Malaria is caused by Plasmodium species and transmitted by Anopheles mosquitoes, which is the most common medical concern all over the world, including in Ethiopia. The current systematic review's goal was to determine the overall malaria prevalence and Plasmodium species distribution in Ethiopia's southern area. To achieve these objectives, 716 articles were manually searched from online databases such as PubMed, Scopus, Google Scholar, Science Direct, and Web of Science. The pooled metalogistic regression was calculated with the STATA 16 software to present the pooled prevalence with a 95% confidence interval (CI). Eighteen full-text articles met the inclusion criteria and were included in the study, out of the 716 studies initially identified. The majority of the articles in the systematic review used a cross-sectional study design, with sample sizes ranging from 160 to 583,668 participants. The study's lowest and highest malaria prevalence was 0.93% and 82.84%, respectively. During the current systematic review, the estimated malaria prevalence was 19.19% (95% CI: 14.67–23.70). There were 263,476 positive individuals in the study, accounting for 148,734, 106,946, and 7,796 cases of P. falciparum, P. vivax, and mixed infections, respectively. The overall prevalence of P. falciparum and P. vivax was 8.97%  and 7.94% , respectively. According to the systematic review, the most predominant Plasmodium species responsible for malaria disease in the study area was P. falciparum. The highest malaria rates were found in this systematic review. In the systematic review, P. falciparum was the most dominant Plasmodium species that was responsible for malaria disease in the study area. This systematic review indicates the highest malaria prevalence in the southern regions of Ethiopia. Therefore, existing malaria prevention and control strategies in the southern region of Ethiopia should be revised. Plasmodium parasites [Anopheles mosquito bites [Plasmodium falciparum and Plasmodium vivax are the most widely distributed and well-known malaria-causing species in Ethiopia, accounting for 60% and 40 percent of cases, respectively [Malaria is a mosquito-borne disease caused by five arasites  and tranto bites –3. PlasmMalaria disease transmission is influenced by environmental factors. The key factors that affect the spread of malaria infections include topography, rainfall, climate, and socioeconomic conditions of the population –8. In EtFrom 2001 to 2011, the average rate of new malaria cases confirmed in Ethiopia declined from 78,325 in 2001-2005 to 30,780 in 2011. The slide positivity rate was 24% in 2001 and 11% in 2011 . MalariaPlasmodium species in the Southern region of Ethiopia. In the Southern region of Ethiopia, different studies were conducted to know the prevalence rate of malaria with great differences and varying findings. But, there is no previous systematic review that indicates the prevalence of malaria and the distribution of Plasmodium species in the regions. Due to this, the current review was designed to know the prevalence of malaria and the distribution of Plasmodium species in the region. The finding of the review has significant value in malaria prevention and control programs at the country and international levels. It is also useful to evaluate the progress of the region towards achieving the regional and national targets and to take immediate action in the planning and implementation of prevention and control strategies.Ethiopia is currently extending the 2020 malaria eradication strategy to 2030. As a result, the goal of this review was to assess the prevalence of malaria and the distribution of 2. The region features a variety of climatic and agroecological zones, including Kefil Bereha, Kolla, Woina Dega, Dega, and Wurch, which are located between 350 and 4200 meters above sea level. Kolla and Woina Dega make up the majority of the region, with Kolla and Woina Dega accounting for around 80% of the total (JICA. 2012). The majority of the land is malarious because it is less than 2,000 meters above sea level. Currently, Ethiopia's population is estimated to be over 90 million, with approximately 68% residing in malaria-prone areas, including the Southern region [The Southern region of Ethiopia is located in the southern and southwestern parts of the country. It lies between 4.43-8.53 degrees' North latitude and 34.88-39.14 degrees' East longitude and is bordered by Kenya in the South, the Sudan in the South West, Gambella region in the North West and surrounded by the Oromia region in the North West, North, and East. Based on the 2007 census conducted by the Central Statistical Agency of Ethiopia, the Southern region has an estimated total population of 14,929,548, of whom 7,425,918 were men and 7,503,630 women. The total area of the region is about 110,932 kmn region . All thePlasmodium species,” “malaria parasites,” and “malaria epidemiology in Ethiopia.”The search articles comprised different study designs  with reported malaria prevalence results (positive-negative cases and parasites) in well-defined populations. Articles were searched without time restriction by using the key terms “malaria prevalence,” “burden of malaria,” “distribution of From the total reviewed articles, those studies published in peer-reviewed journals such as PubMed, Scopus, Google Scholar, Science Direct, Web of Science, and other related journals that report the prevalence of malaria in the Southern region of Ethiopia, were included in the review.All studies included in the systematic review were original research articles published in English and contained the basic information concerning sample size, diagnostic methods, prevalence, and status of malaria infection among all target populations, pregnant women, and children in different parts of the Southern region of Ethiopia.The study conducted among unknown malaria detection methods, an unknown sample size, and a lack of clear figures about infected cases were excluded in this review. After searching PubMed, Google Scholar, Science Direct, Web of Science, and Scopus using keywords, articles were screened by title and abstract to transfer the articles in the full-text review. The quality of articles was assessed using Joana Brigg's Institute (JBI) critical appraisal checklist for simple prevalence .P. falciparum, the prevalence of P. vivax, the prevalence of mixed infection, and type of diagnostic method used. Once the data were extracted using Microsoft Excel, data manipulation and statistical analysis were done using STATA 16 version software.The data extraction protocol was developed by the researcher and evaluated by Ass. Professor Wubetu Barude and Ass. Professor Ritbano Ahmed. This extraction protocol consists of the name of the first author, year of publication, study area, study group, study design, target sample, sample size, the prevalence of malaria, prevalence of The systematic review contains published articles on the prevalence of malaria among patients in the Southern region. A total of 716 articles were retrieved from online databases using manual