Additional file 1: of Modelling population-level impact to inform target product profiles for childhood malaria vaccines

Supplementary results. (DOCX 1310 kb

Summary of PMI support and estimated impact for 19 focus countries (2013–2015).

<p>Summary of PMI support and estimated impact for 19 focus countries (2013–2015).</p

Schematic of the modelling process.

<p>Data inputs and sources (left column) are combined and linked to estimate the contribution of PMI and the impact of funding cuts on national-level intervention coverage (middle column). These estimates are then used as inputs in a dynamic transmission model to estimate the impact of changes in intervention coverage on epidemiological outcomes (right column). ACT, artemisinin combination therapy; DALY, Disability Adjusted Life Year; DHS, Demographic and Health Survey; IRS, indoor residual spraying; LLIN, long-lasting insecticide treated net; MICS, Multiple Indicator Cluster Surveys; NMCP, National Malaria Control Programme; PMI, President’s Malaria Initiative.</p

The health-system benefits associated with PMI funding.

<p>PMI investment in malaria interventions reduces caseloads of national health systems with resulting (A) averted spending due to reduced treatments of clinical and severe cases by country. Without PMI investment, these health system gains are lost, potentially resulting in (B) the estimated cumulative malaria-related deaths in addition to those caused directly by removal of interventions due to health systems not being able to respond to increased caseloads. PMI, President’s Malaria Initiative.</p

Map of PMI activities.

<p>Individual countries and regions that have received PMI-funding and support are highlighted to reflect the level of funding from PMI in (A) sub-Saharan Africa and (B) the GMS over the period 2013–2015. The total regional assignment to the 6 GMS countries over this period is US$9.5 million. Estimated funding per population at risk over this period ranged from US$0.54 (Myanmar) to US$8.08 (Liberia). GMS, Greater Mekong Subregion; PMI, President’s Malaria Initiative.</p

L'Écho : grand quotidien d'information du Centre Ouest

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Dispersal kernel comparison.

<p>A comparison of the A) distance pdf and B) density with respect to distance for estimates using MRR data from Brazil (solid blue line) and Malaysia (dashed pink line). The comparison highlights the similarity in estimated kernels for experiments conducted on different continents, in different habitats.</p

Summary data of the three MRR experiments in Brazil.

<p>Summary data of the three MRR experiments in Brazil.</p

Dispersal kernels.

<p>Examples of different kernel interpretations for the negative exponential (A, B and C) and exponential power (D, E and F) kernels. The distance pdf is shown in panels A and D. The density with respect to distance is shown in panels B and E and the density pdf is illustrated in panels C and F (after Cousens <i>et al</i>. [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004156#pntd.0004156.ref025" target="_blank">25</a>]). Kernels in A, D, C and F integrate to unity (in 1 dimension for the distance pdfs and 2 dimensions for the density pdfs).</p

Brazil model coefficient estimates.

<p>GLM coefficient estimates and associated standard errors, z-value and p-values from the optimal model for the Brazil analysis. Distance was transformed using the exponential power kernel.</p><p>*Overall significance level p<0.0001 (χ² = 46.50, 3df).</p><p>Brazil model coefficient estimates.</p