Contents

1. Combining Entomological, Epidemiological, and Space Mapping data for Malaria Risk-mapping in Northern Uganda Findings and Implications Ranjith de Alwis, Abt Associates November 15, 2012

2. Contents • Malaria and malaria control in Uganda • Indoor residual spraying (IRS) in Uganda • Impact of IRS on malaria prevalence • Entomological monitoring activities and findings • Risk mapping • Lessons learned • Recommendations

3. Malaria and Malaria Control • Malaria transmission • highly endemic and perennial • 90% of population at risk • 99% Plasmodium falciparum • Major vectors • Anopheles gambiae • Anopheles funestus • Interventions • IRS • ITNs/LLINs • IPT • Improved diagnosis/case management

4. Indoor Residual Spraying (IRS) • IRS—most effective malaria vector control method • Currently, the primary factor for deciding where to use IRS is malaria incidence, which results in expensive blanket coverage • Stratification based on risk—more effective strategy but requires reliable and representative data over time

5. Indoor Residual Spraying (IRS) Data needed for planning IRS • Vector bionomics (species and behaviour) • Vector susceptibility to insecticides • Suitability of structures and population compliance • Malaria prevalence patterns to determine time to spray On-going monitoring needs for decision making • Vector bionomics • Vector susceptibility • Residual efficacy of insecticide Data needed for decisions on phase-out or scale-up of IRS • Malaria epidemiological data over the time • Meteorological information • Feasibility of carrying out of other interventions

6. Indoor Residual spraying (IRS) • Started in 2006 in South Western districts • Moved to Northern districts in 2007 • 7-8 rounds have completed • Started with Lambda-Cyhalothrin • Then moved to Alpha-Cypermethrin • DDT was used in 2 districts for one round • Since 2010 Bendiocarb Target Population – 2.8 million Approx. 900,000 structures

7. Impact of IRS on Malaria Prevalence • Marked reduction in malaria cases, especially after Bendiocarb

8. Impact of IRS on Malaria Prevalence • Location based data not available in health institutions • Difficulties in combining epidemiological data with other information

9. Entomological Monitoring Activities • Pre- and post-spraying PSCs • Post-spraying wall bioassays • Monthly wall bioassays • National Susceptibility Study (2011) • Vector bionomics ****

10. Pyrethroid Spray Collections (2009-12)

11. Monthly Wall Bioassays (2009-12)

12. National Susceptibility Study

13. Risk Mapping 2005 risk map based on malaria endemicity. 2012 risk map detailed at district level to facilitate development of national vector control policy. • Planned to used a spatial model based on district-level information: • Malaria prevalence data • Entomological data • Intervention data • Meteorological data • Demographic, physical and geographical data • Data challenges • Malaria data is not representative or reliable • No recent entomological data • Low, predictive power of the risk map model – Need to improve.

14. Malaria Risk Maps

15. Lessons Learned • IRS effectiveness • Combining all these data help us to • Use correct insecticide • Manage resistance • Understand residual efficacy • Indoor resting behavior • Reduction of malaria prevalence / When to phase out IRS Strengthen other control methods. • Importance of location based data at lower administration levels • Risk mapping in project area • Will allow scale up of malaria control activities nationally while phasing out/reducing IRS in on-going areas

16. Recommendations To scale up vector control nationally while reducing IRS in on-going areas, we will need: • Location based data • Confirmed malaria cases • Establishment of indicators institutions • Spatial analysis of population distribution • Spraying time and frequency • Vector bionomics • Resistance status