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Additional file 1: Figures S1–S5. Description of data: Distributions of distance between pairs of study participants or between pairs of cases and all other study participants across the studies; maps of households and P. falciparum infections in the cross-sectional survey in Thailand; maps of households and P. vivax and P. falciparum infections in the second cross-sectional survey in Brazil; maps of households and P. vivax and P. falciparum infections in the villages of the cross-sectional survey in Solomon Islands; spatial signature of prevalence of P. vivax or P. falciparum infections in the cross-sectional survey in Solomon Islands across the full distance range. Figure S6. The spatial signature of prevalence of co-infections of P. vivax an P. falciparum infections in the cross-sectional survey in Cambodia across the full distance range (left) and within 1 km (right). Ribbon: 95%-quantile interval of null distribution. Horizontal line: Global survey prevalence.

We describe the MalariaGEN Pf7 data resource, the seventh release of Plasmodium falciparum genome variation data from the MalariaGEN network. It comprises over 20,000 samples from 82 partner studies in 33 countries, including several malaria endemic regions that were previously underrepresented. For the first time we include dried blood spot samples that were sequenced after selective whole genome amplification, necessitating new methods to genotype copy number variations. We identify a large number of newly emerging crt mutations in parts of Southeast Asia, and show examples of heterogeneities in patterns of drug resistance within Africa and within the Indian subcontinent. We describe the profile of variations in the C-terminal of the csp gene and relate this to the sequence used in the RTS,S and R21 malaria vaccines. Pf7 provides high-quality data on genotype calls for 6 million SNPs and short indels, analysis of large deletions that cause failure of rapid diagnostic tests, and systematic characterisation of six major drug resistance loci, all of which can be freely downloaded from the MalariaGEN website.

This report describes the MalariaGEN Pv4 dataset, a new release of curated genome variation data on 1,895 samples of Plasmodium vivax collected at 88 worldwide locations between 2001 and 2017. It includes 1,370 new samples contributed by MalariaGEN and VivaxGEN partner studies in addition to previously published samples from these and other sources. We provide genotype calls at over 4.5 million variable positions including over 3 million single nucleotide polymorphisms (SNPs), as well as short indels and tandem duplications. This enlarged dataset highlights major compartments of parasite population structure, with clear differentiation between Africa, Latin America, Oceania, Western Asia and different parts of Southeast Asia. Each sample has been classified for drug resistance to sulfadoxine, pyrimethamine and mefloquine based on known markers at the dhfr, dhps and mdr1 loci. The prevalence of all of these resistance markers was much higher in Southeast Asia and Oceania than elsewhere. This open resource of analysis-ready genome variation data from the MalariaGEN and VivaxGEN networks is driven by our collective goal to advance research into the complex biology of P. vivax and to accelerate genomic surveillance for malaria control and elimination.

Additional File 1: Fig S1. Schematic representation of the relationship between the population-level and individual-level parameters for the half-life of the short-lived ASCs. Fig S2. Total IgG kinetic profiles against 52 P. vivax antigens in 34 Thai patients with symptomatic P. vivax infections. Fig S3. Adjusted total IgG kinetic profiles at an individual level following symptomatic infections. Fig S4. Comparison of the adjusted peak IgG subclass magnitude between symptomatic Thai patients and the PNG hyperimmune pool. Fig S5. Comparison of the peak IgM magnitude between symptomatic Thai patients and malaria-free individuals. Fig S6. Unadjusted IgM kinetics against 15 P. vivax antigens in 34 Thai individuals following symptomatic P. vivax infections. Fig S7. Distribution of data for estimated half-lives of antibodies and ASCs against IgG-immunogenic antigens following symptomatic P. vivax infections in the Thai symptomatic cohort. Fig S8. Comparison of adjusted total IgG against 27 P. vivax antigens following symptomatic or asymptomatic P. vivax infections.

