Plasmodium falciparum continues to be the main target for malaria elimination, other Plasmodium species persist in Africa. Their clinical diagnosis is uncommon, whereas rapid diagnostic tests (RDTs), the most widely used malaria diagnostic tools, are only able to distinguish between P. falciparum and non-falciparum species, the latter as “pan-species.” Blood samples from health facilities were collected in southern Nigeria  in 2017 (October–December) and Calabar only in 2018 (October–November), and analyzed by several methods, namely, microscopy, quantitative real-time PCR (qPCR), and peptide serology targeting candidate antigens . Both microscopy and qPCR diagnostic approaches detected comparable proportions (∼80%) of all RDT-positive samples infected with the dominant P. falciparum malaria parasite. However, higher proportions of non-falciparum species were detected by qPCR than microscopy, 10% against 3% infections for P. malariae and 3% against 0% for Plasmodium ovale, respectively. No Plasmodium vivax infection was detected. Infection rates for P. malariae varied between age-groups, with the highest rates in individuals aged > 5 years. Plasmodium malariae–specific seroprevalence rates fluctuated in those aged < 10 years but generally reached the peak around 20 years of age for all peptides. The heterogeneity and rates of these non-falciparum species call for increased specific diagnosis and targeting by elimination strategies.Although  Plasmodium falciparum and Plasmodium vivax.1 However, there are three other malaria species infecting humans, with Plasmodium malariae being the most common worldwide, including sub-Saharan Africa (sSA).2Plasmodium malariae causes the so-called quartan malaria, as the onset of fever occurs in an interval of 3–4 days, because of its 72-hour blood stage life cycle.4 Compared with falciparum malaria, it is a relatively benign infection, possibly due to low parasite densities, although it can cause severe anemia5 and nephrotic syndrome in both children and adults.7Plasmodium malariae is often found in coinfections with other Plasmodium species,3 and because of this and the focus on P. falciparum, its biology, prevalence, and specific public health impact have mostly been understudied. Previous studies in areas of seasonal transmission, such as the Gambia, have reported P. malariae to be responsible for about half of the malaria episodes observed outside the transmission season.8 The implications of this dynamics are poorly understood in terms of sustained transmission, acquisition of antimalarial drug resistance, and malaria elimination.Current global efforts toward malaria elimination target the dominant malaria parasites, P. malariae is uncommon in routine malaria diagnosis; rapid diagnostic tests (RDTs), the most widely used diagnostic tools, predominantly target P. falciparum, whereas other Plasmodium species are identified as “pan-species.”9 As a result, its burden and the population at risk in most endemic areas remain largely unknown. A recent study reported persistent transmission of P. malariae over a 22-year period in Tanzania, suggesting that the decline in P. falciparum prevalence could provide a favorable ecological niche for other malaria parasite species.10 This has also recently been reported for Plasmodium knowlesi, a zoonotic malaria species, in Malaysia.11Species-specific diagnosis of 1 Malaria transmission in Nigeria is perennial, mostly due to conducive geographic landscape, high temperatures, and rainfall; about 85% of the population lives in areas of mesoendemic transmission.12 Although P. falciparum is the dominant species, responsible for more than 95% of cases of clinical malaria, P. malariae is found in 9.8% of malaria cases, mostly as mixed infection.12 Nevertheless, its prevalence, mostly determined by molecular methods, can be as high as 26%.13 Here, we report the detection of P. malariae infections from two sampling surveys aimed to describe the molecular and seroprevalence of this malaria parasite species in individuals attending health facilities in two geopolitical and climatic zones of southern Nigeria.There were an estimated 228 million cases of malaria worldwide in 2018, 93% in Africa, with Nigeria contributing approximately 25% of these.P. malariae infections in two study sites in southern Nigeria, Lagos  in the southwest zone and Calabar, Cross River state  in the southeast zone , a second survey was carried out in Calabar only because of the higher prevalence of P. malariae, expanding to additional health facilities to increase catchment, and the total numbers screened within the sample collection period  and a short dry season covering the remaining 2 months. Temperatures are relatively constant throughout the year, with average high temperatures ranging between 25°C and 28°C; relative humidity is high with most months of the year, recording a monthly mean value of 80%, and annual average precipitation more than 3,000 mm (120 in).16Lagos is the most populous city in Africa, with a population of approximately 15 million people; it has a tropical savanna climate, with a wet season between April and October, and a dry season in the following months. The wettest month is June, with mean precipitation of 315.5 mm (12.42 in). Located near the equator, Lagos has minimal seasonal temperature variation, with high temperatures ranging between 28.3°C and 32.9°C.Ethical approval was obtained from the respective authorities in Lagos and Cross River state, references: IRB/17/038, CRSMOH/RP/REC/2017/545, and CRSMOH/RP/REC/2017/809. Written informed consent was obtained for adult participants, whereas parental or guardian consent was obtained for minors. Participants did not receive any compensation for their participation in study. All individuals diagnosed with malaria by RDTs were treated accordingly, whereas febrile individuals with a negative RDT result were referred to the health facility staff for further investigation. A minimum of 500 samples per site was required to be screened in each survey, assuming maximum malaria prevalence of 50% in suspected cases with 95% CI and 5% margin of error and an additional 30% adjustment for possible incomplete data by any of the detection methods.P. falciparum and lactate dehydrogenase (pLDH) of Plasmodium species in human whole blood, was used for malaria screening according to the manufacturers’ protocol. Any sample with either P.f, pan, or both lines in addition to the control line was considered positive. Thick blood microscopy slides stained with 10% Giemsa solution were examined across 100 high-power fields by two microscopists and parasite counts recorded per number of white blood cells (WBCs) observed. In case of ≥ 20% variance in counts between primary and secondary microscopists, the counts closest to that of a third reader were retained. A slide was declared negative only after observing no parasite in 100 microscopic high-power fields, whereas parasite density was calculated using WHO protocols; the number of parasitized erythrocytes against 8,000 WBCs17 was expressed as parasitaemia/mL of blood.A pan-species RDT kit , which qualitatively and differentially detects histidine-rich protein II antigen of Plasmodium spp., DNA was extracted from DBS samples using the QIAamp DNA Mini Kit  according to manufacturers’ protocols. For the preliminary survey, the presence of Plasmodium species was detected using a commercial species-specific quantitative real-time PCR (qPCR) assay, Genesig®Plasmodium species kits  according to manufacturers’ protocol. Subsequently, the presence of P. malariae and Plasmodium ovale was detected using a custom qPCR assay targeting the Plasmepsin gene,18 which showed comparable results with the Genesig kit (Primerdesign Ltd.), whereas the presence of P. vivax was not assessed further. Pre-amplification was performed using 0.1 μM of the forward and reverse primers for P. malariae  and P. ovale , 1× Qiagen Multiplex Master Mix (QIAGEN) and 5 μL of template DNA in a 15-μL reaction. The main amplification reaction contained 3 µL of pre-amplified products in a 15-µL total reaction volume and was performed using 1× qPCR Taqman Universal Master Mix , 0.4 μM of the forward and reverse primers, and 0.2 μM of the labeled probe . Pre-amplification was performed under the following conditions: initial denaturation for 5 minutes at 95°C, 12 cycles of denaturation for 30 seconds at 95°C, annealing at 60°C for 1 minute, and extension for 90 seconds at 72°C, whereas main amplification and detection were performed under the following conditions: initial denaturation for 10 minutes at 95°C, 40 cycles of denaturation for 10 seconds at 95°C, and data collection for 1 minute at 60°C. Plasmodium falciparum was detected using a highly sensitive qPCR assay (varATS qPCR) targeting multi-copy subtelomeric sequences.19 Amplification reaction was performed using 1× qPCR Taqman Universal Master Mix (Thermo Fischer Scientific).For molecular diagnosis of Plasmodium malariae–specific peptides were designed from highly immunogenic epitopes of three P. malariae antigens—apical membrane antigen (AMA1), circumsporozoite surface protein (CSP), and lactose dehydrogenase (LDH)  peptides to explore species specificity. A web tool, SVMTriP ,20 was used to predict linear antigenic epitopes from the whole P. malariae antigen sequences, and the peptides were queried for species specificity using NCBI BLASTP 2.9.0+ . The regions of immunogenic epitopes containing the peptides were then compared with orthologs of predicted P. falciparum epitopes available on the Plasmodium database21 and the Immune Epitope Database and Resource Analysis.22 The peptides indicating high species specificity and high immunogenicity were chosen for synthesis  at 95% purity and 1–4 mg synthesis scale.se (LDH) . A P. faPlasmodium spp. peptides were detected by indirect ELISA on eluted filter paper samples. One disc (6 mm) per sample was punched and placed at the bottom of 96-well plates—flat bottom with low evaporation lids . Serum from DBS was eluted following overnight (18 hours) incubation at 4°C in 150 µL of reconstitution buffer . The reconstituted solution was equivalent to a 1:100 dilution of whole blood, corresponding to 1:200 dilution of plasma or serum antibodies, if the blood were at 50% hematocrit.23 Peptides were coated on 96-well Nunc MaxiSorp™ Flat-Bottom Plates (Thermo Fischer Scientific) at a concentration of 3 µg/mL. Following blocking of all remaining binding surfaces with 1% skimmed milk, DBS eluate was added at a final concentration of 1:2,000 with respect to the corresponding plasma sample and analyzed in duplicate wells per sample. Each ELISA plate included a positive control, which was a pool of DBS eluate from samples confirmed to be P. malariae positive by microscopy and qPCR. Also included in each plate were plate blanks (no serum) and negative control, which was unexposed European pooled serum.24 Secondary detection was with antibody peroxidase conjugated human IgG (H&L) , developed with Pierce™ TMB Substrate Kit (Thermo Fischer Scientific). The reaction was stopped using 50 μL of Stop Solution for TMB substrates (Thermo Fischer Scientific). Optical densities (ODs) were read on the EMax® Plus microplate reader  at 450 nm.Antibodies against the P. malariae and non-falciparum species detected by the different detection methods was compared from the different study sites. Analytical procedures for reducing between-plate variation in the serology included calculating the differences between the mean ODs averaged across the duplicates of the pooled positive control for each plate and the overall mean OD values across all plates, and obtaining the estimated plate effect for a specific plate by averaging the calculated differences across all pooled controls. Test samples on a given plate were then adjusted by subtracting the estimated plate effect from their OD values.25 Positivity for antimalarial peptide response was obtained by modeling their normalized fold over the negative control (FOC) values (log2 transformed), using finite mixture models, and Gaussian