Managing Malaria

Learning As You Go

The development of malaria analytics is driven by the needs of malaria programs. Among the tasks involving analysis of data are surveillance, monitoring and evaluation; logistics; operational research; and strategic planning. Good malaria managers will also make an effort to stay current on relevant malaria research in the academic community, a task that involves critical thinking and the ability to spend some time crossing over: in some contexts, to evaluate the world as a scientist, and in others, to evaluate that same information differently.

In the current environment, the task list for malaria programs often involves managing international partners, including writing grants for The Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM); managing GFATM grants; dealing with the implementing partners, including quasi-international organizations who implement for the United States President’s Malaria Initiative (US PMI), the United Kingdom’s Department for International Development (DFID), and other funding agencies; and dealing with Gates Foundation and its partners across the world.

  1. Adaptive malaria control is a system for using information from national health surveillance systems and malaria research that has a few core elements:

    • Generating usable advice in a timely way
    • Characterizing, quantifying, and propagating uncertainty
    • Reducing the uncertainty through adaptive surveillance and operational research

  2. We thus need to design information systems around the needs of malaria programs.

  3. Unlike malaria research and normative policy, operational research and analysis for national malaria programs affects a defined geographical area.

  4. National health systems generate and store information in HMIS on weekly, monthly, quarterly, and annual schedules

  5. If we want to make use of that data, we must design analysis that can be repeated on schedule

  6. We need algorithms that combine surveillance data with sparse research data

  7. We need algorithms that translate the past into advice about what to do now and to make plans about the future