Additional file 5: Figure S5. Heat maps for (A) impact and (B) overtreatment with Primaquine in a real-life scenario after 1, 2 or 3 rounds of PvSeroTAT and under two transmission pressures.

Additional file 4: Figure S4. ROC surfaces for (A) impact and (B) overtreatment with Primaquine administered in a high-efficacy scenario after 1, 2 or 3 rounds of PvSeroTAT and under two transmission pressures.

Additional file 2: Table S2. Public health impact and overtreatment modeled in a moderate transmission setting (qPCR prevalence ~10%). Impact was defined as the reduction in qPCR prevalence observed six months after the last round of intervention; overtreatment corresponded to the administration of a hypnozoiticidal drug to a person whose last blood-stage infection occurred more than 9 months ago. The ���Best-case���, ���High efficacy��� and ���Real-life��� scenario correspond to those described in the main text, with decreasing adherence, efficacy and eligibility.

Additional file 3: Figure S3. Heat maps for (A) impact and (B) overtreatment with an optimal hypnozoiticidal drug as defined by the ���best-case��� scenario and after 1, 2 or 3 rounds of PvSeroTAT and under two transmission pressures.

Additional file 2: Table S1. Immunogenicity of total IgG against 52 P. vivax antigens at 1-week post-infection in Thai symptomatic volunteers. Table S2. P. vivax proteins used in this study. Table S3. Immunogenicity of total IgG against 27 IgG-immunogenic P. vivax antigens at 0-week since infection in Thai asymptomatic volunteers.

Additional file 1: Figure S1. SDS-PAGE (10%) visualisation of purified recombinant proteins used for magnetic bead coupling. Figure S2. Standard curve serial dilutions for plates used in this study of protein-magnetic bead couplings with the hyper-immune positive control pool of individuals from Papua New Guinea.

Additional file 1: Figure S1. Modeled P. vivax qPCR prevalence with various implementations of public health interventions in an endemic situation of moderate transmission. The columns correspond to the different scenarios and rows to the number of intervention rounds, with grey area presenting time of evaluation.

Additional file 1: Table S1. Notified cases and incidence per 1000 population per year, malaria type, highest transmission state of the Amazon Basin. Figure S1. Monthly P. vivax and P. falciparum cases. Figure S2. P. vivax and P. falciparum 2018 cases stratified by municipality. Figure S3. States reporting cases imported from Venezuela. Figure S4. Proportion of P. vivax and P. falciparum cases by age group per year in each State stratified by gender. Figure S5. Proportion of cases in the top 1% of municipalities and health units. Figure S6. Gini coefficient and population size.

Additional file 2. Amplification and melting curve of gametocyte assay. The amplification and melting curve was generated by the 7500 Fast Real-Time PCR (Applied Biosystem) software.

Additional file 1. Primer sequences and reaction conditions for 18S qPCR. The amplification and quantification of Pf- or Pv-specific 18S rRNA gene copies were conducted using SYBR-Green-based qPCR.

Additional file 1:.

Additional file 4. Correlation between pfs25/pvs25 transcript numbers and gametocyte density. A regression analysis was conducted to obtain the trendline for the relationship between transcript numbers and gametocyte density from samples with known gametocyte density.

Additional file 3. Performance of 18S qPCR and pfs25/pvs25 RT-qPCR. The amplification and melting curve was generated by the 7500 Fast Real-Time PCR (Applied Biosystem) software. Mean CT, CT standard deviation, PCR efficiency, and r2 were calculated by running serial dilutions of plasmid in quintuples.

Additional file 3. Allele frequencies of Pfmsp1, Pvmsp1f3 and PvMS2 alleles in both surveys. Allele frequencies A) Pfmsp1 alleles in 2005 (top) vs 2012/13 (bottom) of all alleles (left) and split into 3D7 (centre) and FC27 (right) allele families, and B) Pvmsp1f3 and PvMS2 in 2005 (top) vs 2012/13 (bottom).