distribution with two components.24 The estimated mean of the narrower distribution was used as the mean for negative samples. The cutoff for seropositivity was obtained by taking the difference between the peptide FOC values and the mean of the narrower distribution plus 2 SDs. Seroprevalence rates were analyzed according to the following age-groups: 10–20 years, 20–30 years, and > 30 years, which corresponded with estimated the duration of exposure to malaria. A three exponent nonlinear regression model and line of best fit, using the quadratic estimation, was used to obtain the predicted probability of being seropositive for a particular peptide.Data analysis was conducted using Microsoft Excel spreadsheet, GraphPad Prism version 8.4.2 , and Stata IC 16 . The prevalence of In 2017, a total of 243 (21.7%) individuals with a positive malaria RDT result were recruited of 1,115 participants screened—520 screened from Lagos and 595 from Calabar. Rapid diagnostic test positivity rate in Lagos was 13.7% and 28.9% in Calabar . Most ofPlasmodium species detected in Lagos by qPCR was 81.7%, 1.4%, and 1.4% for P. falciparum, P. malariae, and P. ovale, respectively. In Calabar, the proportions detected were 80.8%, 9.9%, and 5.8%, respectively  of P. falciparum infections were detected by qPCR and microscopy in the RDT-positive samples from the different study areas. However, more P. malariae and P. ovale infections were detected by qPCR, with higher proportions in Calabar than in Lagos. This emphasized the low specificity of species-specific identification of malaria parasites by microscopy. Plasmodium spp. detected as mixed or mono infections in the four health facilities in Calabar. On average, relatively more non-falciparum species infections were detected in malaria samples recruited from the two facilities situated to the north of Akpabuyo local government area (26.5%) than those to the south (18.7%); however, this was not statistically significant (P = 0.062) , the odds of having P. malariae as a mono-infection were highest in the < 5 year age-group. The least odds for mono-infections were in those of ages 11–15 years  . Despite15 years ; the oddnfection  and 4.Plasmodium spp. peptides designed, and these were relatively higher than the median values of a pool of nonimmune sera, reported as FOC. More than 70% of the samples showed antibody reactivity levels greater than the negative control for each of the peptides . From the area under the receiver operator curve for the different peptides, the FOC responses of all the peptides could predict up to 70% of the results obtained by microscopy (range = 62.6–70.5%) and RDTs (range = 66.4–71.9%) . Because of the small number of samples from Lagos and some of the health facilities in Calabar, all samples were subsequently analyzed together, and the geographical location was not considered. As observed with P. falciparum, seroprevalence increased with age for all the P. malariae peptides from the age of 10 years . The proportion of seropositivity decreased for PmLDH-1, whereas it increased for PfAMA1 for this younger age-group.Anti-peptide IgG antibody levels were detected against each of the 4–71.9%) . The P. 10 years . Proportfalciparum species was relatively heterogeneous in health facilities in the southwest and south–south zones of Nigeria, supporting the need for broadening the focus of the National Malaria Control Program beyond P. falciparum.29 Nigeria accounts for an estimated 25% of global malaria cases, mostly considered to be due to P. falciparum infection.30 With the push for malaria eradication, this indicates that other human Plasmodium species should also be considered in current elimination strategies and evaluation of intervention outcomes. Unlike P. falciparum, P. malariae infections were more prevalent in individuals older than 5 years. Current malaria intervention strategies such as seasonal malaria chemoprevention (SMC) usually target children aged 3–59 months. Hence, older age-groups in P. malariae–endemic areas could serve as a reservoir for future expansion of this parasite if P. falciparum is selectively eliminated. Higher prevalence of P. malariae infections was also observed in older age-groups in Papua New Guinea based on a sensitive molecular diagnostic method, post-PCR ligase detection reaction–fluorescent microsphere assay, compared with light microscopy.31 Although overall malaria parasite species differ, the rates for P. malariae infections were comparable to those reported for Papua, despite the wide geographic distance and additional prevalence of P. vivax in Papua. A wider surveillance and reporting of non-falciparum malaria parasite species and broadening of future malaria elimination strategies to include all population age-groups at risk of infection with P. falciparum and non-falciparum species is suggested. Identification of five cases of P. vivax and a 10.9% prevalence of P. malariae in a recent study of southern Nigerian communities further corroborates our findings and need for enlarged surveys.29The prevalence of non-P. malariae mono-infections were higher in this age-group than in individuals aged > 15 years. This could reflect the level of exposure and immunity, as younger children may be protected by maternal antibodies as well as interventions such as insecticide-treated bed nets and SMC.32 The age distribution also mirrors the pattern seen for P. falciparum, where an age shift in high infection rates to older children has been reported across Africa following large scale-up of malaria interventions.33 The odds of P. malariae mono-infection also varied between health facilities, with lowest infection odds in Ifondo, which had, by molecular methods, the highest prevalence of P. falciparum infections. This, however, would require further investigation because of the small numbers reported here, reflected in the wide CIs. Nonetheless, some studies have reported higher P. malariae prevalence rates when outside the P. falciparum peak transmission, suggesting an interaction between the occurrence of these species.36 Such dynamics could be driven by co-immune mechanisms as the genomes and proteins of these species are highly similar,37 hypothetically allowing for high immune responses against P. falciparum to eliminate P. malariae from coinfections. There could also be an ecological dynamics in which higher multiplication rates and shorter cycle time of P. falciparum would displace P. malariae, which often occurs at low parasite densities and prefer to invade older red blood cells (RBCs).4 Infected RBCs are cleared by the spleen during malaria infections,39 which may in turn remove P. malariae from peripheral circulation. However, P. malariae has been reported to persist following treatment of mixed species malaria with artemisinin combination therapies,42 an indication that further studies are needed to elucidate the dynamics and consequences of malaria parasite coinfections. This will require specific surveillance tools, including sensitive and specific diagnostic and serological assays.Despite the lower prevalence of infection in children < 5 years of age, the odds of P. malariae and other non-falciparum infections, species-specific peptide ELISA assays provided an alternative tool for determining seroprevalence of antibodies against P. malariae. Predicted peptide epitopes from P. malariae AMA1, CSP, and LDH proteins were all recognized by human IgG antibodies, evidence of specific immune responses in exposed communities. Higher relative antibodies were seen in the south–south zone with higher malaria transmission. Thus P. malariae elicits specific antibody responses, which could be targeted for the development of sensitive surveillance tools. Seroprevalence has been used as a surrogate for exposure, enabling the determination of force of infection and age-specific immunity for P. falciparum and P. vivax, in endemic regions.45 ELISA techniques for the detection of antibodies have also been widely used for the diagnosis of infectious diseases and have been used in malaria since the 1970s.46 Adequately powered studies to compare and predict transmission intensity of P. malariae and other non-falciparum species across sSA would be useful for strategizing toward control and elimination of all malaria parasite species.In the absence of whole antigen ELISA assays for falciparum species in Lagos than in Calabar. This was not surprising as urbanization in Lagos metropolitan city, with better housing and access to health services, will limit malaria transmission despite similar vector populations.47 A consistent pattern of increased urbanization coincident with decreasing malaria transmission and elimination over the past century has been reported.48 Although this study was not designed to determine the epidemiology of P. malariae, a properly designed epidemiological study would be more informative. The non-falciparum infections in the urban area were submicroscopic and could only be detected by sensitive molecular methods. This emphasizes the challenges to malaria diagnosis with decreased prevalence and more submicroscopic infections. Sensitive and accessible diagnostic tools for routine detection of all human Plasmodium species therefore remain important. Microscopy, which is still considered the gold standard for malaria diagnosis, is laborious and time consuming for Plasmodium speciation, requiring expert microscopists. Current antigen-based pan-species RDTs also present peculiar challenges, including low sensitivity and lack of species specificity.49 The poor sensitivity for pan-species Plasmodium detection was recently demonstrated in an experimental human blood-stage model for P. malariae infection, where RDTs targeting pan-genus LDH enzyme (pLDH) remained negative despite the presence of symptoms consistent with malaria 72-hour before testing.50The first survey carried out in 2017 showed a lower prevalence of non-Plasmodium species detected by microscopy compared with PCR. As for P. falciparum, ultrasensitive molecular diagnostics that target multi-copy genes will be relevant to assess the true burden of non-falciparum malaria infections.Most of the malaria infections detected by PCR were low-grade infections or submicroscopic, which is a well-documented limitation of microscopy, and this may explain the high discrepancy between P. vivax detection in Duffy-negative individuals in sSA, including southwestern Nigeria,51 no P. vivax infection was detected. The prevalence of P. vivax in Duffy-negative individuals could therefore be specific to some susceptible populations driven by yet undetermined biological and environmental factors.Despite recent reports of falciparum species reported here indicate that approaches for routine diagnosis in endemic settings should be encouraged to ascertain the true burden of these species as well as for appropriate interventions toward pan-species malaria elimination. Studies to determine the prevalence and dynamics of non-falciparum species as well as associated risk factors in endemic populations need to be expanded across sSA.In conclusion, the prevalence rates of non-Supplemental materials