searching. Out of these studies, 121 articles were excluded due to duplication records. From the remaining 595 articles, 507 articles were excluded due to their study area, titles, and abstracts. Due to this, only 88 articles were eligible for full-text assessment which is done in different parts of Ethiopia. Of the 88 eligible articles, 62 studies were excluded from the review because they are done out in the Southern region of Ethiopia. Only twenty-six full-text articles were screened which were done in the Southern region of Ethiopia. Finally, eighteen full-text articles were included in this systematic review because the remaining ten are not eligible and excluded in the final systematic review due to a lack of clear data, such as the prevalence of malaria, positive cases, total population, and study design as shown in Plasmodium species, statistical methods used, software used for computation and carried out in different parts of Southern region of Ethiopia as indicated in A total of eighteen articles were included in this systematic review. The papers which were considered for this review, including the author's name, study design, target population, sample size, prevalence of malaria, distribution of Plasmodium species [From the total articles reviewed, twelve studies used a cross-sectional design, five used retrospectives, and one study used a longitudinal research design. Out of all included studies, a study conducted in Arbaminch General Hospital and Arbaminch health center Southern Ethiopia had the least sample size with 160 study participants . However species , 23–26.I2 = 99.99% and p value ≤ 0.001) across studies was observed in the analysis.In the reviewed articles, a total of 1,622,356 study participants were tested for malaria infection from the eligible articles. Among these, 263,476 were positive for malaria infection. The overall malaria prevalence in the aspect of gender preference, men were more affected than females, which accounts for 136,683 (51.87%) males and 126,793 (48.12%) females. Concerning age groups, those individuals whose ages were greater than 15 were highly affected by malaria , 24, 25., Plasmodium falciparum and Plasmodium vivax were the two most predominant and widely occurred parasites of Plasmodium species responsible for malaria cases in the Southern region of Ethiopia. Plasmodium falciparum and P. vivax coexist as major parasite species in the Southern region of Ethiopia. Plasmodium falciparum was the dominant parasite that accounted for 8.97% of the pooled prevalence estimate with 95%  and I2 = 99.9% between-study heterogeneity. P. vivax is the second most dominant malaria parasite in the Southern region of Ethiopia which accounted for 7.94%  as shown in Figures In the current systematic review5% CI: 6.1, 11.63 P. vivax and P. falciparum infection, while the prevalence of mixed infections was not reported in four studies. Out of these positive (40.59%), 106,946 were positive for P. vivax. Plasmodium falciparum accounts for (56.45%) of 148,734 total positive cases and the remaining (2.96%) 7,796 of them had mixed infection. In these systematic reviews, the highest and lowest distributions of Plasmodium falciparum was 71.79% and 12.36%, respectively. In all reviewed journals, there was a distribution of mixed plasmodium parasites except those studied by [Plasmodium species was more common in males than females. Plasmodium vivax was more dominate in the age greater than 15 years [Plasmodium falciparum was dominant in the child whose age less than 15 years [As indicated in udied by , 17, 24.15 years , 21, 23,15 years , 20.Plasmodium species in the Southern region of Ethiopia. This systematic review was conducted using eighteen full-text articles to determine the pooled prevalence of malaria among individuals in the Southern region of Ethiopia.The present systematic review assessed the pooled prevalence of malaria and distribution of Malaria causes serious complications in humans, such as severe anemia, acute renal failure, hypoglycemia , loss ofIn the current systematic review, the pooled prevalence of malaria in the Southern region of Ethiopia was 19.19%. This is greater than the systematic review and meta-analysis conducted on malaria prevalence among adults, children, and pregnant women in Ethiopia, which results in 13.61%, 9.07%, and 12.72%, respectively –32.This review is nearly similar to the research conducted in Dilla town and the surrounding rural areas, Gedeo Zone, Southern Ethiopia, which was 16% . On the P. falciparum (9.17%), P. vivax (6.60%), and mixed (0.48%) parasites were found in proportions of 56.45%, 40.59%, and 2.96%, respectively. The results of this systematic review showed that P. falciparum and P. vivax have around 15% variation but greater variation compared to the mixed parasites. The overall estimated distribution of P. falciparum and P. vivax in the current systematic review was similar to nine of the articles included in the review [Plasmodium species in this systematic review is also similar to studies conducted in Bichena Primary Hospital, Amhara Region [P. falciparum was 8.97% , P. vivax 7.94%  parasites resulted in a proportion of 56.45% and 40.59%, respectively. These results were nearly similar to the previous predictions of P. falciparum (60%) and P. vivax (40%) in Ethiopia [The overall prevalence of iparum 9.%, P. vive review , 24–28 ae review –23, 27. a Region , Tselemta Region , Gambella Region , and Kala Region . SimilarEthiopia , 37, 38.Anopheles mosquito vectors.The estimated malaria prevalence over time is not consistent. This is because malaria infection in Ethiopia is highly variable and unstable, and the occurrence of epidemics over several locations  of the country. Malaria prevalence in Ethiopia is seasonal –42. The The studies included in this systematic review showed high heterogeneity in malaria prevalence. The possible source of heterogeneity might be related to the seasons in which each of the included studies was conducted because some of the studies were conducted during the high malaria transmission seasons, whereas the rest of included studies were done during the low malaria transmission seasons and also the methods of diagnosis.Plasmodium species agrees with the national figures. This systematic review showed a high prevalence of malaria in the Southern regions of Ethiopia. Therefore, previous prevention and control strategies could be revised and new strategies implemented. In addition, to meet the WHO's 2030 goal, the region requires independent and stand-alone malaria prevention and control task forces.The overall estimated prevalence of malaria was 19.19% . The majority of the studies in this review met the quality criteria, and there was no publication bias. Based on the included articles in the review, microscopic diagnosis of malaria cases was common in the study area. The pooled distribution of


