RAMP

Robust Analytics for Malaria Policy

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Home \(\mapsto\) Adaptive Malaria Control

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RAMP is a bespoke inferential framework that uses conventional and simulation-based analytics to develop malaria policy that has fully characterized, quantified, and propagated uncertainty.

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Robust Analytics

Malaria analytics – analysis to support decisions or policy – needs a rigorous inferential framework developed around malaria research and surveillance metrics, malaria theory and knowledge, conventional methods, and contemporary policy issues. To address this need, we developed RAMP as an eclectic set of methods motivated by adaptive management, a branch of applied ecology that originated as a way of managing natural resources with uncertainty. We are calling the methodology Adaptive Malaria Control, and a key feature is a focus on uncertainty. Adaptive malaria control is iterative, robust analytics.

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 it should go to great lengths in its attempts to characterize and quantify uncertainty, and then to propagate that uncertainty through analysis to develop policy recommendations that have fully integrated the uncertainty.

We developed RAMP around the need to give policy advice on schedule using evidence that would be regarded with disdain by most statisticians. A different level of scrutiny 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. 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. A critical difference between research and analytics is that malaria policies, if properly designed, can be evaluated to fill critical gaps. The main difference between inference for malaria research and analytics is how to weigh uncertainty.

Related Websites

RAMP, with its focus on uncertainty, is an umbrella concept that weaves its way through adaptive malaria control. 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.

Uganda

The prototype for adaptive malaria control for national malaria programs was developed in Uganda. The home page for Adaptive Malaria Control, Uganda can be reached by clicking on the words Adaptive Malaria Control, Uganda in the navigation bar.

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 supporting this project is described in a more academic way at a closely related website, called Adaptive Malaria Control.

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 Software

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).