Additional file 1. Definition of anaemia. Definitions of anaemia that were used in the analyses stratified by age and sex. Values represent haemoglobin levels in g/L. Adapted from [35].

Additional file 2: Figure S1. Overview of steps used to select informative SNPs from P. vivax whole genome sequence data. Figure S2. Overview of amplicon sequencing approach for multiple samples (96) to amplify all target SNP loci (178) in single sequencing run. Genomic loci were amplified using standard PCR (PCR#1) using locus specific primer with universal overhang adaptors (a). Using purified primary PCR product as a template an additional PCR, index PCR (PCR#2) (b) was performed to attach a multiplex identifier (MID) tag which is unique to each sample and attaches to universal overhang sequence. Unique sequence of MID for each sample enables pooling of secondary PCR products (96 samples in this protocol) for library preparation (c). Then high throughput “multiplexed” sequencing of the combined amplicons from all samples (96) in a single MiSeq run to produce require millions of paired-end reads (2x300bp) (d). e Data was analysed using standard bioinformatics tools and mapped reads were visualized using Integrative Genomics Viewer. The graph shows a large number of reads covering the target SNP locus. This result shows that all reads possess the alternative allele (green) at the SNP site. This high frequency of SNP calls amongst reads gives high confidence to differentiate true SNPs (indicated as SNPS at target locus) from sequencing artefacts (rare SNPs shown left and right side of target locus). Figure S3. Overview of bioinformatic data analysis pipeline. Data processing sequence read consisting of quality checking of raw sequence reads, primers and adaptor trimming, mapping reads to reference sequence, SNP calling and filtering, and population genetic and statistical analyses. Figure S4. Quality control of the Plasmodium vivax barcoding assay. a Assessment of amplification bias and SNP polymorphism among genotyped samples. Comparison of number of successfully amplified loci before and after rWGA of samples. There was no statistically significant difference in number of loci successfully genotyped before and after WGA, and between monoclonal and polyclonal samples. b Read coverage for all 20 SNP markers per sequenced sample. The graph shows that read count is different for different samples with high read count in samples with MOI = 2. Read count variation between amplified WGA) and unamplified samples (S) were not significant. c PCR and SNP genotyping success rate. From a total of 220 SNPs, 42 were negative and were not amplified in the primary multiplex PCR, and 32 failed during genotyping (either no reads detected or did not meet the quality filtering threshold). The remaining 146 were used as “SNP barcode” for downstream population genetic analysis (Additional file 3: Table S2). Figure S5. Distribution of the Minor allele frequency (MAF) of SNP barcodes in P. vivax parasite population in north coast PNG. Figure S6. Optimal number of clusters for each marker based Evanno’s method [42]. For SNP markers the method identified three genetic clusters (K = 3) and two genetic clusters (K = 2) for Microsatellites. However, for Microsatellites K = 2 is a common artifact of the hierarchical clustering algorithm when two very distinct populations are present, so higher K must be observed to identify possible sub-population structure. Figure S7. Pairwise allele sharing of P. vivax genotypes within and between population. a SNP genotype frequency distribution of pairwise allele-sharing (PS). b Microsatellite genotype frequency distribution of pairwise allele-sharing (PS). Black bars indicate within population and grey bars indicate between populations. There was no significant difference in allele sharing within and between parasite populations either SNP barcode or microsatellite markers in north coast of PNG. Figure S8. Kendall’s Tau concordance analysis of genotype allele sharing by SNP and Microsatellite. Each boxplot indicates the proportion of shared alleles between genotypes by SNP. The analysis revealed no significant correlation between the SNP and microsatellite pairwise allele sharing values.

Additional file 7. Self-reported net use.

Additional file 1. Sentinel sites description.

Additional file 5. Severe malaria in sentinel health facilities.

Additional file 4. Key environmental variables by site.