Primaquine is activated by CYP2D6 in the hepatocytes. In Korea, primaquine is the only hypnozoitocidal agent used for patients with vivax malaria. Thus, patients with poor CYP2D6 activity could have an increased risk of primaquine failure and subsequent relapse. The study sought to identify the association between CYP2D6 phenotype and recurrence of malaria in Korean patients.A total of 102 patients with vivax malaria were prospectively enrolled from eight institutions in Korea. An additional 38 blood samples from patients with recurred vivax malaria were provided by the Korea Disease Control and Prevention Agency. Malaria recurrence was defined as more than one episode of vivax malaria in the same or consecutive years. CYP2D6 star alleles, phenotypes, and activity scores were examined.p = 0.02). Patients with lower CYP2D6 activity had a higher probability of recurrence (p = 0.029).Genotyping for CYP2D6 was successful in 101 of the prospectively enrolled patients and 38 samples from the Korea Disease Control and Prevention Agency, of which 91 were included in the no-recurrence group and 48 were included in the recurrence group. Reduced CYP2D6 activity (intermediate metabolizer) phenotype was more common in the recurrence group than in the no-recurrence group ; Plasmodium vivax recurrence in Korea.This study suggests that CYP2D6 polymorphism may affect primaquine efficacy and thus The online version contains supplementary material available at 10.1186/s12936-022-04311-6. Plasmodium vivax is the only parasite that causes human malaria in South Korea. Since the re-emergence of P. vivax infection in 1993, the number of annual malaria cases peaked in the early 2000s and steadily decreased thereafter. However, malaria still occurs in around 400–500 patients annually in Korea and causes significant morbidities.Plasmodium falciparum, P. vivax is known for its ability to cause recurrent clinical malaria from the activation of dormant liver stage parasites, known as hypnozoites. Currently, primaquine is the only approved hypnozoitocidal agent in Korea. Despite the use of primaquine, relapse of clinical malaria is reported in the range of 1.6–4.5% and is considered a major obstacle to the eradication of malaria in Korea [Unlike in Korea . The reaRecently, metabolism of primaquine has been extensively studied to determine whether reductions in active metabolites may affect the anti-malarial properties of primaquine. Primaquine is metabolized into the active form via Cytochrome P450 isoenzyme 2D6 (CYP2D6) in the liver. CYP2D6 is a polymorphic gene with more than 149 known alleles that determine the CYP2D6 phenotype  that has different levels of activity for drug metabolization [In this study, the association between the CYP2D6 phenotype and the risk of vivax malaria relapse in Korean patients was investigated.This study was performed at eight hospitals  located in Gyeonggi Province and Seoul in South Korea, where malaria was endemic from 2018 to 2020.Patients > 16 years of age diagnosed with vivax malaria during the study period were invited to participate in the study. Vivax malaria was diagnosed by peripheral blood smears (thick and thin smears stained with Giemsa stain) and/or polymerase chain reaction (PCR) analysis. Parasite density was determined by Giemsa-stained blood slides at a magnification of 1000× using WHO-recommended methods. Demographic information including age, gender and body weight were collected. Previous work has shown the prevalence of glucose-6-phosphate dehydrogenase (G6PD) deficiency in Koreans to be extremely low (near 0%) , 8, thusRecurrence of malaria was identified by reviewing the medical records at each hospital and the National Infectious Disease Notification System. Malaria, indigenous or imported, is a mandatory notifiable disease in Korea and all individuals with malaria are reported to the Korea Diseases Control and Prevention Agency (KDCA) using the infectious disease reporting systems.P. vivax infections contracted in the previous malaria season that had a subsequent prolonged incubation, rather than new infection [P. vivax malaria episode within the same year or the previous year were considered as recurred patients.Malaria in Korea is seasonal, with 94% of cases occurring between 1993 and 2017 reported between May and October when malaria vectors are active. This seasonal distribution pattern of malaria cases has not changed over time. Thus, indigenous vivax malaria cases that occurred in the first four months of the year, winter and spring, might comprise Considering the low recurrence rate of vivax malaria in Korea (< 5%), there was an attempt to collect more blood samples of patients who were suspected of having recurred vivax malaria. Along with the disease notification to the KDCA, blood samples of most malaria patients are sent to KDCA for confirmation of malaria and further analysis. Accordingly, the blood samples of 38 patients who were suspected of having recurred vivax malaria with a record of a previous vivax malaria episode within the same year or the previous year between 2015 and 2018, as with the prospective enrolled patients, were provided by the KDCA and included in the analysis.CYP2D6 genotyping analysis was commissioned to SPMED Co. Ltd . Patient DNA was isolated and purified from blood samples using SPMED™ Genotyping Kit: CYP2D6  according to the manufacturer’s instructions and CYP2D6 *2, *3, *4, *5 (deletion), *6, *9, *10, *14, *17, *18, *21, *29, *41, *49, *52, *60, *XN (duplicate) were analysed. These 17-star alleles cover most (99.3%) of the major mutation in Asian races based on previous results .Previously, full sequencing data from Korea have shown the *10 promoter and intron SNPs to be linked, such that the *10 alleles detected above is the CYP2D6*10b allele  and remaBriefly, genomic DNA was extracted according to the manufacturer’s instructions of QIAgen Mini Kit . The extracted DNA was stored at − 20 °C. PCR reactions were performed with 0.5 µl genomic DNA, 10 µl of PCR amplification primer mix, 4 µl of each CYP2D6 amplification primer mix. The total volume was adjusted to 20 µl with nuclease-free water. The PCR reaction protocol was controlled with an initial denaturation step at 94 °C for 5 min, followed by 35 cycles with first set at 98 °C for 20 s, 64 °C for 30 s, and 72 °C for 30 s, and a final extension reaction at 72 °C for 5 min. The amplified products were separated based on 1% agarose gel electrophoresis and visualized by ethidium bromide under a UV transilluminator. Only the sequences that showed identical size and 90% or more similar thickness with the wild type DNA were used for subsequent analysis.Specific primers were used for single-base extension using SNaPshot kit , according to manufacturer’s instructions. Reactions were carried out in a final volume of 13.5 µl, containing 1 µl of the SNaPshot Multiplex Reagent, 1 µl of the primer mix, 7.5 µl of purified product and 4 µl of 1/2 term buffer. Purified products underwent capillary electrophoresis on 3500 DX Series Genetic Analyzer  using the standard fragment analysis protocol.GeneMapper software  was used for allele-specific gene expression analysis and calculated the peak color of CYP2D6 single-nucleotide variant (SNV) to the peak density of the wild type DNA. The CYP2D6*1 allele was set when