RAMP
Robust Analytics for Malaria Policy
RAMP is a bespoke inferential framework that uses conventional and simulation-based analytics to develop malaria policy that has attempted to fully characterize, quantify, and propagate uncertainty.
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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.
To develop RAMP, we needed malaria theory packaged for malaria analysts that was linked to software that could be used to apply theory. This website is about malaria theory to support RAMP and Adaptive Malaria Control.
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. A solid backbround in malaria theory is needed to build and analyze models in the service of these tasks, but perhaps more importantly, theory can help the analyst think clearly about malaria epidemiology, transmission dynamics, and control, making it possible to be nimble and creative.
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 and methods 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
This website. Any link to Malaria Theory, including the title in the navigation bar, takes you back to Home.
Malaria Data
An important complement to theory is a repository of Malaria Data. It holds a collection of datasets describing malaria in populations.
SimBA (Software)
We designed software for nimble model building to support RAMP and Adaptive Malaria Control. The software was designed around a modular framework for model building called SimBA (short for Simulation-Based Analytics). The software was developed as a suite of R software packages.
Open SimBA.
Adaptive Malaria Control
Adaptive Malaria Control describes an iterative process of analysis and engagement to help malaria programs make operational decisions, optimize the allocation of resources, improve technical efficiency, and develop evidence-based policies to reduce burden and eliminate malaria. A goal for adaptive malaria control is to provide robust policy advice despite uncertainty. Through careful analysis of uncertainty and through consultation with program managers, surveillance systems are reviewed to identify key data gaps, and plans are made to fill those gaps and improve future decisions.
The methodology, implemented in Uganda, is described at a website, called Adaptive Malaria Control-Uganda.