Additional file 3. Rapid diagnostic test and light microscopy results.

Additional file 8. Percentage of malaria cases treated with the previous first line treatment or partial treatment (mono-therapy), and with artemisinin-based combination therapy.

Additional file 6. Malaria incidence by age group and sex.

Additional file 2. Timeline of implementation of malaria control interventions and surveillance in each sentinel health facility, 2005–2014.

Measurement of malaria specific antibody responses represents a practical and informative method for malaria control programs to assess recent exposure to infection. Technical advances in recombinant antigen production, serological screening platforms, and analytical methods have enabled the identification of several target antigens for laboratory based and point-of-contact tests. Questions remain as to how these serological assays can best be integrated into malaria surveillance activities to inform programmatic decision-making. This report synthesizes discussions from a convening at Institut Pasteur in Paris in June 2017 aimed at defining practical and informative use cases for serology applications and highlights five programmatic uses for serological assays including: documenting the absence of transmission; stratification of transmission; measuring the effect of interventions; informing a decentralized immediate response; and testing and treating P. vivax hypnozoite carriers.

Additional file 1: Table S1. Study population and key outcomes by study site.

Additional file 2: Figure S1. Haemoglobin concentration.

Supplementary methods and analyses. (DOCX 142 kb)

Full list of recorded diagnoses for the outpatient surveillance sample. (DOCX 21Â kb)

Supporting information. Supplementary text, figures and tables. (DOCX 2111Â kb)

Background: The scale-up of effective malaria control in the last decade has resulted in a substantial decline in the incidence of clinical malaria in many countries. The effects on the proportions of asymptomatic and submicroscopic infections, and on transmission potential are yet poorly understood. Methods: In Papua New Guinea, vector control has been intensified since 2008, and improved diagnosis and treatment introduced in 2012. Cross-sectional surveys were conducted in Madang Province in 2006 (n=1280), 2010 (n=2117) and 2014 (n=2516). Infections were quantified by highly sensitive qPCR and gametocytes by RT-qPCR. Results: P. falciparum prevalence by qPCR decreased from 42% in 2006 to 9% in 2014. P. vivax prevalence decreased from 42% in 2006 to 13% in 2010, but then increased to 20% in 2014. Parasite densities decreased 5-fold from 2006 to 2010; 72% of P. falciparum and 87% of P. vivax infections were submicroscopic in 2014. Gametocyte density and positivity correlated closely with parasitemia, and population gametocyte prevalence decreased 3-fold for P. falciparum and 29% for P. vivax from 2010 to 2014. Conclusions: Sustained control has resulted in reduced transmission potential but an increasing proportion of gametocyte carriers are asymptomatic and submicroscopic and represent a challenge to malaria control.

Performance of qPCR and qRT-PCR. The threshold cycles (CT) are shown for detection of plasmid standards at different copy numbers per reaction. The means and the standard errors of the mean (SEM) are shown for CT values used to determine the amplification efficiency (E) and r 2, with values in parenthesis excluded. Neg indicates no amplification. The limit of detection (red) is defined as the lowest copy number with >â 50% success rate. (DOCX 24Â kb)

Data collection periods by study site. (DOCX 18Â kb)

Morbidity indicators in the general population before and after LLIN distribution. (DOCX 19Â kb)

Morbidity indicators in two sentinel health facilities before and after LLIN distribution. (DOCX 15Â kb)

Morbidity indicators in two sentinel health facilities before and after LLIN distribution. (DOCX 15Â kb)

Morbidity indicators in the general population before and after LLIN distribution. (DOCX 19Â kb)

Artemisinin resistant Plasmodium falciparum is advancing across Southeast Asia in a soft selective sweep involving at least 20 independent kelch13 mutations. In a large global survey, we find that kelch13 mutations which cause resistance in Southeast Asia are present at low frequency in Africa. We show that African kelch13 mutations have originated locally, and that kelch13 shows a normal variation pattern relative to other genes in Africa, whereas in Southeast Asia there is a great excess of non‐synonymous mutations, many of which cause radical amino‐acid changes. Thus, kelch13 is not currently undergoing strong selection in Africa, despite a deep reservoir of standing variation that could potentially allow resistance to emerge rapidly. The practical implications are that public health surveillance for artemisinin resistance should not rely on kelch13 data alone, and interventions to prevent resistance must account for local evolutionary conditions, shown by genomic epidemiology to differ greatly between geographical regions.

