RAMP
Robust Analytics for Malaria Policy
HOME \(\mapsto\) Malaria Theory – Malaria Data – SimBA (Software) – Adaptive Malaria Control, Uganda
RAMP is a bespoke inferential framework that uses conventional and simulation-based analytics to develop malaria policy that has attempted to fully characterized, quantified, and propagated uncertainty.
Malaria analytics – systematic analysis of data to generate policy advice or to support decisions – needs a rigorous inferential framework developed around malaria research and surveillance metrics, conventional methods, malaria knowledge and malaria theory, and contemporary policy cycles. To address this need, we developed Robust Analytics for Malaria Policy (RAMP) as an eclectic set of methods inspired by adaptive management, a branch of applied ecology that originated as a way of managing natural resources with uncertainty. Adaptive management aims to quantify uncertainty, and then to reduce uncertainty through iterative policy engagement and activities designed to fill critical gaps. We have implemented a methodology to generate iterative, robust analytics for malaria policy, with an adaptive loop, and we are calling it Adaptive Malaria Control.
This website is about RAMP for Adaptive Malaria Control. The methodology has been implemented in Uganda.
Given the complexity of malaria and the need for information, most policy decisions will be made despite enormous uncertainty. The guiding principle for robust analytics is that the analysts should go to great lengths in their attempts to characterize and quantify uncertainty, and then to propagate that uncertainty through analysis. Robust policy recommendations have fully integrated the uncertainty.
Adaptive malaria control starts with iterative, robust analytics, but within each round of policy engagement, the analysts must prioritize missing data and develop recommendations about how to modify surveillance systems or design studies to fill those gaps.
RAMP was developed because of the need for an inferential framework designed for analytics, not research. Since policy decisions are made on a schedule, something will be done whether or not there is any evidence to support it. It is better to pass along advice based on weak evidence than give no advice at all. We developed RAMP around the need to give policy advice on schedule using all available evidence, including data that would be regarded with disdain by most statisticians. While the same concepts and methods are used for research and analytics, the studies must weigh the uncertainty very differently. A different level of scrutiny and rigor is appropriate for analysis done to add to our collective knowledge about malaria, but the endpoint of malaria analytics is advice about what to do. A critical difference between research and analytics is that with malaria policies, the same process will get repeated. Malaria policies, if properly designed, can be self-correcting.
RAMP, with its focus on uncertainty, is an umbrella concept. The background material required for malaria analytics is broad and deep, so we have developed a cluster of closely related websites to organize the information. Links in the ad hoc navigation bar at the top of this page make it possible to access the other websites.
Malaria Theory
Malaria theory refers to the diverse set of concepts, principles, metrics, and models used measure malaria and to understand malaria in populations, including mosquito ecology and malaria epidemiology, transmission dynamics, and control. It is characterized by various mathematical, computational, and statistical approaches, including dynamical systems and individual-based models to simulate malaria. We developed a website on Malaria Theory to provide important background for malaria policy.
Malaria Data
An important complement to theory is a repository of Malaria Data.
SimBA
Nimble model building to apply dynamical systems theory to support RAMP and Adaptive Malaria Control are supported by a suite of R software packages, at a website called SimBA (short for Simulation-Based Analytics).
Adaptive Malaria Control
This website. Any link to Adaptive Malaria Control, including the title in the navigation bar, takes you back to Home