Background: Malaria has recently been identified as a candidate for global eradication. This process will
take the form of a series of national eliminations. Key issues must be considered specifically for elimination
strategy when compared to the control of disease. Namely the spread of drug resistance, data scarcity and
the adverse effects of failed elimination attempts. Mathematical models of various levels of complexity have
been produced to consider the control and elimination of malaria infection. If available, detailed data on
malaria transmission (such as the vector life cycle and behaviour, human population behaviour, the
acquisition and decay of immunity, heterogeneities in transmission intensity, age profiles of clinical and
subclinical infection) can be used to populate complex transmission models that can then be used to design
control strategy. However, in many malaria countries reliable data are not available and policy must be
formed based on information like an estimate of the average parasite prevalence.
Methods: A simple deterministic model, that requires data in the form of a single estimate of parasite
prevalence as an input, is developed for the purpose of comparison with other more complex models. The
model is designed to include key aspects of malaria transmission and integrated control.
Results: The simple model is shown to have similar short-term dynamic behaviour to three complex
models. The model is used to demonstrate the potential of alternative methods of delivery of controls.
The adverse effects on clinical infection and spread of resistance are predicted for failed elimination
attempts. Since elimination strategies present an increased risk of the spread of drug resistance, the model is used to demonstrate the population level protective effect of multiple controls against this very serious
Conclusion: A simple model structure for the elimination of malaria is suitable for situations where data
are sparse yet strategy design requirements are urgent with the caveat that more complex models,
populated with new data, would provide more information, especially in the long-term.