no nucleotide change was observed in all genotyped SNPs.CYP2D6 genotype identified levels of enzymatic activity qualitatively by the activity score model, where a score of 0.0, 0.25–1.0, 1.25–2.25, and > 2.25 each indicate a poor metabolizer (PM), intermediate metabolizer (IM), normal metabolizer (NM), and ultrarapid metabolizer (UM), respectively .U test. For analysing the trends between categorical variables, the Cochran-Armitage test was used. All tests of significance were two-tailed, and differences were considered statistically significant at p < 0.05. All statistical analyses were performed using SPSS Statistics version 23 .Descriptive statistics are expressed as means, medians or proportions. The associations between categorical data were tested using the χ-squared test or Mann-Whitney This study was approved by the ethical committees of each hospital . Signed written informed consents were obtained from prospectively registered patients or their guardians before the study. A waiver of patient consent was granted for data that were retrospectively collected.A total of 102 patients with vivax malaria ; recurred, n = 10 (9.8%)) were prospectively enrolled in the study, including one patient who had four episodes of malaria attack. Table CYP2D6 analysis was not available in one patient in the recurred group, and thus 101 patients underwent CYP2D6 analysis, whose CYP2D6 genetic profiles, activity score, phenotypes, and recurrence status are summarized in Table p = 0.02)   patients showing a score lower than 1.5 and 31 (22.3%) patients showing a score of 1.5 or higher. When the association between recurrence and the activity scores was analysed, patients with higher activity scores were significantly less likely to have a recurrence ) in this study. Genotypes *1/*10B, *1/*1, and *2/*10B were found in 23.7, 7.9 and 7.2%, respectively. These genotypes were also common in other Asian countries, such as China, Japan and Thailand . The genIn addition to the CYP2D6 phenotype, many factors could affect the efficacy of primaquine. The total dose of primaquine must be considered by priority. In Korea, primaquine 15 mg for 14 days had been traditionally recommended . HoweverP. vivax infection, which includes relapse, reactivation of the hypnozoites, and re-infection were included. Several studies identified that the molecular markers of P. vivax could be either homogenous or heterogeneous between the primary episode and relapsed episodes [One limitation of this study is that detailed information could not be obtained on other drugs that the patients may have used. Drug-drug interaction could significantly affect the metabolism of primaquine, and the CYP2D6 enzyme is known to be inhibited by commonly used drugs such as fluoxetine, duloxetine and terbinafine . Thus, iepisodes , 25. Curepisodes .This study found that patients deemed as intermediate metabolizers as determined by CYP2D6 enzyme activity had a higher risk of vivax malaria recurrence than did those deemed as normal or ultrarapid metabolizers. CYP2D6-dependent metabolism of primaquine may be a key determinant of the anti-hypnozoite activity of primaquine that prevents vivax malaria relapse. Additional study for the optimization of primaquine efficacy is urgently needed.Additional file 1: Table S1. Diagnostic date for each episode of vivax malaria for 10 prospectively enrolled relapsed patients.Additional file 2: Table S2. The CYP2D6 alleles of the entire samples defined by tagging single nucleotide polymorphisms (tSNPs).


Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net.Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0–81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8–96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0–88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6–86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development.Malaria Screener reached 74.1% (95% CI 63.5–83.0) accuracy in detecting Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.The online version contains supplementary material available at 10.1186/s12936-023-04446-0. Microscopic examination of Giemsa-stained blood films is a primary diagnostic tool for malaria case management . HoweverPlasmodium falciparum malaria by analysing a Giemsa-stained thick smear. Later, this prototype system evolved to a more advanced version and was renamed EasyScan Go  was included, and a negative control  was included as well.DNA extraction was performed from DBS using a QIAamp DNA extraction kit following the manufacturer’s instructions . Nested PCR was performed to identify malaria species following Snounou et al. using previously published primers . In eachAn object-level (parasite) diagnosis was performed first within each blood smear image; then, results were combined across images to determine whether a patient was positive or negative.The deep learning classifier provided a confidence score for each detection in an image. The average score of all detections was computed and used as the confidence score of an image Eq. . Then, ti prediction likelihood of parasite candidate.p1 number of parasite candidates in the image.nimg confidence score of the image.Conf2 number of images captured for the smear.npatient confidence score of the smear.ConfSystem performance was evaluated on the patient-level based on accuracy, sensitivity, and specificity. Microscopy and PCR were used as reference standards for evaluation, where decisions of WHO Level 1 microscopists were used for microscopy.P. falciparum, 38 were P. vivax, and one was a P. falciparum + P. vivax mixed infection.A total of 380 slides were prepared with blood collected from the 190 patients of the two participating sites. 103 (54.2%) patients were male, and 87 (45.8%) were female. The average age of the patients was 29.8, with a standard deviation of 15.6. Specifically, two slides were collected for each patient, one for analysis and another for backup, containing both a thin and a thick smear. Of the 190 slides used for analysis, 100 (52.6%) tested positive by expert microscopy, and 90 (47.4%) tested negative. Among the positive slides, 61 were A total of 2944 images were collected from thick blood smears (15.5 images/patient), and 875 images were collected from thin blood smears (4.6 images/patient). Details about the image collections can be found in Table P. falciparum and negative samples only. This part of the dataset includes 85 patients (40 positive patients and 45 negative patients). Meanwhile, during post-study experiments, PVF-Net was tested on both P. falciparum and P. vivax species from both sites, including 189 patients (99 positive and 90 negative patients). The evaluation results are listed in Table Malaria Screener was tested on SOR P. falciparum malaria through thick smears. It correctly saw whether malaria is present in 63 of 85 patients. This result meets the WHO Level 3 criterion in the parasite detection category [Malaria Screener achieved 74.1% (95% CI 63.5–83.0) accuracy in detecting category . The appP. falciparum and P. vivax was excluded. Thus, the total number of patients is 189 