Since 2014, seasonal malaria chemoprevention (SMC) with amodiaquine–sulfadoxine–pyrimethamine (AQ–SP) has been implemented on a large scale during the high malaria transmission season in Burkina Faso. This paper reports the prevalence of microscopic and submicroscopic malaria infection at the outset and after the first round of SMC in children under 5 years old in Bama, Burkina Faso, as well as host and parasite factors involved in mediating the efficacy and tolerability of SMC.pfcrt K76T and pfmdr1 N86Y, molecular markers associated with reduced amodiaquine susceptibility.Two sequential cross-sectional surveys were conducted in late July and August 2017 during the first month of SMC in a rural area in southwest Burkina Faso. Blood smears and dried blood spots were collected from 106 to 93 children under five, respectively, at the start of SMC and again 3 weeks later. Malaria infection was detected by microscopy and by PCR from dried blood spots. For all children, day 7 plasma concentrations of desethylamodiaquine (DEAQ) were measured and CYP2C8 genetic variants influencing AQ metabolism were genotyped. Samples were additionally genotyped for Plasmodium falciparum infection by microscopy and 13.2% (14/106) by nested PCR within 2 days of SMC administration. Three weeks after SMC administration, in the same households, 4.3% (4/93) of samples were positive by microscopy and 14.0% (13/93) by PCR (p = 0.0007). CYP2C8*2, associated with impaired amodiaquine metabolism, was common with an allelic frequency of 17.1% (95% CI  10.0–24.2). Day 7 concentration of DEAQ ranged from 0.48 to 362.80 ng/mL with a median concentration of 56.34 ng/mL. Pfmdr1 N86 predominated at both time points, whilst a non-significant trend towards a higher prevalence of pfcrt 76T was seen at week 3.2.8% (3/106) of children were positive for This study showed a moderate prevalence of low-level malaria parasitaemia in children 3 weeks following SMC during the first month of administration. Day 7 concentrations of the active DEAQ metabolite varied widely, likely reflecting variability in adherence and possibly metabolism. These findings highlight factors that may contribute to the effectiveness of SMC in children in a high transmission setting. In Burkina Faso, malaria is still the leading cause of morbidity and mortality with 7875,575 cases and 12,725 deaths in 2018 [A concern with the implementation of SMC is the absence of malaria infection screening using available diagnostic methods before the treatment of eligible children. Studies have demonstrated that in malaria endemic countries, a large proportion of malaria infections are asymptomatic , 4. WhilTherapeutic drug concentrations are influenced by pharmacokinetics, host immunity, and parasite susceptibility to the drug. Amodiaquine is metabolized to its primary active metabolite N-desethylamodiaquine (DEAQ) by the cytochrome P450 enzyme (CYP) 2C8. Two of the more common CYP2C8 genetic variants have been described to have altered drug metabolism, including the impaired conversion of amodiaquine to DEAQ; CYP2C8*2, which is more prevalent in people of African descent, and CYP2C8*3, which is more prevalent in Caucasians [Plasmodium falciparum chloroquine resistance transporter (pfcrt) gene. Resistance is augmented by polymorphisms in another transporter gene, pfmdr1. Before SMC implementation in Burkina Faso, AQ resistance mediating polymorphisms have been reported in several studies [pfcrt and pfmdr1 towards wild-type SNPs [Drug resistance to amodiaquine is primarily mediated by a single nucleotide polymorphism (SNP) that results in a K76T substitution in the  studies , 9, 10.  studies . Intriguype SNPs . CurrentThe aim of this study is to report the prevalence of microscopic and submicroscopic malaria infections before and after the first round of SMC in children under five in Bama, Burkina Faso. This study also provides a more comprehensive view of the host and parasite factors that may influence SMC, specifically day 7 plasma concentrations of DEAQ, the presence of CYP2C8 genetic variants, and the prevalence of parasite drug resistance-mediating polymorphisms.Anopheles gambiae is very high with an annual entomological inoculation rate of up to 200 infective bites/person/night [Samples for the present study were collected in Bama, Burkina Faso, a rural area located in the district of Dandé in the Kou Valley. Bama is a rice-growing area of 1200 hectares located 30 km from Bobo-Dioulasso in the southwest of Burkina Faso. The rainy season in this area extends from June to October and the dry season from November to May. The Kou Valley is a permanent source of irrigation water with two rice crops per year from July to November and from January to May. Malaria transmission is perennial with a peak during the rainy season when the density of on/night . The medSMC is distributed by a team of community health workers (CHW) during the rainy season corresponding to the high malaria transmission period. In 2017, SMC was distributed four times, from the end of July to the end of October, and each distribution cycle lasted 4–5 days with an interval of 1 month between administration. Drugs were delivered to eligible children using a door-to-door strategy. Drugs were crushed and mixed with sugary water to improve uptake. A complete treatment course of SMC includes a single dose of SP and three daily doses of AQ; the CHWs provide the first dose to each child under directly observed treatment (DOT). The study team observed children for 30 min following ingestion of the first dose. In all situations where the child vomited or regurgitated the drug within 30 min, a second treatment was administered. After administration of the first dose of AQ and SP, the CHWs explained to parents/guardians how to administer the second and third doses of AQ to the child at home and informed them about possible side effects.Two weeks’ prior the delivery of the first round of SMC, 124 randomly selected households were visited by the study team. EPI-style random walk method was utilized to randomly select households with children under 5 years of age in a two-step process which involved first selecting a starting point and secondly selecting households from that point onward. The EPI method was appropriate due to lack of local census data and boundary maps. Only one child was selected per household; if multiple children met the inclusion criteria, one child was randomly selected. Households with children aged 3 to 59 months were selected to participate in the study after the parents/guardians gave informed consent. Unoccupied houses were visited a total of three times. The first malaria prevalence survey was conducted in late July 2017, within the period of distribution of the first round of SMC. When logistically feasible, CHWs were accompanied by the study team and blood smears and dried blood spots (DBS) were collected by finger-prick. Otherwise, houses were visited ± 2 days of the 1st day of SMC administration. The team returned to collect blood samples for pharmacokinetic analyses (see Quantification of DEAQ below) 7–8 days post- SMC administration. For the second malaria prevalence survey in late August 2017, 1 week before the second round of SMC, the study team visited the same households and collected information on any symptoms related to malaria, along with smears and DBS from the same children, if available for resampling.Thick and thin blood films obtained from children by finger prick were air dried, stained in Giemsa 2% for 30 min, and examined by light microscopy fitted with 100 × oil immersion lens in the laboratory of IRSS. A smear was considered negative after examination of at least 100 fields. Parasite density was calculated by counting the number of asexual parasites per 200 leukocytes, assuming a leukocyte count of 8000/μL. For all positive smears, parasite species were determined using the corresponding thin smear on the same slide. Each smear was read by two experienced microscopists. Results were considered discordant if one result was negative and the other positive for malaria infection. Discordant results were resolved by a third reader.Plasmodium species were subsequently detected by nested PCR as previously described [Plasmodium falciparum, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax. For quality control, a template free control was used in all reactions and genomic DNA from laboratory strains (www.beiressource.com) were used as positive control for respective species. All positive samples by microscopy and negative by PCR were reanalysed by PCR before confirmation of the microscopy results.Parasite DNA was extracted from DBS using Chelex-100. escribed . Speciesg for 10 min and the plasma was then transferred to cryovials and stored at − 80 °C. DEAQ plasma concentrations were quantified by reverse phase liquid chromatography and detection with an AB Sciex API 5000 tandem mass spectrometer (LC–MS-MS) in turbo-ion spray-positive mode by North East BioLab . 2H5N-Desethylamodiaquine was used as an internal standard and the total assay coefficient of variation were < 7%. The lower limit of quantification (LLOQ) of DEAQ was 0.34 ng/mL.A total of 200 µL of capillary whole blood was collected in EDTA-containing microtubes on day 7. Samples were centrifuged at 2000×Genotyping for two nonsynonymous CYP2C8 variants was performed on DNA extracted from DBS using the QIAamp DNA Mini Kit (Qiagen). CYP2C8*2 (805A > T) and CYP2C8*3 (416G > A and 1196A > G) alleles were determined using a TaqMan Drug Metabolism Genotyping Assay . PCR was performed in a 25 µL reaction with 12.5 µL of TaqMan Universal PCR Master Mix, 1.25 µL of the drug Metabolism Genotyping Assay Mix, and at least 3 ng of DNA template, per the manufacturer’s instructions. Quality control was maintained with no template controls with every reaction, along with previously determined wild type, heterozygous, or homozygous CYP2C8*2 or *3 allele positive controls  .pfcrt and pfmdr1. Amplicons were used for a ligase detection reaction (LDR) which contained an allele-specific and conserved sequence primer [pfcrt K76T or pfmdr1 N86Y. Conserved sequence primers were modified by 5′ phosphorylation and 3′ biotinylation. After primer ligation, LDR reaction products were hybridized to their respective MagPlex beads in 1.5 TMAC buffer . Fluorescent labeling was performed with a 1:50 dilution of streptavidin-R-phycoerythrin. Drug resistance polymorphisms were determined by measuring fluorescent intensity using xPonent software (Luminex) on a Bio-Plex 200 instrument (Bio-Rad). Genotyping was attempted for SP-associated mutations, but due to a low success rate, data is not included.Genotyping was performed on parasite DNA extracted from DBS using the QIAamp DNA Mini Kit (Qiagen). Nested PCR was performed to amplify amodiaquine resistance-associated alleles for e primer . Allele-p value of < 0.05 was considered statistically significant.Data were collected with Excel and analysed by R version 3.4.0 (2017-04-21). Chi square was used to compare proportions and a Blood samples were collected from 106 children at the start of SMC by randomly selecting one child under 5 years in each enrolled household. In the second survey, the same households were revisited and blood samples were collected from 93 children; 31 were absent from their households for three or more visits and thereby excluded. The ages of children ranged between 6 and 48 months old, with an average of 29 months. The ratio of male to female children were comparable at both time points (0.93 to 1.1).Plasmodium infection detected by both microscopy and PCR are summarized in Table Plasmodium falciparum was the only species detected in positive sample by both microscopy and PCR. All microscopy positive samples were also PCR positive.DBS were collected 1 day before (16.7%) or on the day of (54.6%) SMC, while 28.7% were collected within 2 days after SMC due to field logistics. Results of P. falciparum single infection. For microscopy, positive samples parasite densities ranged from 960 to 62,137 parasites/μL.The proportion of malaria-positive individuals was 4.3% (4/93) by microscopy and 14.0% (13/93) by nested PCR  . With respect to genotype, 28.7% (31/108) were heterozygous and only 2.8% (3/108) were homozygous for the CYP2C8*2 allele. Genotyping for CYP2C8*3 variants was successful in 97.3% (107/110) of the samples; however, the CYP2C8*3 allele was not detected in any of the children genotyped. Results of the genotyping are summarized in Table DNA was available from 110 children for CYP2C8 genotyping. Of those, genotyping for CYP2C8*2 variants was successful in 98.2% (108/110) of the samples. The CYP2C8*2 allelic frequency was 17.1%  and was found to be in Hardy–Weinberg equilibrium .The relationship between DEAQ drug levels and CYP2C8*2 genotype was also assessed; however, there was no statistically significant difference in DEAQ concentration between those with homozygous wild-type alleles  or heterozygous CYP2C8*2 alleles  (Pfcrt genotyping for the K76T SNP