Basic Concepts

An Overview of Basic Theory for Malaria

NOTE: In all this material, we are focused on Plasmodium falciparum, so when we say malaria, we mean human disease caused by infection with P. falciparum. While there are many differences, the main one is that the other human malaria parasites have another stage, called hypnotozoites that remain dormant in the liver. When we discuss other malaria parasite species, we will be very explicit about it.

To understand malaria in populations, we start with the basics: parasite transmission through blood feeding and malaria transmission dynamics. These concepts are best introduced with simple models.

While simple models are great for learning about malaria transmission, they are probably not adequate for malaria analytics. If we want to use models to support malaria policies, we will need to have the capability of building models that are realistic enough to address policy questions. To build realistic models, we will need to develop a deeper understanding of malaria epidemiology, malaria transmission dynamics, mosquito ecology, health systems, and vector control. Each one of these can be understood as a separate system that is affected by other parts of the system, but to make any headway, they must all fit together, both conceptually and computationally.

We are not just learning these models as an academic exercise. We need some way of setting rational expectations about the effects of malaria control and other factors that affect the health burden of malaria in a country, or in the health districts or villages. There are important questions that can be addressed using some combination of mathematical models and data, and some of these would be difficult to address in any other way:

There are many other questions we will eventually need to address, but it is important to recognize that it is often much easier to ask a question than answer it. There are constraints on basic research that are unforgiving, so it would be challenging to design a study that could answer the questions that matter. If we are going to provide answers to these questions, we will need to use some combination of malaria research data collected over the past 144 years of studying malaria, the malaria surveillance data collected through national health management information systems (HMIS).

The basic models are a good way to introduce the basic concepts, but the simple models often get it wrong. The simple models are lacking some of the basic skill sets required to make good policies – or to put it another way, they simply aren’t complex enough to address some questions: a non-spatial model can’t doesn’t make spatial predictions. Before we go too far with the complexity, we need to remember that all models are approximations. We will need to get it right, but there’s a trap here. We probably don’t need to know everything to make good decisions, and we certaintly won’t need to know everything with a high degree of certainty. We don’t need a perfect model. To develop robust analytics, it’s important to remember that all models are wrong, but some models are useful. (This statement, often used in one form or another, was paraphrased from George P. Box. I’ve probably got the quote wrong, but it’s still useful.)

We need some way of doing analysis for policy that can help us make malaria policy without knowing everything. We need some way of building models that are not too complex for the questions we need to address, but that can be repurposed later. In many cases, we will need to elaborate, and we want to guarantee that we don’t get stuck. This is one of the reasons why we developed ramp.xds and a suite of software packages to support nimble model building for simulation-based analytics, or SimBA.

Scaling Complexity

If, as malaria analysts, we want to have a strong understanding of malaria theory to support malaria policies, then we must start simple and add complexity. To get it right, we need to understand malaria spatial dynamics, exogenous forcing, various kinds of heterogeneity and complexity. We introduce the concept of thresholds for malaria analysts, and the metrics we use to measure malaria.

We discuss the limitations of simple models through the concept of a skill set, which takes us to our undertstanding of malaria as a complex adaptive system with the major interacting domains: transmission, malaria epidemiology (in the narrow sense), mosquito ecology, and malaria control.

Transmission

Parasite transmission through blood feeding by adult mosquitoes and parasite infection dynamics in adult mosquitoes. We understand blood feeding as an interaction between adult female mosquitoes and humans. These are the core processes that sustain malaria in populations, and we would like to understand parasite dispersal, the structure of malaria transmission, the spatial scales that characterize transmission, malaria connectivity, and other concepts that will help us to understand malaria transmission well enough to plan for malaria control;

Malaria Epidemiology

Malaria epidemiology, in the narrow sense, describes a set of concepts including exposure, infection, disease, immunity, infectiousness, care seeking, drug taking, diagnostics, and detection. Within malaria epidemiology, we recognize two important, closely related themes:

  • Exposure, infection, and malaria transmission.

  • Exposure, infection, and disease.

Mosquito Ecology

Vector Ecology includes blood feeding by adult mosquitoes and all the other processes that regulate mosquito population dynamics, and the factors that determine the distribution and abundance of mosquitoes. Under vector ecology, we study adult mosquito behavior, exogenous forcing by weather, mosquito dispersal, mating, habitat dynamics, and all the factors that could become an important factor in the management of malaria;

Malaria Control

The management of malaria, through health systems and vector control, has played a major role in shaping the epidemiology of malaria in the world today, and it is critical to understand those effects before we try to modify existing systems. Under malaria control, we recognize two major domains:

  • Medical interventions and malaria therapeutics applied through routine health care and used for public health measures including mass treatment, mass distribution of vaccines, and mass distribution of monoclonal antiboddies. These interventions play a direct role in reducing disease, and they can also play some role in reducing transmission.

  • Vector control to reduce exposure, suppress mosquito populations, and suppress malaria transmission.

Spatial Concepts

Analysis

In Analysis,, we introduce some of the methods commonly used to understand dynamical systems models of malaria, from the perspective of the malaria analyst.

Advanced Topics

In Advanced we take on some of the topics that provide some important context,

Parasites

In The Parasites

  • Parasite genetics and evolution, including the evolution of drug resistance.

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Microsimulation