rather than 190 for this experiment). PVF-Net correctly identified whether there was a malaria infection for 157 of 189 patients through thick smear analysis, yielding an accuracy of 83.1% (95% CI 77.0–88.1). This result meets the WHO Level 2 requirement for parasite detection. The sensitivity is 86.9% (95% CI 78.6–92.8), and the specificity is 78.9% (95% CI 69.0–86.8). For P. falciparum only, its accuracy is 82.8% (95% CI 75.8–88.4), sensitivity is 88.5% (95% CI 77.8–95.3), and specificity is 78.9% (95% CI 69.0–86.8). For P. vivax only, its accuracy is 80.5% (95% CI 72.5–86.9), sensitivity is 84.2% (95% CI 68.8–94.0), and specificity is 78.9% (95% CI 69.0–86.8).The images of Sudan data were re-analysed during this post-study experiment. Results are listed below  at < 1000 p/µL (n = 1), 100% (95% CI 79.4-100) at 1000–10,000 p/µL (n = 16), and 100% (95% CI 85.2-100) at > 10,000 p/µL (n = 23). Sensitivity varied for PVF-Net among the three groups. It was 50.0% (95% CI 15.7-84.3) at < 1000 p/µL (n = 8), 77.5% at (95% CI 61.6-89.2) 1000–10,000 p/µL (n = 40), and 100% (95% CI 93.0-100) at > 10,000 p/µL (n = 51) . The sensitivity is 89.6% (95% CI 77.3–96.5), and the specificity is 48.7% (95% CI 31.9–65.6).Nested PCR tests were performed on all 190 patients and compared with results from Malaria Screener and PVF-Net. The 85 non-When compared to PCR on 189 patients, PVF-Net correctly detected whether malaria was present in 153 of 189 patients, reaching 81.0% (95% CI 74.6–86.3) accuracy. It has a relatively high sensitivity of 81.1% (95% CI 72.6–87.9) while achieving a relatively high specificity of 80.8% (95% CI 70.3–88.8). More details are shown in Table Following the semi-automated approach, the app analyses each image automatically, while the user identifies FoVs. Hence, the total time needed to process one smear contains both the app’s runtime and the user’s operating time. It took, on average, only 11.47 and 9.96 s for the app to analyse one thin and thick smear image, respectively, on the used smartphone devices. However, since users also needed time to adjust the microscope between FoVs, they found that the overall processing time per smear for Malaria Screener was only slightly shorter than manual microscopy, although Malaria Screener is much faster in processing each FoV. However, the user’s operating time was not assessed systematically in this study. The above statement is only based on users’ observations.A cross-checking quality control system was implemented during the reference microscopy test. Among 100 patients with positive reads, the first two microscopist readings reached consensus decisions regarding species and parasitaemia for only 27 patients while having discordant diagnoses for 73 patients, according to the Obare method calculator. A Bland–Altman plot Fig.  for asseNested PCR tests confirmed microscopy diagnosis for 178 patients while finding parasites in 11 patients that microscopists identified as negative. Thus, compared to PCR, the WHO Level 1 microscopists achieved a 94.2% accuracy, confirming that they meet the WHO Level 1 requirement for parasite detection.P. falciparum. It achieved 74.1% (95% CI 63.5–83.0) accuracy, 100% (95% CI 91.2–100) sensitivity, and 51.1% (95% CI 35.8–66.3) specificity. Switching the patient-level classification method improved the results to 91.8% (95% CI 83.8–96.6) accuracy, 92.5% (95% CI 79.6–98.4) sensitivity, and 91.1% (95% CI 78.8–97.5) specificity. PVF-Net screened 189 patients from both sites for P. falciparum and P. vivax. It achieved 83.1% (95% CI 77.0–88.1) accuracy, 86.9% (95% CI 78.6–92.8) sensitivity, and 78.9% (95% CI 69.0–86.8) specificity. PVF-Net demonstrated a more robust performance than Malaria Screener, especially in specificity and its ability to detect both P. falciparum and P. vivax.Malaria Screener screened 85 patients from the SOR site for Overall, although the results from the two systems are still inferior to the top-tier expert microscopists and the systems were not ready yet for species identification and parasite counting, their performance showed that automated systems have the potential to be used in real-world settings. In addition, the results showed that the developed machine learning algorithms are generalizable in that this field study was conducted in a different malaria-endemic region (Sudan) and by another group of malaria experts, compared to the region in which the original training data was acquired and where the system was trained .This study reported patient-level results, an essential feature of any computer-aided system for malaria diagnosis to be meaningful in the field. However, such field studies have been lacking in smartphone-assisted malaria microscopy. Most studies were only focusing on object-level or image-level evaluations so far. Even outside the scope of smartphone-based systems, only one such patient-level study  was founThere are some limitations to this study. The test data was not collected so that it could be easily organized to evaluate the software’s performance in diagnosing patients with different levels of parasitaemia. Although such an evaluation was included in this paper, the small number of patients at low parasitaemia levels is less than ideal. Besides, the study was not designed to evaluate the software’s performance for different slide qualities.Malaria Screener demonstrated high sensitivity and low specificity performance during the field evaluation. This is primarily due to many parasite-like artifacts in the images. Unlike the training data, the field data contains more parasite-like staining artifacts, which are difficult for the model to distinguish. These artifacts caused false detections that led to negative patients being diagnosed as positive and likely increased the parasite count in positive patients. More details about the system’s performance can be found in the additional files The results of the post-study experiment showed that the method used for computing patient-level results greatly impacts the system’s patient-level performance. Initially, the image score was generated by calculating the mean of the object scores, and then, the patient score was generated by calculating the mean of the image scores. This method ignored the number of parasite candidates detected for a patient. Using a threshold for the maximum number of parasite candidates allowed in a negative patient avoided this issue and led to a much higher performance. If the number of parasite candidates for a patient exceeded the threshold, the patient was considered positive.PCR was used as a reference test to see what the performance would be when evaluated with a more sensitive method. In addition, sponsors were interested in evaluating the performance of the microscopists compared to PCR. The results showed that the performance of Malaria Screener and PVF-Net dropped when compared to PCR, especially in sensitivity, as expected. However, both systems still achieved the same WHO competency levels. The microscopists achieved an accuracy of 94%, confirming their WHO Level 1 qualification.i.Easy to deploy. The