was successful on 85.7% (12/14) and 46.2% (6/13) of samples from the first and second surveys, respectively. The first survey showed a high proportion of mixed  and wild-type K76T , while only a single wild-type and no mixed K76T infections were detected in the second survey . Thus, the proportion of pure mutant infections increased from 25% (3/12) to 83.3% (5/6) between surveys. However, there was no statistically significant difference in the prevalence of polymorphisms between the first and second survey , though the comparison is limited due to the small number of successfully genotyped samples in the second survey. For pfmdr1, genotyping for the N86Y SNP was successful on 71.4% (10/14) and 69.2% (9/13) of samples from the first and second surveys, respectively. The results showed a high prevalence of wild-type N86 alleles at both sampling time points, with only one sample positive for the 86Y polymorphism from the second survey.The present study assessed the prevalence of malaria infection by microscopy and PCR in a cohort of children under 5 years old after the first round of SMC with AQ–SP in Bama, Burkina Faso. The results of this study demonstrated a relatively low but non-negligible parasitaemia in SMC eligible children after drug administration. These results further support that the majority of malaria infections are sub-microscopic –21. All To probe the potential drug-related impacts, day 7 DEAQ levels were further assessed in children after SMC administration. The median day 7 DEAQ concentration in the study cohort is consistent with previous PK studies of amodiaquine and DEAQ , 23. Thopfcrt 76T mutations, associated with reduced amodiaquine sensitivity, less than half of the PCR-positive samples could be genotyped at this time point by our methods. Genotyping failure is likely attributed to the low-density of these infections, reflected in the limited number of these detected by microscopy, and also in the lack of successful SP-associated mutation typing. In contrast to pfcrt polymorphisms, there was limited genetic diversity at the pfmdr1 N86Y loci, with all but one sample positive for the N86 wild-type SNP. As noted earlier, lumefantrine selects toward the wild-type N86, in opposition to the direction of selection for amodiaquine. Careful monitoring of both AQ and SP-resistance associated polymorphisms and of possible prophylactic failures of SMC should be ensured in order to understand the potential population-level impacts of the opposing selection pressure by the first-line treatment, artemether–lumefantrine, and the widespread SMC regimen, AQ–SP. If possible, future studies should encompass multiple rounds of SMC, with testing at baseline, throughout, and after SMC to assess for the selection of resistance. Finally, more in-depth pharmacokinetic studies with detailed drug resistance typing may be required to understand the relationship between AQ–SP exposure and the development of resistance.As reported in other studies , 25, 26,This study estimated the prevalence of malaria infection in children at two time points after treatment with the first round of seasonal chemoprophylactic AQ–SP, demonstrating a notable prevalence of low-level malaria parasitaemia in children 3 weeks after the first round of SMC. Day 7 concentrations of the active DEAQ metabolite varied widely, likely reflecting variability in adherence and possibly metabolism. The findings of this study highlight factors that may contribute to the effectiveness of SMC in children in a high transmission season.


Plasmodium falciparum, a bacteria that causes malaria, using a publicly available benchmark image data set.This diagnostic study assesses an expert-level detection algorithm for  Can deep learning be used to develop an automated malaria detection algorithm?Plasmodium falciparum in thin blood smear images. The comparable performance between the algorithm and human experts was confirmed by a clinical validation study at the cell level and the image level.In this diagnostic study that used a 1-stage deep learning framework and benchmark data sets, the malaria detection algorithm achieved expert-level performance in detecting The findings suggest that a clinically validated expert-level malaria detection algorithm could be used to accelerate the development of clinically applicable automated malaria diagnostics. Decades of effort have been devoted to establishing an automated microscopic diagnosis of malaria, but there are challenges in achieving expert-level performance in real-world clinical settings because publicly available annotated data for benchmark and validation are required.To assess an expert-level malaria detection algorithm using a publicly available benchmark image data set.In this diagnostic study, clinically validated malaria image data sets, the Taiwan Images for Malaria Eradication (TIME), were created by digitizing thin blood smears acquired from patients with malaria selected from the biobank of the Taiwan Centers for Disease Control from January 1, 2003, to December 31, 2018. These smear images were annotated by 4 clinical laboratory scientists who worked in medical centers in Taiwan and trained for malaria microscopic diagnosis at the national reference laboratory of the Taiwan Centers for Disease Control. With TIME, a convolutional neural network–based object detection algorithm was developed for identification of malaria-infected red blood cells. A diagnostic challenge using another independent data set within TIME was performed to compare the algorithm performance against that of human experts as clinical validation.Plasmodium falciparum–infected blood cells was measured by average precision, and performance on detecting P falciparum infection at the image level was measured using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).Performance on detecting P falciparum–positive and 6 P falciparum–negative smears) that had reliable annotations. For clinical validation, the average precision was 0.885 for detecting P falciparum–infected blood cells and 0.838 for ring form. For detecting P falciparum infection on blood smear images, the algorithm had expert-level performance , especially in detecting ring form  compared with experienced microscopists .The TIME data sets contained 8145 images of 36 blood smears from patients with suspected malaria (30 The findings suggest that a clinically validated expert-level malaria detection algorithm can be developed by using reliable data sets. Plasmodium species, is a severe and reemerging global health issue despite years of effort in global malaria control. In 2017, an estimated 219 million cases of malaria and 435 000 malaria-related deaths occurred worldwide.1 Most patients were in the World Health Organization African region (92%) and South-East Asian region (5%). Although Taiwan has been certified malaria free for more than 5 decades, imported cases, mostly from Africa and Southeast Asia and caused by Plasmodium falciparum, still occur every year.2Malaria, a mosquito-borne disease caused by 4 Thick blood smears are used for screening, whereas thin blood smears are used for confirming the species and measuring parasite density.3 However, conventional microscopic diagnosis is labor intensive and dependent on techniques and experience. This expertise is rare not only in resource-limited countries, where malaria poses a significant burden, but also in countries close to malaria elimination where the microscopists lack experience.4 Both situations prompted efforts to seek more efficient and accurate diagnostic tools. Economical and reliable rapid diagnostic tests (RDTs) can replace thick smears as a screening tool in resource-limited settings. However, RDTs provide insufficient information about species, life-cycle stages, and quantification of parasitemia, which are pivotal for clinical management.4 Therefore, researchers from engineering and computer science have performed extensive studies for an automated microscopic examination during the past decade.5 Nevertheless, progress toward a clinically applicable system was slow because of several challenges. First, the variety of staining methods and quality of smear preparation for microscopic blood smear images makes it difficult to devise universal features using traditional approaches. Second, it is hard to compare different algorithms because of different evaluation metrics and the absence of reference benchmark data. The difficulty of acquiring a large number of images with reliable annotations for public reference hinders the development of automated malaria diagnosis.The criterion standard for malaria diagnosis is microscopic examination.5 With the advent of deep learning, convolutional neural network (CNN)–based algorithms have achieved expert-level performance in the detection of pathologic characteristics in multiple medical image modalities,8 and the potential of applying deep learning on both thick and thin smears has been explored.11 Because thin smears can provide more clinical information, we aimed to assess high-quality image data sets of thin blood smears with expert annotations for public reference, develop a CNN-based algorithm to identify P falciparum infection automatically, and validate its performance in a clinical context.Traditional approaches to detecting malaria-causing organisms on thin smears involve a multistage approach of image preprocessing, red blood cell (RBC) segmentation, feature engineering, and classification of infected and noninfected RBCs.STARD) reporting guideline.For this diagnostic study, our framework began with the acquisition and digitization of thin blood smears, followed by algorithm training and validation at the cell and image levels . The stuP falciparum infection by local physicians and public health workers who alerted the Taiwan CDC from January 1, 2003, to December 31, 2018. Laboratory diagnosis was made by microscopic examination and polymerase chain reaction.12 The details of enrollment of patients and sample collection are described in eMethods 1 in the Data were retrospectively acquired from ex vivo peripheral blood samples from patients suspected of having P falciparum–infected RBCs were annotated according to our annotation guideline, which defined the targets and guided the appropriate annotations  to scan the blood smears and acquired pictures via digital cameras. The whole slide was sliced into grids of 2 × 2 mm. We then used the 100 × objective scope with oil immersion to scan slides into Olympus virtual slide images (.vsi). Olympus VS-ASW software was used to convert the .vsi files into jpeg files of 2048 × 2048 pixels. All digitized images were annotated by microscopists at the Taiwan CDC using an in-house annotation tool. For each image, 2 levels of annotation were made. At the cell level, the 14 a reliability test subset was generated by randomly sampling 500 images from blood smears in the development set (19-20 images per smear) and was annotated by 2 other experienced microscopists (S.-F.H. and H.-J.L.), who worked in the parasitology laboratory at the Taiwan CDC for more than 5 years. Interrater reliability was assessed using mean F1 scores  among pairs of annotators at the cell level,15 and the percentage of agreement and the Fleiss κ were calculated for multiple raters at the image level. For the clinical validation set, all images were annotated by 3 expert microscopists  at the Taiwan CDC. Interrater reliability was similarly measured as described above.All images in the development set were reviewed and annotated by an experienced microscopist (M.-C.K.) responsible for microscopic diagnosis of malaria at the Taiwan CDC for more than 45 years. Because of the variability among different microscopists,16 The architecture consisted of a backbone network and 2 subnetworks, as in the original work,14 with a specially crafted loss function termed focal loss. The only modification in our architecture was that 1 fewer convolution layer was adopted for the backbone network. The backbone network was a feature pyramid network built on top of a CNN17 that was responsible for computing convolutional feature maps for input images. ResNet50 pretrained on the ImageNet data set was used for the backbone network.19 The first subnetwork is the object class subnet in the architecture of a CNN, which predicts labels given the output of the backbone network. The second subnetwork is the box subnet, which calculates regression on bounding box locations. The network was trained with the development set to identify the location of P falciparum–infected RBCs and to classify their stages.The malaria detection algorithm was developed based on Retinanet, a 1-stage object detection neural network.−5 and gradient clipping were used for training. The α was set to .25 and the γ to 2 for the focal loss. Our models were trained with a computer with an Intel Xeon E5-2630 v4 2.20 GHz central processing unit with a NVIDIA GeForce GTX 1080 Ti GPU 11-GB graphic card and 128 GB of RAM. We referenced our algorithm to the Keras implementation of Retinanet.35An Adam optimizer with a learning rate of 1 × 10P falciparum–infected cells. The scientists were trained for malaria microscopic diagnosis in a national reference laboratory at the Taiwan CDC and had 5 to 10 years of working experience.A diagnostic challenge was conducted to compare the performance of the algorithm against clinical laboratory scientists. Four practicing clinical laboratory scientists were recruited from 2 medical centers in Taiwan  to review the clinical validation set and to annotate P falciparum.20Algorithm performance was evaluated at the cell and image levels. At the cell level, whether bounding boxes identified by the algorithm matched ground truth bounding boxes was determined by a matching process  curves. At the image level, ROC curves were plotted and areas under the ROC curve (AUCs) were computed using the Python package Scikit-learn, version 0.18.1 (Python Software Foundation).23 The second operating point, the high specificity operating point, which approximates the mean specificity of practicing microscopists in the clinical validation set, could further characterize our algorithm performance against the 4 practicing microscopists. The 95% CIs for the sensitivity and specificity at the 2 operating points were calculated to be exact Clopper-Pearson intervals.24 Two-sided 95% CIs were computed using the Python package StatsModels, version 0.8.0.Two operating points were selected at the image level to further characterize the algorithm performance. The first operating point, corresponding to the point with the highest Youden index, reflects the optimal point of algorithm performance with high sensitivity. It was chosen because high sensitivity is a prerequisite for a potential screening tool.Experiments were designed to evaluate the association of different labeling strategies with malaria detection. For the development set, 2 sets of meta-labels were processed and inferred from the raw annotations. For the first set, we pooled the labels of young trophozoite (ring form) and trophozoite into the category trophozoite, together with the remaining labels of schizont and gametocyte; for the second set, all labels regardless of their life-cycle stages were merged into the category malaria infection. These 2 sets of meta-labels were then used to train another 2 malaria detection models with similar configurations delineated above. Their performances were measured with the same metrics.P falciparum–positive and 4 P falciparum–negative smears). Images in the clinical validation set were scanned from 10 blood smears   data sets, which included 2 data sets: the development set for training and the clinical validation set for evaluating performance. Images in the development set were scanned from 26 blood smear slides (22  smears) . The devP falciparum–infected cells annotated, among which the 2 most common life-cycle stages were young trophozoite  and trophozoite (10.9%), followed by gametocyte (1.3%) and schizont (0.2%). Of the 500 images in the clinical validation set, 400 (80.0%) were identified with 3061 P falciparum–infected cells. The most common life-cycle stages were ring form (2909 [95.0%]) and trophozoite (117 [3.8%])  in the development set and 4  in the clinical validation set.Of the 7645 images in the development set, 4402 images (57.6%) were identified with 21 220  [3.8%]) . Both da [3.8%]) . For P fAt the cell level, the mean F1 score of the reliability test subset among pairs of experts was 0.924 , and the clinical validation set had an F1 score of 0.954 . At the image level, the percentage of agreement was 97.5% for the reliability test subset and 100% for the clinical validation set. The Fleiss κ among the 3 experts was 0.459 in the reliability test subset and 1.000 in the clinical validation set , showing no statistically significant difference compared with the mean error rate of microscopists .At the cell level, our algorithm achieved an average precision of 0.885 in detecting ted RBCs . At the ted RBCs A. At theFor ring form detection at the image level, our algorithm had an AUC of 0.995  B. At theOn the clinical validation set, the model trained with meta-labels that pooled ring form and trophozoites as 1 category had an AUC of 0.987  for malaria detection, whereas the model trained with meta-labels that pooled all parasites together as 1 category had an AUC of 0.994  for malaria detection. The results indicated that the performance of these models trained with different labeling strategies were comparable to that of the original version.P falciparum–infected RBCs, encompassing a wide range of variations encountered in clinical settings. A CNN-based object-detection algorithm was developed with promising sensitivity and specificity to identify malaria detection and demonstrated performance similar to that of human microscopic experts .In this study, we established benchmark image data sets of thin blood smears for malaria microscopic diagnosis. To our knowledge, this is the first intent to provide publicly available image data sets of microscopic blood smears with reliable annotation. Our data sets provide more than 8000 images with more than 24 000 annotated 27 However, touching cells on thin smears makes accurate RBC segmentation challenging. We designed our data sets for the development of 1-stage detection algorithms by providing images directly acquired from slides without additional preprocessing or segmentation. In this manner, we bypassed the segmentation problem and minimized artifacts resulting from human-designed preprocessing, ensuring the highest fidelity of the images with respect to the clinical reality. Retinanet was adopted as our 1-stage detector for its better performance, simpler structure, and previous application to other clinical contexts.29 Compared with the 2-stage approach, our algorithm demonstrated encouraging performance in dealing with touching blood cells. Furthermore, because the input images required no preprocessing, we minimized the computation cost both in hardware and processing time, ensuring rapid smear-to-diagnosis turnaround time.From data set generation to algorithm development, our framework was designed with clinical applicability in mind. Previously released data sets typically consisted of images of individually segmented RBCs, designed for the 2-stage approach in which researchers segmented individual RBCs from microscopic images and then developed algorithms to classify them.31 and the annotations might be susceptible to errors and biases because of the varied training protocol and experience of the individual annotator.14 To overcome this challenge, we recruited multiple microscopists and standardized the annotation process. In addition, because the reliability test showed a moderate to high level of agreement among our experts’ annotations at the cell and image levels, we are confident of the reliability of the annotations in our data sets. Furthermore, to our knowledge, this study was the first attempt to validate the algorithm performance against practicing microscopists in a clinical context. The expert-level detection algorithm demonstrated the potential to automate the microscopic examination, providing malaria diagnosis once a blood smear was made and photographed. Beyond streamlining the diagnosis workflow, our algorithm presents an opportunity to preserve and commoditize the increasingly scarce expertise in microscopic diagnosis, especially for countries close to malaria elimination but still dealing with imported cases related to international travel.For image annotation, previous data sets were usually annotated by only 1 expert,32 Currently, countries where malaria is endemic rely on RDTs to screen for malaria.33 Our algorithm achieved a 99.5% sensitivity and a 90.0% specificity at high-sensitivity operating points, whereas standard RDTs achieved sensitivities of 80% to 95% and specificities of 85% to 99%.34 A possible explanation of our algorithm’s superior performance might be that a P falciparum–infected cell in thin smears could provide more details for a deep learning algorithm to identify compared with thick smears. Compared with RDTs, which lack information regarding parasite density and life-cycle stages, our framework not only provided comparable malaria detection performance but also may affect clinical management through automated parasite density estimation, providing pivotal information on disease stratification and treatment response monitoring.34 Manual counting of parasites is labor intensive and impractical in resource-constrained settings. With the use of our algorithm, it might be feasible to derive quantitative estimates of parasite density using methods similar to those described in the World Health Organization guideline3 but without human intervention. In this manner, practitioners can rapidly stratify patients by severity, optimize therapies, and monitor a patient’s therapeutic response.34Given that malaria burden is greatest in countries with limited resources, we envision deploying a mobile application of our algorithm for unskilled workers with minimum equipment requirements.P falciparum without other species . Nonetheless, the algorithm is expected to be expanded without difficulty to identify the ring form of other species with tuning because Plasmodium species are morphologically similar. Third, our data sets were established from a limited number of patients, which might affect the algorithm’s generalizability. However, the variability in our data sets that resulted from different imported countries, clinical settings, and staining methods might prevent overfitting and help with generalizability. Our clinical validation that mimicked a clinical scenario also showed promising performance for the patients not included in the development set and the potential for real-world application. Nevertheless, how to link negative malaria detection at the cell and image levels with the exclusion of a malaria diagnosis would be another issue for clinical decision making. To further validate and implement the algorithm in real-world settings, work on the clinical workflow design and pilot field validation in countries where malaria is endemic will be required.This study has limitations. First, false-negative detection occurred in several challenging situations, for example, when the cytoplasm and nuclei of parasites were obscure or deformed. False-positive detections happened when stained platelets or impurities on top of RBCs mimic the parasites. To perform better in these challenging scenarios, algorithm training with high-quality images focused on those situations would be helpful. Second, the algorithm was developed to identify only In this study, we built a publicly available benchmark image data set of malaria thin blood smears with reliable annotations (TIME) and demonstrated the potential to develop a deep learning–based malaria detection algorithm with expert-level performance. Both the data sets and algorithm may help accelerate the development of automated microscopic diagnosis and a decision support system in resource-limited countries with heavy malaria burden.


