Adaptive Malaria Control
The website is searchable. The navbar at the top includes links to essays on concepts and related content. The sidebar at the left presents a set of essays in an organized outline.
Adaptive Malaria Control is a way of managing malaria with high quality, robust analytics that includes two essential components:
it should be iterative; and
it should be designed to identify, prioritize, and fill critical uncertainties.
Adaptive malaria control draws on principles developed for adaptive management of natural resources, including a reliance on mathematical models and theory and a focus on uncertainty. Adaptive management is not entirely new to malaria, but it had not been formally described as a methodology or implemented by a national malaria program. This website was developed to introduce the concepts to malaria programs.
Adaptive malaria control relies on malaria analytics with multi-disciplinary teams who manage data and develop advice that is robust to uncertainty. Malaria is complex, and data describing malaria in context is limited. Most decisions will be made despite massive uncertainty. To make decisions despite uncertainty, we developed an inferential system bespoke for malaria called RAMP (=Robust Analytics for Malaria Policy). RAMP is designed around malaria surveillance and research data. It combines conventional and simulation-based analytics and that has gone to great lengths to characterize, quantify, and propagate uncertainty.
This website introduces Robust Analytics for Malaria Policy as an inferential framework, and adaptive malaria control as a way of developing policy in iterative cycles. In closely related websites, we tackle the closely related content:
A discussion of malaria epidemiology, transmission dynamics, and control is split into Measuring Malaria and Malaria Theory
Measuring Malaria is an organized repository for malaria data where we review malaria research data and the science;
Malaria Theory describes the mathematical models and supporting mathematical theory;
SimBA introduces the software we have developed for model building and computation to support RAMP and adaptive malaria control;
We have implemented the methodology for (and in collaboration with) the national malaria program in Uganda. To learn more, please see the companion website Adaptive Malaria Control, Uganda
If you’re interested in contributing, please write me.