app itself can be easily downloaded from Google Play Store. A microscope adapter can be purchased online via a variety of options. Lastly, the app can run on standard Android devices.ii.Easy to use. A user manual was provided along with the software. Then, field experts learned how to use it after one short online training session.iii.Affordable. The testing device, a Samsung Galaxy A10, cost around $150 at retail price. An adapter can cost from $10 to $100.iv.The experts found that the app used in a semi-automated fashion is not necessarily time-saving, especially when WBC concentration is low and more images must be captured to adhere to the protocol.For automated malaria diagnostic applications, practicality plays an integral part in whether field practitioners will accept the technology. For this study, the following points are worth highlighting in this respect:P. falciparum. This was improved to Level 1 in a post-study experiment. Also, in a post-study experiment, a deep learning network (PVF-Net) achieved Level 2 competence in the category of parasite detection for both P. falciparum and P. vivax. To the best of our knowledge, this is the first patient-level evaluation study of a smartphone-based malaria diagnostic application. Therefore, this study can serve as a reference for evaluating similar systems in the future. The application shows promise for malaria screening in resource-limited areas. With continued improvements, especially for species identification and parasitaemia quantitation, Malaria Screener has the potential to facilitate malaria screening in the field.This paper reported evaluation results for Malaria Screener in a field study conducted by clinical experts in Sudan. According to WHO standards, Malaria Screener reached the Level 3 competence requirement in the category of parasite detection, although only for Additional file 1: Malaria_Screener_User_Manual.pdf: User manual for Malaria Screener.Additional file 2: Overlay_images.zip: Example images showing overlays of blood smear images with detected parasite candidates.Additional file 3: Patient_level_results_SOR.xlsx: A file with microscopy results and PCR results for each patient.Additional file 4: Object_scores_SOR.txt: A txt file containing all object scores with corresponding identifiers.Additional file 5: Object_score_histograms.zip: Object score histograms for each patient.


Plasmodium spp. infections cluster spatially so that interventions need to be spatially informed, e.g. spatially targeted reactive case detection strategies. Here, the spatial signature method is introduced as a tool to quantify the distance around an index infection within which other infections significantly cluster.Over the last decades, enormous successes have been achieved in reducing malaria burden globally. In Latin America, South East Asia, and the Western Pacific, many countries now pursue the goal of malaria elimination by 2030. It is widely acknowledged that Plasmodium infection by PCR. Cohort studies from Brazil and Thailand with monthly sampling over a year from 2013 until 2014 were also included. The prevalence of PCR-confirmed infections was calculated at increasing distance around index infections . Statistical significance was defined as prevalence outside of a 95%-quantile interval of a bootstrap null distribution after random re-allocation of locations of infections.Data were considered from cross-sectional surveys from Brazil, Thailand, Cambodia, and Solomon Islands, conducted between 2012 and 2018. Household locations were recorded by GPS and finger-prick blood samples from participants were tested for Plasmodium vivax and Plasmodium falciparum infections was elevated in close proximity around index infections and decreased with distance in most study sites, e.g. from 21.3% at 0 km to the global study prevalence of 6.4% for P. vivax in the Cambodian survey. In the cohort studies, the clustering decreased with longer time windows. The distance from index infections to a 50% reduction of prevalence ranged from 25 m to 3175 m, tending to shorter distances at lower global study prevalence.Prevalence of P. vivax and P. falciparum infections demonstrate spatial clustering across a diverse set of study sites, quantifying the distance within which the clustering occurs. The method offers a novel tool in malaria epidemiology, potentially informing reactive intervention strategies regarding radius choices of operations around detected infections and thus strengthening malaria elimination endeavours.The spatial signatures of The online version contains supplementary material available at 10.1186/s12936-023-04515-4. Plasmodium falciparum transmission. However, Plasmodium vivax causes relapses [P. vivax malaria has thus been less pronounced so that P. vivax has predominance over P. falciparum in many areas [Globally, enormous successes have been achieved over the last decades in reducing the malaria burden . The mairelapses  and to arelapses , 4. The ny areas .In Latin America, South East Asia, and the Western Pacific, the overall reduction of malaria burden in many countries has paved the way for the ambitious goal of malaria elimination by 2030 , 5. The Plasmodium spp. through an Anopheles mosquito bite depends on a complex interplay of factors of the pathogen, host, vector, and environment. Each of these factors, such as proximity to the vector’s breeding grounds, bed net usage, Anopheles species composition, and host immunity, can vary geographically [Infection with human phically , 12. Geophically –16 to wiphically , 11. At phically .Reducing malaria transmission country-wide usually leads to a geographical fragmentation of areas where malaria risk remains high, further increasing spatial heterogeneity . Tailorik-nearest neighbour method for binary testing whether spatiotemporal clustering of cases occurred [P. falciparum [P. vivax [Plasmodium malariae [To date, most efforts to characterise the spatial clustering of malaria have relied on comparing infection prevalence at community or household scales , 13. In occurred . The intoccurred , 21 to moccurred –26, Afrioccurred –39, and occurred –44, for lciparum , 40, 41,P. vivax , 40, 41,P. vivax and P. falciparum infections were revealed by assessing the pooled prevalence of infections around confirmed index infections in widening spatial and temporal windows. Spatial and temporal clustering of infections was demonstrated in both cross-sectional surveys and cohort studies from a diverse spectrum of study sites in Brazil [In this study, a global clustering statistic is introduced to malaria epidemiology, borrowing strongly from earlier development in the epidemiology of dengue and other pathogens , 46. Then Brazil , Thailann Brazil , 8, Cambn Brazil , and Soln Brazil .P. vivax and P. falciparum infections by real-time PCR and GPS locations of households were collected.Data from 5 cross-sectional surveys from 4 countries, i.e. Brazil, Thailand, Cambodia, and Solomon Islands were used. The study design and basic descriptions are summarized in Table From Kanchanaburi and Ratchaburi provinces in western