Sensitivity of effect sizes to changes in the underlying parameters is very different.

<p>Effect sizes are linearly proportional to mosquito density (<i>m</i>), infectivity (<i>b,c</i>), and the duration of the infectious period (<i>1/r</i>), quadratically proportional to human feeding (<i>a</i>), and approximately cubically proportional to mosquito survival (<i>g</i>) depending on the duration of latency in the mosquito (<i>v</i>).</p

Alignment of notation.

<p>Each version of the Ross-Macdonald model used different parameter names for the same or very similar quantities. This table aligns all of those names. The common notation is defined in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002588#ppat-1002588-box002" target="_blank">Box 2</a>. Differences in the parameter interpretations described in the separate boxes.</p

The Ross-Macdonald theory of transmission dynamics.

<p>(Top left) In a hypothetical location, for a fixed value of <i>R₀</i> (plotted here for <i>R₀</i> = 5), the model describes changes in the proportion of infected humans or infectious mosquitoes during an epidemic. (Top right) Alternatively, the models predict the endemic parasite rate or sporozoite rate as a function of <i>R₀</i>. Malaria is not endemic if <i>R₀</i><1, or after control, if <i>R_C</i><1, or equivalently, if mosquito density is below a critical threshold. (Bottom left) The model also describes changes in the parasite rate with respect to age (e.g., in a cross-sectional study) in infants or others who were previously unexposed to malaria. (Bottom right) Finally, the models also predict the response timelines and endpoints following the implementation of control (grey).</p

The Ross-Macdonald theory of control.

<p>(Top left) A relationship exists between the length of a mosquito feeding cycle (2, 3, or 5 days in blue, black, or red), the proportion of parous mosquitoes (denoted <i>O</i>), and the mosquito lifespan (denoted <i>1/g</i>). (Top right) This relationship can be used to measure predicted changes in the mosquito lifespan (<i>Δg⁻¹</i>) through estimated proportional changes in the proportion parous, which are invariant to the mosquito blood feeding rate (<i>ΔO/O</i>). (Bottom left) These changes can be translated into an effect size on transmission, a proportional change in reproductive numbers (<i>R₀/R_C</i>). (Bottom right) Finally, these can be translated into changes in the endemic parasite rate for a given effect size: <i>R_C</i> = <i>R₀</i>/2.5 (dashed) or <i>R₀</i>/5 (dotted).</p

Index of severity risk from G6PDd.

<p>(A) shows the national score of variant severity, determined by the ratio of class II to class III variant occurrences reported from each country; (B) maps the risk index from G6PDd, accounting for both the severity of variants (A) and the overall prevalence of G6PDd (<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001339#pmed-1001339-g003" target="_blank">Figure 3A</a>); the scoring matrix describing these scores is given in (C), specifying the different categories of risk determined by the scores of national-level prevalence of phenotypic deficiency (rows) multiplied by severity scores of the variants present (columns). (D) represents the uncertainty in the assembly of the risk index based on the prevalence scores (E rows) and in the assessment of variant severity (E columns). These uncertainties relate specifically to the analysis of these data into the risk index, and do not account for the underlying uncertainty in their interpretation in relation to haemolysis (see <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001339#s4" target="_blank">Discussion</a>).</p

G6PDd allele frequency and G6PDd population estimates across malaria endemic countries (<i>n</i> = 99) and the subset of malaria eliminating countries (<i>n</i> = 35).

<p>All figures are in thousands. Q25 and Q75 refer to the low and high limits of the IQR of the model predictions. Numbers in brackets represent the Monte Carlo standard error (SE) of the estimates; presented in the same units as the associated estimate. Full explanations are given in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001339#pmed.1001339.s005" target="_blank">Protocol S4</a>.</p>a<p>Total regional male population: 2,736,515; Total regional female population: 2,644,975. Source: GRUMP-adjusted projected UN 2010 population estimates and sex-ratio data from UN World Population Prospects 2010 Revision.</p>b<p>Total regional male population: 1,156,300; Total regional female population: 1,105,603. Source: GRUMP-adjusted projected UN 2010 population estimates and sex-ratio data from UN World Population Prospects 2010 Revision.</p>c<p>Figures derived from the allele frequency estimates so do not have specific model-derived uncertainty metrics.</p><p>n/a, not available.</p

The global distribution of G6PDd.

<p>(A) shows the global assembly of G6PDd community surveys included in the model dataset; data points are coloured according to the reported prevalence of deficiency in males (<i>n</i> = 1,720). Background map colour indicates the national malaria status (malaria free/malaria endemic/malaria eliminating). (B) is the median predicted allele frequency map of G6PDd. (C) presents the associated prediction uncertainty metrics (IQR); highest uncertainty is shown in red and indicates where predictions are least precise.</p

P. falciparum and P. vivax matched incidence and prevalence database

Database of 1680 records and 61 fields. Incidence data obtained by active case detection are space-time matched to measures of prevalence. Data were curated from PubMed literature searches. Additional prevalence data were obtained from the Malaria Atlas Project (map.ox.ac.uk) database and modeled Plasmodium falciparum and Plasmodium vivax mapped endemicity surfaces. Age standardization was done using the ageStand package in the statistical software platform R

Composite map of the evidence of <i>Pv</i> transmission in sub-Saharan Africa, summarised to the Admin1 unit.

<p>Composite map of the evidence of <i>Pv</i> transmission in sub-Saharan Africa, summarised to the Admin1 unit.</p

Weighting of the different data types at Admin1 level.

<p>Ranks refer to the relative strength of the evidence as an indicator of ongoing <i>Pv</i> transmission in an area. *The total number of returning traveller infections from each country was divided between the number of Admin1 regions per country. If there was less than 1 reported infection per Admin1, the weighting score was 0.5. Note that because the traveller infections data were retrospective and not sub-nationally specific, it was not possible to distinguish the Republic of Sudan from South Sudan, thus results were considered for Sudan pre-separation and the same score allocated to both countries based on the overall data.</p