Plasmodium species distribution and the genetic diversity of msp1 and msp2 of parasite populations using molecular tools.While the World Health Organization (WHO) Southeast Asia region has the second highest incidence of malaria worldwide, malaria in Vietnam is focal to few provinces, where delayed parasite clearance to anti-malarial drugs is documented. This study aims to understand Plasmodium species identification by nested real-time PCR. 166 isolates positive for Plasmodium falciparum mono infections were further genotyped for msp1 , and msp2 allelic families (3D7 and FC27). Amplicons were resolved through capillary electrophoresis in the QIAxcel Advanced system.A total of 222 clinical isolates from individuals with uncomplicated malaria were subjected to P. falciparum, 14% Plasmodium vivax and 9% P. falciparum/P. vivax co-infections, with less than 1% Plasmodium malariae identified. For msp1, MAD20 was the most prevalent (99%), followed by K1 (46%) allelic family, with no sample testing positive for RO33 (0%). For msp2, 3D7 allelic family was predominant (97%), followed by FC27 (10%). The multiplicity of infection of msp1 and msp2 was 2.6 and 1.1, respectively, and the mean overall multiplicity of infection was 3.7, with the total number of alleles ranging from 1 to 7.Mono-infections were high and with 75% P. falciparum msp1 and msp2 should be regularly monitored with respect to treatment outcomes and/or efficacy studies in regions, where there are ongoing changes in the malaria epidemiology.Given the increasing importance of antimalarial drugs in the region, the genetic diversity of  Malaria is still one of the overwhelming public health problems worldwide, and the World Health Organization (WHO) Southeast Asia region has the second highest incidence of malaria with a total of 7.9 million cases in 2018 . AlthougPlasmodium falciparum (64%) and Plasmodium vivax (35%) are the most important malaria parasites. Artemisinin-based combination therapy (ACT), especially using dihydroartemisinin piperaquine (DHA-PPQ), is the first-line of treatment [P. falciparum are essential to understand the intensity of transmission, epidemiological patterns and virulence of the parasites and, in particular, to evaluate measures aimed at malaria control [In Vietnam, reatment . One of ciparum 6% and Plam vivax 3% are thereatment . Since a control –11.P. falciparum is usually determined by the evaluation of the extent of the polymorphism of the merozoite surface proteins msp1 and msp2, which are expressed on the surface of the merozoites during the erythrocytic stage of the life cycle of P. falciparum [msp1 gene on chromosome 9 consists of 17 different blocks, and block 2 is highly polymorphic and consists of three different allelic families: MAD20, K1 and RO33. The msp2 gene on chromosome 2 contains highly polymorphic central repeats in block 3 and is distinguishable for two allelic families: 3D7 and FC27. Both msp1 and msp2 are highly immunogenic and are considered potential blood stage vaccine candidates [P. falciparum with excessive parasite transmission in holoendemic areas [The diversity of lciparum , 13. Thendidates , 15. Thendidates . The cloic areas  .msp1 and msp2 alleles in 2012 [Plasmodium species and subsequently the different msp1, msp2 genotypes and to estimate the MOI in clinical isolates from malaria endemic areas in Vietnam.In Vietnam, the predominant  in 2012  are MAD2 in 2012  and 3D7  in 2012 , respectPlasmodium parasites. Blood samples were stored at − 20 °C until further use.Written informed consent was obtained from all study participants. The study was approved by the Institutional Review Board of Vietnam Military Medical University, Hanoi, Vietnam. The study included 222 clinical isolates collected from adult individuals with uncomplicated malaria . An individual who presents with symptoms of malaria and a positive parasitological test (microscopy or RDT) but with no features of severe malaria is defined as having uncomplicated malaria. Clinical isolates were collected in Vietnamese provinces, which included Dak Lak, Gia Lai and Dak Nong between years 2017–2019. The three provinces  are adjacent to each other and are part of the central highlands bordering Cambodia. Whole blood samples were collected from individuals and were microscopically confirmed for the presence of Plasmodium species, a Taqman probe-based Pan-Plasmodium real-time PCR was used, as described earlier [Genomic DNA was isolated from 50 µl whole blood using the QIAamp DNA Mini-Kit  according to the manufacturer’s protocol. For detection and characterization of the  earlier .Plasmodium species. In each single-plex nested real-time PCR assay, distinct primers for P. falciparum [P. vivax [Plasmodium malariae, Plasmodium ovale curtisi, Plasmodium ovale wallikeri [In short, the parasite DNA was amplified in a conventional PCR using the primers PLU5 and PLU6 . The amplciparum , P. vivaP. vivax , Plasmodallikeri  are specP. falciparum were genotyped by nested PCR. The outer PCR was used to amplify conserved regions of msp1 and msp2 and then the nested PCR was used to amplify MAD20, K1 and RO33 allele families at the msp1 gene locus and 3D7 and FC27 allele families at the msp2 gene locus. The outer and inner PCRs were performed with published primers as described elsewhere [The samples positive for lsewhere .2), 200 µM dNTPs, 100 nM of each primer and 1U Taq DNA polymerase  on an Eppendorf Nexus Gradient PCR Cycler . The thermal cycle parameters for the first PCR amplification were: initial denaturation at 94 °C for 10 min, followed by 35 cycles of 30 s at 94 °C denaturation, 30 s at 55 °C annealing, 1 min at 72 °C extension, followed by a final extension of 10 min at 72 °C. The inner PCRs for the allele families MAD20, K1 and RO33 (msp1) and 3D7 and FC27 (msp2) were performed with the outer PCR templates of msp1 and msp2, respectively. All five reactions were performed independently in 25 µl reaction mixture containing 3 µl from the outer PCR template, 1x PCR buffer, 1U Taq polymerase , 200 µM dNTPs, 100 nM forward and reverse primers . The PCR reaction conditions are identical to those of the outer PCR, except that the annealing temperature was 61 °C for the msp1 allele families and 56 °C for the msp2 allele families. For the msp1 positive controls, P. falciparum DNA was isolated from the strains Dd2, NF54 and 7G8 for the alleles MAD20, K1 and RO33, respectively. For the msp2 positive controls, DNA was isolated from P. falciparum strains Dd2 and NF54 for alleles 3D7 and FC27, respectively.In brief: for the outer PCR, the DNA fragment was amplified in a reaction mixture of 20 µl volume containing 1x PCR buffer  and the QX alignment marker 15 bp/3 kb .The QIAxcel Advanced system  was used to resolve the amplicons by capillary electrophoresis. All nested PCR products were performed according to the AM420 protocol using the QX-DNA size marker 50–800 bp  and the QX alignment marker 15 bp/1 kb , except for the nested PCR products of the The results were analysed with the QIAxcel Screen Gel software . All positive controls yielded a single peak, except the FC27 positive control, which clearly showed two peaks in each run . Each band represents one allele in the respective allele families  isolates were positive for P. vivax, one isolate was positive for P. malariae and 20 (9%) isolates were positive for both P. falciparum and P. vivax, indicating co-infection. Furthermore, we did not find any isolates positive for P. ovale curtisi and P. ovale wallikeri. In total, 3 (2%) of the isolates were PCR negative.On screening 222 samples, 166 (75%) isolates were positive for P. falciparum by nested real-time PCR were subsequently genotyped for both msp1 and msp2 loci  followed by K1 74 (46%) isolates, with the complete absence of RO33 allelic family. In msp1, 85 (53%) isolates were positive for MAD20 only and 73 (46%) isolates were positive for both MAD20 and K1, except one (0.6%) isolate that was positive for K1 only. In the msp2 locus the 3D7 allele family was predominant in 148 (97%) and the FC27 allele family in 16 (10%) isolates. A total of 137 (90%) isolates were positive for 3D7 and 11 (7%) positive for both 3D7 and FC27. There were up to 7 different alleles for msp1, with allele sizes varying between 141 and 255 bp (MAD20) and 161–1270 bp (K1). There were up to 4 different alleles for msp2, with allele sizes varying between 248 and 650 bp (3D7) and 366–531 bp (FC27).All 166 isolates positive for msp1 and msp2 genotyping results. A mean MOI of 2.6 ± 1 for msp1 and a mean MOI of 1.1 ± 0.5 for msp2, and an overall MOI for msp1 and msp2 is 3.7 ± 1.2 observed. There is no significant distribution of MOI between age groups.Multiplicity of infection was calculated from the Plasmodium species using sensitive real-time nested qPCR and were characterized the P. falciparum diversity, and MOI using molecular epidemiological tools.In this study, 222 clinical isolates were tested for P. falciparum was observed, followed by 15% P. vivax and 8% P. falciparum/P. vivax co-infections. These results differ slightly from those of the WHO, where 64% was reported for P. falciparum and 35% for P. vivax in Vietnam [P. falciparum, 27% P. vivax, 25% co-infections with P. falciparum and P. vivax and 12% unidentified species from the same region [P. falciparum. However, due to control measures, there has been a stronger impact on P. falciparum than on P. vivax since 2014, resulting in an almost equal ratio of both species [P. knowlesi and P. vivax have been documented in mosquitoes and humans in South Vietnam [From the nested real-time PCR results, 75%  Vietnam . These de region . In addi Vietnam .Anopheles mosquito, during a blood meal [P. falciparum malaria in the last 10 years, and ACT being introduced as a component of comprehensive malaria control efforts, it is a challenge to reduce P. falciparum malaria cases in these provinces. The fact that the use of indoor spraying and insecticide-treated mosquito nets (ITN) is equally to contribute to these changes in the malaria epidemiology. Studies of the therapeutic efficacy of first-line treatment with DHA-PPQ at national sentinel sites have shown delayed parasite clearance in Gia Lai Province (2010), Dak Nong Province (2011), Quang Nam Province (2012), Khanh Hoa Province (2014) and Ninh Thuan Province (2015), with over 10% of patients being microscopically positive on day 3 after treatment initiation [The use of ACT may have population-wide benefits in malaria control due to their effect in reducing the transfer of gametocytes, the sexual stage of the parasite, transmitted from humans to an ood meal , 30. Rapood meal , 30 and,ood meal . Despiteitiation , 33.P. falciparum msp1 and msp2 should be regularly monitored with respect to treatment outcomes and/or efficacy studies in regions, where there are ongoing changes in the malaria epidemiology, as mentioned above [msp1 MAD20 allelic family (99%) and the msp2 3D7 allelic family was predominant (97%). These results are in accordance with an earlier study from Vietnam [msp1 and msp2 allele distribution over time [Given the increasing importance of antimalarial drugs in the region, the genetic diversity of ed above , 9. When Vietnam . Anotherver time . For exaver time , 35. Nevver time .msp1 and msp2 in this study was 2.6 and 1.1, respectively, and these differ from those in Thailand and Laos, which are geographically closer to Vietnam. For msp1, the MOI in Thailand and Laos are 1.7 and 1.6, respectively, and for msp2, the MOI in Thailand is 2.5 [Studies with multiplicity of infections (MOI) are rare in Vietnam. The overall MOI of d is 2.5 , 37. TheP. falciparum msp1 and msp2. Routine monitoring of parasite genetic diversity has important implications for linking treatment outcomes and/or efficacy studies as the epidemiology of malaria changes.Against the background of the increasing importance of resistance to antimalarial drugs, which is reported in the region, a low to moderate genetic diversity was observed in the focal endemic provinces in Vietnam based on the genetic diversity of


Progress in controlling malaria has stalled in recent years. Today the malaria burden is increasingly concentrated in a few countries, including Burkina Faso, where malaria is not declining. A cohort study was conducted to identify risk factors for malaria infection in children in southwest Burkina Faso, an area with high insecticide-treated net (ITN) coverage and insecticide-resistant vectors.Plasmodium falciparum infection was measured in 252 children aged 5 to 15 years, using active and passive detection, during the 2017 transmission season, following clearance of infection. Demographic, socio-economic, environmental, and entomological risk factors, including use of ITNs and insecticide resistance were monitored.Incidence of P. falciparum infection was 2.78 episodes per child (95% CI = 2.66–2.91) by microscopy, and 3.11 (95% CI = 2.95–3.28) by polymerase chain reaction (PCR). The entomological inoculation rate (EIR) was 80.4 infective bites per child over the six-month malaria transmission season. At baseline, 80.6% of children were reported as sleeping under an ITN the previous night, although at the last survey, 23.3% of nets were in poor condition and considered no longer protective. No association was found between the rate of P. falciparum infection and either EIR (incidence rate ratio (IRR): 1.00, 95% CI: 1.00–1.00, p = 0.08) or mortality in WHO tube tests when vectors were exposed to 0.05% deltamethrin . Travel history  and higher socio-economic status were associated with an increased risk of P. falciparum infection .During the six-month follow-up period, the overall incidence of P. falciparum infection remains overwhelmingly high in the study area. The study findings suggest that because of the exceptionally high levels of malaria transmission in the study area, malaria elimination cannot be achieved solely by mass deployment of ITNs and additional control measures are needed.Incidence of  Malaria remains an acute public health problem throughout sub-Saharan Africa (SSA) with an estimated 213 million cases and 380,000 deaths in 2018 –6.Despite the known protective efficacy of ITNs, the 2010 ITN universal coverage campaign in Burkina Faso did not decrease malaria and all-cause mortality in children younger than 5 years old, even with 92% of children reportedly sleeping under ITNs . The stuPlasmodium falciparum infection in an area of persistent and intense malaria transmission in rural Burkina Faso, with high ITN coverage and high levels of insecticide resistance. The study findings may help to identify potential opportunities for improving malaria control in Burkina Faso and other countries in SSA experiencing persistently high malaria transmission.A cohort study of children was conducted to determine risk factors for Plasmodium falciparum accounts for 90% of cases [Anopheles gambiae sensu stricto (s.s.), but Anopheles coluzzii is also found [The study was conducted from June to December 2017 in Banfora Health District, Cascades region, southwest Burkina Faso . Older children were selected as they have the highest malaria incidence, relatively low immunity and contribute substantially to transmission , while cPlasmodium infections were recorded using both active and passive detection. Study children were visited at home every 2 weeks by fieldworkers during the peak transmission season, from July-December 2017. At each visit, fieldworkers measured a child’s axillary temperature and prepared two blood films and a filter paper blood spot. Children with an axillary temperature ≥ 37.5 °C or history of fever in the previous 48 h were advised to visit the local health centre and, if they were a malaria case, treated with AL, following National Malaria Control Programme (NMCP) guidelines [Symptomatic and asymptomatic idelines . If a chidelines . ClinicaCentre National de Recherche et de Formation sur le Paludisme (CNRFP) in Banfora. Parasite counts were recorded per high power field and 100 fields counted before a slide was declared negative. Two blood slides from each subject were read separately by two microscopists. Discrepancies in positive and negative reads and parasite counts differing by more than tenfold between the two reads were resolved by the supervisor. Filter paper blood samples were analysed for the presence of 18 s rRNA gene using PCR [Thick blood films were stained with Giemsa and examined under 1000-fold magnification by experienced microscopists at the sing PCR .At enrolment, sociodemographic characteristics of the child  and their caregiver  were recorded. Questionnaires recorded the presence or absence of large domestic animals  within 5 m of each study child’s house and the materials used to construct the building in which the study child slept, including presence of a metal roof, eaves and door and window screening. The head of household completed a questionnaire on their asset ownership, house construction and other variables, following standard procedures used in the Burkina Faso Malaria Indicator Survey (MIS) . Houses An. gambiae sensu lato (s.l.) at the CNRFP insectaries in Banfora using the WHO efficacy requirement of ≥ 80% mortality [Use of an ITN the previous night by each study child and use of spatial or topical repellents were recorded at enrolment and each active visit. Survivorship and integrity of each study child’s ITN was measured in July, October and December. Each net was recorded as being in use , in storage, being washed, or lost. Loss was categorized as: (i) net given away voluntarily; (ii) net stolen; or, (iii) net destroyed, discarded or used for alternative purposes. Fabric integrity of the net was assessed by counting the number of holes and their size according to WHO guidance . A weighortality .An. gambiae s.l. typed to species using PCR. The presence of sporozoites in An. gambiae s.l. was determined using an enzyme-linked immunosorbent assay [Mosquitoes were sampled with CDC light traps, positioned with the light 1 m above the ground at the foot end of the bed of each study child sleeping under an ITN from 19.00 to 06.00, every 4 weeks from July to December 2017. In addition, each child’s net was systematically searched for mosquitoes between 06.00 and 07.00 every 4 weeks using a torch. Mosquitoes were identified morphologically using established keys and female nt assay . Larval An. gambiae s.l. mosquitoes reared from immatures collected in seven study villages (larvae were not found in the other three villages).Phenotypic insecticide resistance was measured using WHO tube tests as per standard procedures . Assays Data were collected on android personal digital assistants programmed using KoboCollect and included drop-down boxes and consistency checks to reduce data entry errors. Following cleaning, the dataset was locked and saved in Microsoft Access. An analytical plan was developed prior to data analysis.P. falciparum infection during the transmission season, detected using active and passive case detection. PCR-confirmed P. falciparum incidence rate was a secondary outcome. After ACT treatment, further infections were censored for 28 days since infections during this time were most likely due to recrudescent parasites. Time at risk was also censored for time that study children spent away from their compound should they be found to be absent at the two-weekly home visits. The entomological inoculation rate (EIR) or estimated number of infectious bites per study child during the transmission season was calculated using the formula EIR = MaSd where Ma is the human biting rate, estimated from the arithmetic mean number of female An. gambiae s.l. caught per light trap night across the six-month transmission season, where S is the proportion of female An. gambiae s.l. found to be sporozoite positive by village and d is the number of days in the transmission season (n). Cone bioassay results for the netting pieces from each sampled net were pooled by village and by net type. QGIS Geographic Information System (QGIS Development Team (2019), Open Source Geospatial Foundation Project) was used to determine distances between the child’s home and aquatic habitats. Distance to the nearest health facility was determined based on the shortest distance to travel by road. Principal component analysis was used to calculate a SES factor score based on asset ownership and household characteristics. SES factor scores (ranging from -1.8 to 3.2) were ranked and households divided into five equal wealth quintiles .The primary outcome was the incidence rate of microscopically confirmed P. falciparum infection incidence rate, adjusting for clustering by village. Risk factors assessed were: child’s age, gender, ethnic group, religion, caregiver’s education and occupation, wealth quintile and SES factor score, use of ITNs and other personal protection, ITN integrity, number of people sleeping in the room with the study child, housing materials , presence of cows, horses, sheep or goats within 5 m of the child’s home, Euclidean