Thailand, data from a cross-sectional survey in September–October 2012 were considered . In thisData were added from two cross-sectional surveys in a peri-urban part of Manaus, Amazonas State, Brazil from November 2012 until January 2013 (rainy season) and from August until September 2013 (dry season), respectively . After aFrom Solomon Islands, data were used from a cross-sectional survey in several villages across Ngella, Central Islands province from May until June 2012 . From a P. vivax and P. falciparum real-time PCR testing and collection of household locations by GPS at baseline. One study was conducted in two villages in Kanchanaburi and Ratchaburi provinces, Thailand, respectively, in May 2013 until June 2014, enrolling 999 participants . The distance was approximated as the mean of the pair of 50 m-increments of distance The data were described, analysed, and visualised in R 4.1.2 . The mapP. vivax and P. falciparum infections were found in all villages across the study region  are easily accessible for this approach. However, if sampled villages are far apart, it can complicate the interpretation (e.g. the survey in Solomon Islands). This limitation was controlled for by ensuring that no gaps occur in the pairwise distance distributions. However, the It is generally acknowledged that malaria elimination efforts need to be spatially tailored towards areas where (the highest) transmission risk persists , 19. TheReactive interventions are part of the recommended toolbox for malaria elimination . Unless P. vivax and P. falciparum, a trend towards stronger spatial clustering was observed at lower global study prevalence. This suggests that reactive infection detection strategies could become increasingly effective in low transmission settings approaching malaria elimination.The distance to 50% reduction in prevalence was considered a suitable measure for comparing the signatures across study sites. For both P. vivax and P. falciparum infections was demonstrated across a diverse set of study sites and transmission intensities. Introducing a novel method in spatiotemporal malaria epidemiology, the distance within which clustering occurs around index infections was quantified. These distances are often short, e.g. below 200 m, tending to lower values at lower global study prevalence. The spatial signature of Plasmodium spp. infections offers a new tool to extract insights on Plasmodium spp. epidemiology from observational epidemiological field studies. It also provides a method to inform reactive infection detection strategies regarding effective and feasible radius choices of interventions around detected infections.Spatial clustering of Additional file 1: Figures S1–S5. Description of data: Distributions of distance between pairs of study participants or between pairs of cases and all other study participants across the studies; maps of households and P. falciparum infections in the cross-sectional survey in Thailand; maps of households and P. vivax and P. falciparum infections in the second cross-sectional survey in Brazil; maps of households and P. vivax and P. falciparum infections in the villages of the cross-sectional survey in Solomon Islands; spatial signature of prevalence of P. vivax or P. falciparum infections in the cross-sectional survey in Solomon Islands across the full distance range. Figure S6. The spatial signature of prevalence of co-infections of P. vivax an P. falciparum infections in the cross-sectional survey in Cambodia across the full distance range (left) and within 1 km (right). Ribbon: 95%-quantile interval of null distribution. Horizontal line: Global survey prevalence.


The meta-analysis results demonstrated increased IL-10 levels in patients with severe malaria compared with those with non-severe malaria. However, with the heterogeneity of the meta-analysis results, further studies are required to confirm the changes in the IL-10 levels according to the severity of malaria and to investigate whether a combination of other severity parameters with IL-10 levels could be an alternative marker for severe malaria.The roles of anti-inflammatory cytokines in the pathogenesis of severe malaria have been widely studied, and the role of IL-10 in the pathogenesis of severe malaria remains unclear. Therefore, we performed a systematic review and meta-analysis to determine the difference in IL-10 levels between patients with severe malaria and those with non-severe malaria. The search for relevant studies was performed using PubMed, Scopus, and Embase from 1 February 2022 to 12 February 2022. The quality of the included studies was assessed according to the guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology. The random-effects model was used to estimate the pooled effect. In all, 1215 studies were identified, and 19 were included in the quantitative syntheses. The results showed that patients with severe malaria had a higher IL-10 level than those with non-severe malaria ( Plasmodium falciparum is the primary cause of malaria in Africa, Plasmodium vivax is the primary cause of malaria outside of Africa [Plasmodium species, including P. malariae and P. ovale, but these cases are rare [P. falciparum and other less common Plasmodium species could lead to severe outcomes in patients [Malaria is the major cause of death in children younger than 5 years of age in Africa, with an estimated 241 million malaria cases and 627,000 deaths in 2020 worldwide [are rare . NeverthBoth pro- and anti-inflammatory cytokines play a vital role in the human immune response to malaria parasites . The humThe roles of pro-inflammatory and anti-inflammatory cytokines in the pathogenesis of severe malaria have been widely studied, but the role of IL-10 in the pathogenesis of severe malaria remains unclear. In addition, most studies have provided less information from a relatively small number of participants, which precludes the collection of comprehensive data on IL-10 in the pathogenesis of the severity of malaria. Thus, we performed a systematic review and meta-analysis of the available studies that compared the IL-10 levels in patients with severe malaria and in those with non-severe malaria. These studies included a larger number of participants to provide evidence-based information as to whether the IL-10 levels could be a cytokine signature used to differentiate patients with severe malaria from those with non-severe malaria.This systematic review was performed according to the Cochrane Handbook for the Systematic Reviews of Interventions . This reP. falciparum malaria is defined as the presence of the P. falciparum asexual parasitemia with one or more complications, including impaired consciousness, prostration, multiple convulsions, acidosis, hypoglycemia, severe malarial anemia, renal impairment, jaundice, pulmonary oedema, significant bleeding, shock, or hyperparasitemia. Severe P. vivax malaria is defined as for P. falciparum malaria but with no parasite density thresholds [P. knowlesi malaria is defined as for P. falciparum malaria but P. knowlesi hyperparasitemia is defined as a parasite