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. 2015 Dec 29:4:e09520.
doi: 10.7554/eLife.09520.

Mapping residual transmission for malaria elimination

Robert C Reiner  1   2 , Arnaud Le Menach  3 , Simon Kunene  4 , Nyasatu Ntshalintshali  3 , Michelle S Hsiang  5   6   7 , T Alex Perkins  1   8   9 , Bryan Greenhouse  10 , Andrew J Tatem  1   11 , Justin M Cohen  3 , David L Smith  1   12   13   14
Affiliations

Mapping residual transmission for malaria elimination

Robert C Reiner et al. Elife. .

Abstract

Eliminating malaria from a defined region involves draining the endemic parasite reservoir and minimizing local malaria transmission around imported malaria infections . In the last phases of malaria elimination, as universal interventions reap diminishing marginal returns, national resources must become increasingly devoted to identifying where residual transmission is occurring. The needs for accurate measures of progress and practical advice about how to allocate scarce resources require new analytical methods to quantify fine-grained heterogeneity in malaria risk. Using routine national surveillance data from Swaziland (a sub-Saharan country on the verge of elimination), we estimated individual reproductive numbers. Fine-grained maps of reproductive numbers and local malaria importation rates were combined to show 'malariogenic potential', a first for malaria elimination. As countries approach elimination, these individual-based measures of transmission risk provide meaningful metrics for planning programmatic responses and prioritizing areas where interventions will contribute most to malaria elimination.

Keywords: ecology; epidemiology; global health; human; malaria elimination; plasmodium falciparum; spatio-temporal transmission dynamics.

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Conflict of interest statement

The authors declare that no competing interests exist.

Figures

Figure 1.
Figure 1.. Consensus network plot of causal links.
Panel A: Swaziland imported and local malaria cases (green squares and orange diamonds, respectively) are plotted spatially. Local case pairs identified as putative orphaned chains are indicated by red diamonds. A solitary local case also identified as an orphan is identified as a red diamond within a circle. Panel B: Swaziland imported (green line) and local (orange line) malaria cases are plotted in time, aggregated by month. Panel C: The final consensus network plot is displayed. Local cases are plotted as diamonds and imported cases as green circles. The color of each link corresponds to the “strength” of the connection as measured by the number of parameter sets where that link was identified as optimal. Imported cases that were not found to be the “most likely” parent of a local case are not displayed. DOI: http://dx.doi.org/10.7554/eLife.09520.003
Figure 2.
Figure 2.. Vulnerability, receptivity and malariogenic potential.
Panel A: Extrapolated Rc values for Swaziland using a zero-inflated negative binomial regression. Areas in orange to red indicate locations where Rc is greater than unity. The legend doubles as a histogram indicating the number of individuals (on a log10 scale) that live within each range of Rc values. Panel B: Extrapolated importation probabilities for Swaziland using a logistic regression. Panel C: Malariogenic potential for Swaziland calculated as the product of Rc and the probability of importation. DOI: http://dx.doi.org/10.7554/eLife.09520.004
Figure 3.
Figure 3.. Vulnerability, receptivity and malariogenic potential (2010-6/2012).
Panel A: Extrapolated Rc values for Swaziland using a zero-inflated negative binomial regression. Areas in orange to red indicate locations where Rc is greater than unity. The legend doubles as a histogram indicating the number of individuals (on a log10 scale) that live within each range of Rc values. Panel B: Extrapolated importation probabilities for Swaziland using a logistic regression. Panel C: Malariogenic potential for Swaziland calculated as the product of Rc and the probability of importation. DOI: http://dx.doi.org/10.7554/eLife.09520.005
Figure 4.
Figure 4.. Vulnerability, receptivity and malariogenic potential (7/2012-2014).
Panel A: Extrapolated Rc values for Swaziland using a zero-inflated negative binomial regression. Areas in orange to red indicate locations where Rc is greater than unity. The legend doubles as a histogram indicating the number of individuals (on a log10 scale) that live within each range of Rc values. Panel B: Extrapolated importation probabilities for Swaziland using a logistic regression. Panel C: Malariogenic potential for Swaziland calculated as the product of Rc and the probability of importation. DOI: http://dx.doi.org/10.7554/eLife.09520.006
Figure 5.
Figure 5.. Timing of ‘orphan’ cases.
The average number of cases per month and total occurrence of looped (or ‘orphaned’ cases) are plotted against month. DOI: http://dx.doi.org/10.7554/eLife.09520.007
Figure 6.
Figure 6.. Spatial covariates for malaria receptivity regression.
The four significant covariates for the malaria receptivity regression were (A) distance from paved roads, (B) distance from unpaved roads, (C) distance from feeder roads, and (D) distance from Mozambique. All distances were in meters. DOI: http://dx.doi.org/10.7554/eLife.09520.008
Figure 7.
Figure 7.. Spatial covariates for malaria importation regression.
The ten significant covariates for the malaria importation regression were (A) elevation, (B) population, (C) annual mean temperature (bio1 - http://www.worldclim.org/bioclim), (D) maximum temperature of the warmest month (bio5 - http://www.worldclim.org/bioclim), (E) minimum temperature of coldest month (bio6 - http://www.worldclim.org/bioclim), (F) precipitation of the wettest month (bio13 - http://www.worldclim.org/bioclim), (G) precipitation of driest month (bio14 - http://www.worldclim.org/bioclim), (H) TWI, (I) normalized difference vegetation index, and (J) enhanced vegetation index. DOI: http://dx.doi.org/10.7554/eLife.09520.010
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