Malaria Analytics

Managing malaria involves a broad set of tasks; some of those tasks involve analyzing data to generate information that will support decision making or development of malaria policies. The systematic analysis of data for malaria decision support or policy is called malaria analytics.

Inference for decision support and policy follows a different set of principles than the kind of inference that is taught at most universities to support basic research. On the one hand, the way people do analysis analysis for policy looks a lot like what they do for basic research: any analysis of data relies on the same statistical concepts and similar algorithms regardless of the purpose of analysis. Despite these similarities, analysis for policy and research handle the data and uncertainty in very different ways. The endpoint of analytics is to give advice about a decision or policy. Decisions will get made and actions taken regardless, so the advice should be based on the best available evidence, even if it is weak. Analysis for policy that does not offer some advice is useless.

A core challenge for malaria analytics is that an inferential system must be developed around the data and issues that drive malaria policies. The information needed for malaria policy – malaria intelligence, if you will – calls for methods that are not part of the conventional training most people get. This is a core area of an emerging discipline called health metrics sciences. To address the need for a bespoke inferential system developed around malaria concepts and metrics, we developed RAMP. To address the need for training in malaria analytics and adaptive management, we developed this website.

What we are calling RAMP was designed around ideas from adaptive management, a branch of applied ecology that originated as a way of managing natural resources with uncertainty. Some of the ideas needed to be adjusted to fit malaria. We are calling the result Adaptive Malaria Control. A key feature of adaptive management is a focus uncertainty.

RAMP is not a Procrustean system – it is an eclectic ecosystem of concepts and algorithms developed to analyze data using whatever methods are called for, including conventional and simulation-based methods. These methods are designed around malaria metrics from two main kinds of sources: malaria data from national health management information systems (HMIS); and malaria research.

These fall into three broad sets of tasks: