Hitting hotspots: spatial targeting of malaria for control and elimination

Abstract

Teun Bousema and colleagues argue that targeting malaria “hotspots” is a highly efficient way to reduce malaria transmission at all levels of transmission intensity.

Conflict of interest statement

AG was paid by GSK to attend a single advisory board meeting in December 2009 on how to develop models for the economic evaluation of RTSS. Currently she has a collaborative agreement with GSK to re-analyse RTSS Phase II trial data, which is managed via Imperial College but does not involve monetary exchange and under which the company does not have final decision on publication. None of these activities relates to this article on hotspots. All other authors have declared that no competing interests exist.

Figures

Figure 1. Hotspots of malaria transmission in the dry and wet season.

Mosquito exposure and parasite carriage are highly focal in the dry season (A). People living in hotspots are exposed to higher mosquito densities and, because individuals in households belonging to hotspots are more likely to be infected and infectious, mosquitoes are more likely to acquire a malaria infection in these households. In the wet season, as mosquito density and geographic distribution increase, infectious mosquitoes drive infection out into the rest of the village (B).

Figure 2. Targeted and untargeted interventions with long-lasting LLINs and IRS in a malaria elimination scenario.

The simulations for the low endemic setting with a baseline parasite prevalence of ∼15% in the general population (A) are based on parasite prevalence and mosquito exposure data from Korogwe, northern Tanzania (2008) . Effective coverage with LLINs is scaled up over 6 years to 60% prior to the intervention, creating a starting point for interventions aiming towards malaria elimination . Subsequently, the impact of four intervention strategies is simulated using an individual-based simulation model : (i) increasing LLIN coverage to 80% in a untargeted manner (blue solid line); (ii) increasing LLIN coverage with the same number of LLINs but preferentially targeting hotspots where 90% coverage is reached (dashed blue line); (iii) increasing LLIN coverage to 80% and yearly introducing IRS at 20% coverage in a untargeted manner (red solid line); (iv) a targeted approach using the same resources as the third scenario, reaching 90% effective coverage with LLINs and 90% effective coverage with yearly IRS in hotspots (dashed red line). LLINs were replaced every 4 years. Simulations were repeated for an area of high endemicity with a parasite prevalence of ∼40% in the general population (B).