Malaria theory refers to the diverse set of concepts, principles, methods, metrics, and models commonly used to measure and understand mosquito ecology, malaria epidemiology, malaria transmission dynamics, and malaria control.
Quantitative approaches to the study of malaria in populations trace back to Ronald Ross and his early attempts to measure and manage malaria (roughly, from 1900 to 1911) [1]. Since Ross, 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.
Website
This website was developed as a primer for malaria analysts who want to learn malaria theory. It is organized into a set of vignettes – short essays focused on a single topic.
Topical vignettes introduce introduce concepts, ideas, models or mathematical or analytical methods. Some of these are provided as background, and others are needed to apply theory to reducing the burden of malaria and eradicating malaria parasites. These are organized by theme in the sidebar (to the left): malaria epidemiology, transmission dynamics, mosquito ecology, malaria control, measuring malaria, and other topics.
A set of cross-cutting essays about modeling is available from drop-down menus in the navigation bar (at the top), as well as some other useful resources.
In developing a website to explain theory to malaria analysts, we have made a commitment to constructivism: everything we explain here can be computed in some form and incorporated into an analysis. Most of the examples use SimBA software, which was developed for these tasks.
Related Content
This website was developed to explain mathematical ideas and concepts and malaria models. While it is also important to know have some experience building and solving models, and applying theory, those topics are not covered here. Instead, other websites have been developed that use malaria theory to teach the skills malaria analysts will need to become proficient at simulation-based analytics:
Software has been developed for nimble model building and computation. The software is modular, flexible, and extensible, and it implements most of the mathematical models described on this website. The software includes functions to set up, solve, and visualize outputs, so it should be easy to learn and easy to use. To learn the software, the place to get started is an open-access R package called ramp.xds, available for download from github. The software is well-documented, and it has its own vignettes, including the vignette: Getting Started. The core computational package, ramp.xds, is supported by four satellite R packages. A website called SimBA was developed as an umbrella website for the software, and it includes some examples of workflows that show how to build models.
To support malaria analytics, we have been aggregating Malaria Data to validate the models and ensure they are grounded in the best available evidence.
An inferential framework was needed to support malaria analysts, who need a rigorous way of giving robust policy advice with weak evidence and uncertainty. The guiding concept was robust analytics: analysts should go to great lengths to develop malaria policy advice that has characterized, quantified, and propagated uncertainty. We have called this Robust Analytics for Malaria Policy or RAMP. In malaria policy, robust analytics can be done iteratively on schedules that make sense for national strategic planning. The goal is to accumulate malaria intelligence, to prioritize critical data gaps, and to reduce uncertainty over time. We have developed these ideas around the notion of adaptive malaria control. The website Robust Analytics for Adaptive Malaria Control was developed to develop ideas about uncertainty in malaria, information, malaria intelligence, robust analytics and the application of theory.
A new methodology called Adaptive Malaria Control, Uganda a prototype that implements adaptive malaria control for national malaria programs.
The vignettes in this cluster of websites are meant to create a coherent system that can support development of malaria analysts in malaria-endemic countries who can implement simulation-based analytics. The organization of the material in multiple websites was done, in part, to make it possible to introduce complex ideas in order:
Introduce the concepts and ideas;
Building, solving, and analyzing models;
Use the models to understand malaria in situ;
Integrate simulation-based analytics into national malaria programs.
The vignettes in one website will, thus, often have links to the vignettes in another (and back), so you can trace an idea from theory, through model building, to a discussion of how to apply it, and to an example of it being used by a national malaria program.
This website is maintained by Professor David L Smith, University of Washington.
To contribute, ask for help, or offer comments or constructive criticism, please send email to smitdave@uw.edu.
References
1.
Ross R. The Prevention of Malaria. 2nd ed. London: John Murray; 1911.