Indoor residual spraying with a non-pyrethroid insecticide reduces the reservoir of <i>Plasmodium falciparum</i> in a high-transmission area in northern Ghana

High-malaria burden countries in sub-Saharan Africa are shifting from malaria control towards elimination. Hence, there is need to gain a contemporary understanding of how indoor residual spraying (IRS) with non-pyrethroid insecticides when combined with long-lasting insecticidal nets (LLINs) impregnated with pyrethroid insecticides, contribute to the efforts of National Malaria Control Programmes to interrupt transmission and reduce the reservoir of Plasmodium falciparum infections across all ages. Using an interrupted time-series study design, four age-stratified malariometric surveys, each of ~2,000 participants, were undertaken pre- and post-IRS in Bongo District, Ghana. Following the application of three-rounds of IRS, P. falciparum transmission intensity declined, as measured by a >90% reduction in the monthly entomological inoculation rate. This decline was accompanied by reductions in parasitological parameters, with participants of all ages being significantly less likely to harbor P. falciparum infections at the end of the wet season post-IRS (aOR = 0.22 [95% CI: 0.19–0.26], p-value < 0.001). In addition, multiplicity of infection (MOIvar) was measured using a parasite fingerprinting tool, designed to capture within-host genome diversity. At the end of the wet season post-IRS, the prevalence of multi-genome infections declined from 75.6% to 54.1%. This study demonstrates that in areas characterized by high seasonal malaria transmission, IRS in combination with LLINs can significantly reduce the reservoir of P. falciparum infection. Nonetheless despite this success, 41.6% of the population, especially older children and adolescents, still harboured multi-genome infections. Given the persistence of this diverse reservoir across all ages, these data highlight the importance of sustaining vector control in combination with targeted chemotherapy to move high-transmission settings towards pre-elimination. This study also points to the benefits of molecular surveillance to ensure that incremental achievements are not lost and that the goals advocated for in the WHO’s High Burden to High Impact strategy are realized

Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections.

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the varcoding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While varcoding can underestimate MOI, it allows robust estimation, especially under high transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low and moderate transmission. Regardless of transmission intensity, results for THE REAL McCOIL indicate that an inaccurate tail at high MOI values is generated, and that at high transmission, an apparently reasonable estimated MOI distribution can arise from some degree of compensation between overestimation and underestimation. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs

Reliability of the multiplicity of infection (MOI) estimations when THE REAL McCOIL approach using an upper bound for MOI of 4, 9, and 20 for the low-, moderate-, and high-transmission simulations, respectively.

For each category, the horizontal central solid line represents the median, the diamond represents the mean, the box represents the interquartile range (IQR) from the 25th to 75th centiles, the whiskers indicate the most extreme data point which is no more than 1.5 times the interquartile range from the box, and the dots show the outliers, i.e. the points beyond the whiskers. The upper, middle, and lower row panels correspond to simulations under low-, moderate-, and high-transmission settings, respectively (S1 and S2 Tables). A) Accuracy of MOI estimates, defined as the differences between estimated and true MOI per host. While null values highlight accurate MOI estimates (indicated by a dashed black horizontal line), the positive and negative values highlight over- and under-estimation, respectively. The estimated MOI using the var genes based approach (i.e. var coding) are indicated in blue, and the estimated MOI using the neutral SNPs based approach (i.e. THE REAL McCOIL) are indicated in green. The dark and light blue or green colors indicate respectively MOI estimates made without and with a measurement model (Fig 2). The column panels show differences for specific true MOI values. B) Population distribution of the estimated and true MOI per host from the simulated “true” values and those estimated with the methods indicated by the colors similar to panel A. (EPS)</p

Pearson correlation coefficients between the inaccuracy of the minor allele frequency (MAF) per locus estimated with THE REAL McCOIL approach (defined as the absolute differences between estimated and true MAF per locus), and the locus properties.

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Reliability of the multiplicity of infection (MOI) estimations when intervention changed the recent transmission intensities.

Only one combination of parameters (i.e. run) per transmission intensity (i.e. run 12, 36, and 60 for the low-, moderate-, and high transmission intensities, respectively) is illustrated. For each category, the horizontal central solid line represents the median, the diamond represents the mean, the box represents the interquartile range (IQR) from the 25th to 75th centiles, the whiskers indicate the most extreme data point which is no more than 1.5 times the interquartile range from the box, and the dots show the outliers, i.e. the points beyond the whiskers. The upper, middle, and lower row panels correspond to simulations under low-, moderate-, and high-transmission settings, respectively (S1 and S2 Tables). A) Accuracy of MOI estimates, defined as the difference between estimated and true MOI per host. While null values highlight accurate MOI estimates (indicated by a dashed black horizontal line), the positive and negative values highlight over- and under-estimation, respectively. Estimates with the neutral SNP-based approach (THE REAL McCOIL) are indicated in green, and those with the var gene-based approach (varcoding) are indicated in blue. The dark and light green or blue colors indicate respectively MOI estimations made without and with a measurement model (Fig 2). The column panels show differences for specific true MOI values. B) Population distribution of the estimated and true MOI per host from the simulated “true” values and those estimated with the methods indicated by the colors similar to panel A. (EPS)</p

Pairwise type sharing (PTS).

