to cover the costs of malaria control. Conclusions: The System Dynamics simulation model illustrates how a computer-aided scenario analysis tool can inform the design of malaria control policies and assist decision-makers. Simulation models create systematic mechanisms for analyzing alternative interventions and inform about the different tradeoffs.