Improving the efficiency of malaria diagnosis is one of the main goals of current malaria research. We have recently developed a magneto-optical (MO) method which allows high-sensitivity detection of malaria pigment (hemozoin) crystals via their magnetically induced rotation in blood. Here, we validate this technique on laboratory derived blood samples infected with \textit{Plasmodium falciparum}. Using two parasite cultures, the first containing mostly ring stages and the second corresponding to the end of the parasite life cycle, we demonstrate that our novel method can detect parasite densities as low as $\sim$40 and $\sim$10\,parasites per microliter of blood for ring and schizont stage parasites, respectively. This detection limit exceeds the performance of rapid diagnostic tests and competes with the threshold achievable by light microscopic observation of blood smears. Our method can be performed with as little as 50\,microliter of capillary blood and is sensitive to the presence of hemozoin micro-crystals down to ppm concentrations. The device, designed to a portable format for clinical and in-field tests, requires no special training of the operator or specific reagents, except for an inexpensive lysis solution to release intracellular hemozoin. Beyond diagnostics, this technique may offer an efficient tool to study hemozoin formation, trace hemozoin kinetics in the body and test susceptibility/resistance of parasites to new antimalarial drugs inhibiting hemozoin formation.

People living in endemic areas often habour several malaria infections at once. High resolution genotyping can distinguish between infections by detecting the presence of different alleles at a polymorphic locus. However the number of infections may not be accurately counted since parasites from multiple infections may carry the same allele. We use simulation to determine the circumstances under which the number of observed genotypes are likely to be substantially less than the number of infections present and investigate the performance of two methods for estimating the numbers of infections from high resolution genotyping data. The simulations suggest that the problem is not substantial in most datasets: the disparity between the mean numbers of infections and of observed genotypes was small when there was 20 or more alleles 20 or more blood samples a mean number of infections of 6 or less and where the frequency of the most common allele was no greater than 20. The issue of multiple infections carrying the same allele is unlikely to be a major component of the errors in PCR based genotyping. Simulations also showed that with heterogeneity in allele frequencies the observed frequencies are not a good approximation of the true allele frequencies. The first method that we proposed to estimate the numbers of infections assumes that they are a good approximation and hence did poorly in the presence of heterogeneity. In contrast the second method by Li et al estimates both the numbers of infections and the true allele frequencies simultaneously and produced accurate estimates of the mean number of infections.

Additional file 2. Parameter estimates for serocatalytic models. Parameters are presented as posterior medians with 95% credible intervals. All parameters had improper uniform prior distributions.

Trap-nights and mosquitoes caught per trap and year in each of the seven villages for the most abundant Anopheles mosquito species. (DOCX 25 kb)

Performance of qPCR and qRT-PCR. The threshold cycles (CT) are shown for detection of plasmid standards at different copy numbers per reaction. The means and the standard errors of the mean (SEM) are shown for CT values used to determine the amplification efficiency (E) and r 2, with values in parenthesis excluded. Neg indicates no amplification. The limit of detection (red) is defined as the lowest copy number with >â 50% success rate. (DOCX 24Â kb)

Additional file 3: Table S1. Patient characteristics for the M2 migrant population*. Table S2. Patient characteristics for the M1 migrant population*. Table S3. Patient characteristics for the Thai national population. Table S4. Occupation distribution among the different population strata.

Additional file 2. Supplementary Figures.