5 Models and Data

Plasmodium falciparum transmission dynamics are complex: they involve multiple-agents (mosquitoes, parasites, and humans as hosts and system managers), non-linear dynamics, locally peculiar features, spatial population heterogeneity, stochasticity, and exogenous forcing by weather, hydrology, and malaria control. Over time, these processes can be modified by economic development; by changing socioeconomic status, human incentives and social norms; and by the evolution of resistance. Every one of these features of malaria transmission dynamics and control presents its own set of challenges as we use of models and data for malaria decision support.

As analysts, the challenge we will face is how get the information we need, and then use it to answer a question. Since malaria transmission is complex, we use mathematical models formulated as systems of differential equations. In the chapters that follow, we will present a set of mathematical models and computational methods that can handle all that complexity and heterogeneity. An important practical problem is how to quantify and synthesize all of the factors affecting transmission at some particular place and time to support malaria control programs in various ways, including monitoring and evaluation of malaria control. In developing models for decision support, we will need a lot of information that we will probably never get, and the data we get is often not the data we need. Even research studies rarely collect enough to understand local malaria epidemiology, transmission dynamics, and control. We use the data we have to calibrate a model, and then we will simulate various scenarios and compare the outcomes. We will need to make the most of the data, and borrow information to fill the gaps.

How do we ensure that our models are close enough to the data to be useful?