Kate Battle

Geospatial Modelling for Malaria Risk Stratification and Intervention Targeting

In collaboration with key partners, MAP is engaged in a Bill and Melinda Gates Foundation funded project to provide geospatial analysis for malaria risk stratification and intervention targeting. Areas of research include:

  • Developing of new frameworks for modelling routine malaria surveillance data and integrating other malariometric data
  • Improving methods for incorporating seasonality and other drivers of risk (vectors, human mobility, intervention coverage)
  • Generating improved spatial models of intervention coverage and impact
  • Understanding how patterns of treatment seeking vary within and across countries.

We are working closely with the Clinton Health Access Initiative and the National Malaria Control Programs in the countries they support, as well as other partners to maximize the utility of model outputs for national malaria planning.

Clinton Health Access Initiative

The Clinton Health Access Initiative (CHAI) is a global health organization committed to strengthening integrated health systems in the developing world and expanding access to care and treatment for HIV/AIDS, malaria and tuberculosis. CHAI’s solution-oriented approach focuses on improving market dynamics for medicines and diagnostics; lowering prices for treatment; accelerating access to life-saving technologies; and helping governments build the capacity required for high-quality care and treatment programs.

CHAI is supporting a number of countries in Southern Africa, South-East Asia, Hispaniola and Mesoamerica to sustainably accelerate efforts to eliminate indigenous cases of malaria from 2015-2020 by providing direct technical and management support to governments on elimination planning, surveillance, and targeted attached and response activities.

Under the leadership of Dr Ewan Cameron and Dr Katherine Battle, MAP is working with CHAI to produce rapidly updatable, high resolution malaria risk maps to help countries better target and stratify intervention measures. A range of modelling methodologies are employed to produce these maps to accommodate data on case incidence, intervention coverage and treatment-seeking behaviours that are available at varying levels of spatial and temporal aggregation. Final outputs are then shared with national malaria programmes with supporting information on how best to use and interpret this information to improve strategic planning for elimination in each country and at the regional level.