For each category, the horizontal central solid line represents the median, the diamond represents the mean, the box represents the interquartile range (IQR) from the 25th to 75th centiles, and the whiskers indicate the most extreme data point. A) Distribution of the PTS per transmission intensity. B) Distribution of the PTS per run. (EPS)</p

Multiplicity of infection (MOI).

For each category, the horizontal central solid line represents the median, the diamond represents the mean, the box represents the interquartile range (IQR) from the 25th to 75th centiles, the whiskers indicate the most extreme data point, which is no more than 1.5 times the interquartile range from the box, and the dots show the outliers, i.e., the points beyond the whiskers. The upper, middle, and lower row panels show correspond to simulations under low-, moderate-, and high-transmission settings, respectively (S1 and S2 Tables). A) Accuracy of MOI estimates, defined as the difference between estimated and true MOI per host. While null values highlight accurate MOI estimates (indicated by a dashed black horizontal line), the positive and negative values highlight over- and under-estimation, respectively. Estimates with the neutral SNP-based approach (THE REAL McCOIL) are indicated in green, and those with the var gene-based approach (varcoding) are indicated in blue. The dark and light green or blue colors indicate respectively MOI estimations made without and with a measurement model (Fig 2). The column panels show differences for specific true MOI values. B) Population distribution of the estimated and true MOI per host from the simulated “true” values and those estimated with the methods indicated by the colors similar to panel A. For high transmission, the distribution obtained with THE REAL McCOIL shows a more pronounced tail than that from the simulated infections, with a secondary peak around MOI = 14. Note that the method considerably over-estimates individual MOI below that value but then under-estimates above it (panel A). Thus, these opposite trends compensate each other to some extent in the population distribution, producing nevertheless a deviation at high values. The varcoding method provides a good representation of the “true” distribution from the simulations, and of the individual values in general, with a consistent tendency to underestimate when sampling error is taken into account.</p

Proportion of SNP calls (genotypes) per host SNP haplotype.

A) Single major allele calls; B) Double allele calls (DACs); C) Single minor allele calls; D) Missing allele calls. Each SNP call proportion was calculated using the low-, moderate-, and high-transmission setting simulations (S1 and S2 Tables). The column panels show the proportions for specific true MOI values. The dark and light green colors indicate the proportion of calls made without and with a measurement model, respectively (Fig 2). For each category, the horizontal central solid line represents the median, the diamond represents the mean, the box represents the interquartile range (IQR) from the 25th to 75th centiles, the whiskers indicate the most extreme data point, which is no more than 1.5 times the interquartile range from the box, and the dots show the outliers, i.e., the points beyond the whiskers.</p

Prevalence and entomological inoculation rate (EIR) per transmission intensity.

For each category, the horizontal central solid line represents the median, the diamond represents the mean, the box represents the interquartile range (IQR) from the 25th to 75th centiles, the whiskers indicate the most extreme data point which is no more than 1.5 times the interquartile range from the box, and the dots show the outliers, i.e. the points beyond the whiskers. A) Prevalence; B) EIR. Statistics calculated for simulations under low-, moderate-, and high-transmission settings are indicated in yellow, orange, and purple, respectively (S1 and S2 Tables). (EPS)</p

Reliability of the multiplicity of infection (MOI) estimations when hosts are sampled through several time-points during the malaria season.

Only one combination of parameters (i.e. run) per transmission intensity (i.e. run 12, 36, and 60 for the low-, moderate-, and high transmission intensities, respectively) is illustrated. For each category, the horizontal central solid line represents the median, the diamond represents the mean, the box represents the interquartile range (IQR) from the 25th to 75th centiles, the whiskers indicate the most extreme data point which is no more than 1.5 times the interquartile range from the box, and the dots show the outliers, i.e. the points beyond the whiskers. The upper, middle, and lower row panels correspond to simulations under low-, moderate-, and high-transmission settings, respectively (S1 and S2 Tables). A) Accuracy of MOI estimates, defined as the difference between estimated and true MOI per host. While null values highlight accurate MOI estimates (indicated by a dashed black horizontal line), the positive and negative values highlight over- and under-estimation, respectively. Estimates with the neutral SNP-based approach (THE REAL McCOIL) are indicated in green, and those with the var gene-based approach (varcoding) are indicated in blue. The dark and light green or blue colors indicate respectively MOI estimations made without and with a measurement model (Fig 2). The column panels show differences for specific true MOI values. B) Population distribution of the estimated and true MOI per host from the simulated “true” values and those estimated with the methods indicated by the colors similar to panel A. (EPS)</p