Malaria Theory
The website is searchable. The navbar at the top includes links to related content and vignettes. The sidebar at the left presents vignettes in an organized outline. Callout tips like this one can be collapsed.
What is Theory?
To understand and manage malaria, we measure it. The concepts and background information come from more than a century of malaria research and practical malaria control experience. We need mathematics and statistics because they have the vocabulary and grammar to discuss, analyze, and apply quantitative information about malaria.
Malaria is complex. One way of dealing with the complexity is to build mathematical models that represent the underlying biological processes (as currently understood). The models – often formulated as dynamical systems – can be understood as abstract representations of a generic process or as approximations of real systems. In malaria analytics, the point of model building is to generate useful intelligence and support malaria policy.
Malaria theory refers to the diverse set of concepts, principles, methods, metrics, and mathematical models commonly used to measure and understand mosquito ecology, malaria epidemiology, malaria transmission dynamics, and malaria control.
This website is focused on mathematical models and theory for malaria analysts. It is part of a cluster of websites (see Related Content).
To keep this focused on the mathematical models, we put supporting material in other websites:
In Measuring Malaria, we look at similar themes, but we focus on the evidence from malaria research. It also serves as an organized hub or repository for malaria data.
SimBA (short for Simulation Based Aanalytics) introduces software for nimble model building and computation to support robust malaria analytics.
RAMP & Adaptive Malaria Control is focused on malaria analytics: how can we apply malaria theory to improve malaria control?
Adaptive Malaria Control is a way of managing malaria with high quality, robust analytics; and
Robust Analytics for Malaria Policy (RAMP) is the supporting inferential framework
In Adaptive Malaria Control, Uganda, we present an adaptive malaria control prototype developed for national malaria programs. The project was a collaboration between Uganda Minsitry of Health’s Department of Health Information and National Malaria Elimination Division, the University of Washington, the RAMP Uganda team at Pilgrim Africa.
These links can be also be accessed through Related Content in the navbar.
A Little Bit of History
We find it useful to study malaria theory from a historical perspective.
Quantitative approaches to the study of malaria in populations trace back to Ronald Ross and his early attempts over two decades (roughly from 1897 to 1917) to measure and manage malaria [1] and to develop the mathematical foundations for understanding epidemics [2–4]. Ross’s work influenced early field studies in malaria epidemiology and the metrics we use to measure malaria [5–7].
Since then, malaria theory has benefited from concepts and models developed in malariology and various related academic disciplines, including mathematics, epidemiology, ecology, entomology, anthropology, economics, and pharmacology. Today, malaria theory is characterized by various mathematical, computational, and statistical approaches, including dynamical systems and individual-based models to simulate malaria epidemiology, transmission dynamics and control [8]. Despite all that, some important questions about malaria remain poorly quantified [9].
Using history as a lens, we can sometimes get insights into why studies were originally done, how the lessons were generalized, and perhaps what was missing. For example, the earliest debate on malaria control failures (that we’re aware of) occurred in 1904 in the British Medical Journal [10]. Ross participated in these debates, and it led to development of the first mathematical model of malaria, which discussed the idea of a buffer zone for mosquito control [11]. That idea shaped the control strategy for yellow fever and malaria control during the Panama Canal Project.
History provides a useful way of pressure testing ideas. It can help us understand how malaria analytics for disease control can work well even with incomplete information, and what aspects of malaria that could make a difference today might have been overlooked in the past. We’re interested in understanding how practical constraints on building, analyzing, and publishing mathematical models might have distorted the models we use in science and policy today.
This website was developed and is maintained by Professor David L Smith, University of Washington. It has benefitted from contributions by several others. If you’re interested in making a contribution and becoming a part of the project, please write me.
Also, if I’ve failed to cite an important paper or mis-characterized something, please write me.