distance to the nearest aquatic habitat with larval anophelines, distance by road to the nearest health centre, EIR at village level, and percentage mortality of An. gambiae s.l. mosquitoes from the village when exposed to 0.05% deltamethrin in a WHO tube test. A multivariate regression model was developed using a forward stepwise approach. Statistical analysis was carried out in Stata 15 .Mean values were compared using a t-test and proportions compared using Chi-squared tests. Poisson regression models were used to identify risk factors associated with P. falciparum infections per child during the transmission season, an intraclass correlation coefficient of 0.1 (design effect of 3.4), and a loss to follow-up of 10%, the study had greater than 90% power to detect effect sizes of > 50% at the 5% level of significance, assuming 50% prevalence of the risk factor of interest in the population using the formula for comparison of two rates [Assuming two wo rates . The stuwo rates .Of 300 randomly selected children, 252 children aged 5–15 years old were enrolled in the cohort after confirmation of parasite clearance by PCR  had one P. falciparum infection, 139 (55.2%) children experienced two P. falciparum infections, 75 (29.8%) experienced three P. falciparum infections, and 4 (1.6%) experienced four P. falciparum infections. A total of 550 P. falciparum infections were identified using microscopy while 608 infections were identified using PCR. Of the 550 P. falciparum infections confirmed using microscopy, 528 (96.0%) were detected using active case detection and 22 (4.0%) detected using passive case detection. Infections detected passively had a higher geometric mean P. falciparum density  compared to those detected through active surveillance . The P. falciparum infection incidence rate was 2.78 episodes per child during the six-month transmission season (95% CI = 2.66–2.91) by microscopy, and 3.11 episodes (95% CI = 2.95–3.28) by PCR. Among children suffering from at least one infection, the median time to first infection detected by microscopy was 27 days (range 14–123 days).During the follow-up period, 249 of the 252 children experienced at least one p < 0.001). Most study children’s bed nets were either Permanet 2.0 (52.0%) or Olyset net (16.7%), with 85.5% provided by the NMCP and 4.0% purchased on the open market. Sleeping place inspections in July, October and December found 87.8% of children had a bed net hanging over their sleeping place (638 out of 727 observations). The most common reason for not having a net hanging was loss of the net , while 15.7% (14/89 observations) of nets were stored and 2.2% (2/89 observations) being washed. The proportions lost, stored or being washed did not differ across the three surveys. Of the nets that were reported as being lost, 76.3% (45/59 observations) were destroyed, 22.0% (13/59 observations) stolen and 1.7% (1/59 observations) given to friends or family. It was common for children to share a bed net with siblings and 61.4% of children slept with three or more children. At the last survey in December, 62.2% (155/249) of ITNs were in good condition, 14.5% (36/249) were damaged and 23.3% (58/249) badly torn and non-protective. Net condition did not differ significantly by survey round, although there was a tendency towards net deterioration over the study period. Cone testing of a random sample of 26 children’s bed nets gave a mean knockdown at 1 h of 76.5% (95% CI = 61.9–91.1%) and mortality after 24 h of 42.2% (95% CI = 18.8–65.6%) for Olyset nets  and mean knockdown of 85.2% (95% CI = 77.9–92.4%) and 24 h mortality of 39.9% (95% CI = 27.7–52.0%) for Permanet .At baseline, 80.6% of caregivers reported that the study child slept under an ITN the previous night. Reported ITN use the previous night at the baseline survey was greater than 82% across the four lowest SES quintiles compared to the least poor children  were An. gambiae s.s. and 2,590 (34.0%) An. coluzzii. 3.3% of the An. gambiae s.l. were sporozoite positive. The overall EIR in the study area was 80.4 infective bites/child over the six-month transmission season. The village-level EIR ranged from 40.8 in Timperba to 191.9 in Tondoura. Monthly systematic searches of study children’s bed nets in the early morning did not collect any mosquitoes. Anopheles gambiae s.l. were resistant to 0.05% deltamethrin in WHO tube tests with a mean corrected mortality of 52.3% (95% CI = 26.4–78.2%), with values ranging from 27.2% mortality in Toumousseni to 93.0% in Yendere.A total of 20,929 mosquitoes were caught from 1,151 trap collections with 16,270 of these being ust Fig. . TengrelThe mean number of water bodies with anopheline larvae within 500 m of study houses was 8.7 (95% CI = 7.0–10.3). The average distance by road from the study house to the nearest clinic was 3.6 km (95% CI = 3.2–4.0), although for the villages of Toundoura and Gouera, which did not have a clinic nearby, the distance was 7.5 km and 9.4 km, respectively.P. falciparum infection in the study children and both remained significant in a multivariate model : 1.52, 95% CI: 1.45–1.59, p < 0.001). The least poor children were found to have a higher incidence of P. falciparum infection. A 1 unit increase in the SES factor score was associated with a 5% increase in infection rate .Univariate Poisson regression analysis identified two variables associated with incidence of el Table . AlthougP. falciparum infection was observed, with only three of the 252 cohort children remaining free from infection during the six-month follow-up period and 86.5% of children experiencing two or more infections. This high incidence of infection, despite regular retreatment with an effective anti-malarial if a child was infected, indicates the high force of infection in the study area and is supported by an estimated EIR of 80.4 infective bites per child during the transmission season. Similarly high levels of P. falciparum infection and malaria morbidity have been reported from other recent studies in Burkina Faso in areas of high ITN coverage [An extremely high incidence of coverage –36.P. falciparum infection did not elucidate any strong associations. This may be because of the overwhelming force of infection in the study area which meant that all the children were at extremely high risk of infection. Nevertheless, there was an indication of increased malaria risk in the few children that travelled overnight during the study period. Whether staying with family or for farming reasons, these children may be at higher risk of P. falciparum infection because they lack or have limited access to malaria prevention including ITNs, and diagnosis and treatment [P. falciparum infection was unexpected since it is widely reported that the least poor children are typically at lower malaria risk than the most poor children [P. falciparum infection risk is not explained by a greater number of passively detected infections in these children. Passive cases contributed only 4% of all cases identified and there were no differences in this proportion across wealth quintiles. Plasmodium falciparum infection rates may be higher in the least poor children due to lower ITN use than the most poor children. Only 53.2% of the least poor children were reported to use an ITN the previous night compared to over 82.6% of children in the lowest quintile (most poor).The assessment of socio-demographic, entomological and environmental risk factors for reatment . Overnigreatment . The finchildren . The samP. falciparum infection risk and either of the two entomological variables hypothesized to impact on the primary outcome, namely EIR and insecticide resistance. The lack of association with EIR and vector density is counter-intuitive given it has been demonstrated in several other studies [P. falciparum infection was not greater in villages with higher levels of resistance. This finding may be partly due to the relatively small number of villages surveyed for insecticide resistance, and the resulting lack of statistical power, although no evidence of an association between malaria and insecticide resistance has been found in large area studies in the Sudan and Kenya [An. gambiae s.l. in holed ITNs, with significantly higher numbers in nets with holes greater than 50 sq cm in size [No association was found between  studies , 41, butnd Kenya . Despitend Kenya . Another in size . Althoug in size , and hig in size –47.P. falciparum infections than those that slept under good nets.ITN use was high with 80.6% of caregivers reporting that the study child slept under an ITN the previous night at the baseline survey. This is in line with other surveys including the 2017–8 MIS survey, which reported that 76.0% children under 5 years slept under an ITN the previous night . DespiteThe study has several limitations. Firstly, and most importantly, the study was probably underpowered to detect small risk factors. The sample size calculation assumed 50% prevalence of risk factors in the study population, while the study children were, in reality, relatively homogeneous with regard to risk factors shown to be important in other studies, for example, house construction. Secondly, while caregivers reported high compliance with ITN use, assessing ITN use is notoriously difficult and this may have impacted on the ability to identify this as an important risk factor. Indeed, social desirability bias and other forms of error mean that surveys are likely to substantially overestimate use , and objBurkina Faso is one of ten SSA countries designated as a High Burden High Impact country with a response plan including increased political will, strategic use of data to deploy tools for maximum impact and a multi-sectoral approach. While this study generates useful data on malaria burden in Banfora District, it is clear that additional tools will be needed to reduce this burden. Dual-active ITNs (pyrethroid plus piperonyl butoxide (PBO) or pyrethroid plus chlofenapyr) are now being deployed in the study area. PBO-pyrethroid ITNs have been shown to be more effective in reducing malaria than pyrethroid-only ITNs in areas of pyrethroid-resistant vectors , 51, andThe study found overwhelmingly high levels of malaria transmission in Banfora district, in southwest Burkina Faso, and the risk factor survey did not identify any risk factors for further investigation to reduce the malaria burden. The findings have implications for achievement of the ambitious goals set out in the WHO Global Technical Strategy . Malaria