density of more than 100,000/µL; jaundice with a parasite density of more than 20,000/µL [Plasmodium asexual parasitemia without one or more of the complications listed by the WHOs criteria for severe malaria [Severe resholds . Severe 0,000/µL . Non-sev malaria .The inclusion criteria for the review were (1) studies that reported the IL-10 levels in patients with severe malaria and those with non-severe malaria, and (2) study designs that were cross-sectional, observational, cohort, or case–control studies. The exclusion criteria were studies in which (1) the data on the IL-10 levels could not be extracted, (2) no data of clinical presentation  were available, (3) IL-10 was reported in pregnant women/cord blood, (4) the full text was unavailable, (5) experiments were in vitro, and (6) the IL-10 levels were measured after the treatment or intervention.The searches were conducted using PubMed, Scopus, and Embase from 1 February 2022 to 12 February 2022. The reference lists and other sources were searched to ensure that relevant studies were not missed.The following combinations of search terms were used to identify potentially relevant studies: (“Interleukin 10” or IL10 or IL-10 OR “CSIF-10” OR “Cytokine Synthesis Inhibitory Factor”) AND  AND (severe OR complicated OR complication).” The search terms and their synonyms were identified by checking the Medical Subject Heading terms produced by the National Library of Medicine .Two authors independently selected the studies. Duplicates were first deleted from the databases. Next, the titles and abstracts of the studies were screened to find useful studies, and those that lacked pertinent information were removed. The full texts of the remaining studies were then checked, and those that did not match the inclusion criteria were eliminated with justification. Any disagreements between the authors throughout the research selection process were resolved by a consultation with the third author.The data of the listed studies were gathered using a standardized Excel spreadsheet . Two authors were responsible for gathering the data independently, and another author verified the accuracy of the data.Plasmodium species, mean age and age group of the patients, percentage of male patients, IL-10 levels (mean ± SD or median and range in pg/mL), parasite density in severe and non-severe malaria, methods for malaria parasite detection, and methods for the IL-10’s quantification. For studies that reported the median and range of the IL-10 levels, the mean and SD were estimated from the median and the range, as described previously [The following data were extracted: the first author’s name, year of publication, study design, study location and year of conduct, number of patients enrolled in the study, eviously .The quality of the included studies was determined using the criteria of the Strengthening the Reporting of Observational Studies in Epidemiology . The quaThe effect measures were the pooled mean difference in the IL-10 levels between patients with severe malaria and those with non-severe malaria.I2 statistic were used to determine the heterogeneity of the included studies. If heterogeneity existed (Q test with a p of < 0.1 or I2 > 25%), meta-regression and subgroup analyses were conducted to ascertain the source(s) of the heterogeneity. The results of the individual studies and syntheses were visually displayed as a forest plot. Data synthesis was performed using Stata version 17.0 .The pooled standardized mean difference  in the IL-10 levels between patients with severe and non-severe malaria was estimated using the DerSimonian–Laird random-effects model . The chiThe publication bias among the included studies was assessed by visualizing the funnel plot asymmetry, analyzing the small-study effect and Egger’s test, and interpreting the Contour-enhanced funnel plot.A sensitivity analysis was performed to assess the robustness of the synthesized results. A sensitivity analysis using the leave-one-out method was performed to determine the influence of each study on the pooled mean difference in the IL-10 levels.In all, 1215 studies were identified through database searching . Among those studies, 623 articles were duplicates and were excluded. The titles and abstracts were screened in the remaining 592 studies, after which 541 non-relevant studies were excluded. The remaining 51 articles were assessed for their eligibility by reading the full text, after which 35 articles were excluded for the following reasons: 12 studies reported IL-10 levels in severe and non-severe malaria as qualitative data, 8 studies reported IL-10 data that could not be extracted, 3 studies reported IL-10 levels in severe malaria only, 3 studies were in vitro, 3 studies reported IL-10 levels in uncomplicated malaria, the full text was unavailable in 2 studies, 1 study reported IL-10 levels after the treatment, 1 study reported IL-10 levels in tissue sections, 1 study included the same groups of participants, and 1 study reported IL-6 levels in thrombocytopenia cases. Finally, 16 studies ,34,35 meP. falciparum, four (21.1%) enrolled patients with P. vivax, and two (10.5%) enrolled patients with both P. falciparum and P. knowlesi [The 19 included studies were published between 1994 and 2021. Eleven studies (57.9%) were prospective studies, six (31.6%) were retrospective studies, and two (10.5%) were cross-sectional studies. Of the included studies, 10 (52.6%) were conducted in Africa, 5 (26.3%) were conducted in Asia, 3 (15.8%) were conducted in the United States, and 1 (5.26%) was conducted in Europe. Thirteen studies (68.4%) enrolled patients infected with knowlesi . Ten stuTwelve studies (63.2%) used only microscopy for the detection of malaria parasites, four (21.1%) used both microscopy and PCR, two (10.5%) used both microscopy and a rapid diagnostic test (RDT), and one study (5.26%) used three methods (microscopy/RDT/PCR) for the detection of malaria parasites. Twelve studies (63.2%) used an ELISA to quantify the IL-10 levels, whereas seven studies (36.8%) used a bead-based assay . The detEighteen studies (94.7%) were high-quality studies ,36,37,38P. falciparum and P. knowlesi were analyzed separately by classification into four sub-studies. Overall, the meta-analysis results showed that patients with severe malaria had a higher SMD of the IL-10 levels than those with non-severe malaria .Since a high level of heterogeneity was found in the effect estimates among the included studies, meta-regression and subgroup analyses were performed to identify the source(s) of heterogeneity. The meta-regression analysis using the study designs, geographic location (continents), p = 0.31, pooled SMD: 0.19, 95% CI: −0.1800.58, I2: 0%, 2 studies), prospective studies , and retrospective studies  and America . Moreover, the meta-analysis results showed that patients with severe malaria had a higher SMD of the IL-10 level than those with non-severe malaria in the studies performed in Asia . The subgroup analysis also showed a higher SMD of the IL-10 level in patients with severe P. knowlesi malaria than in those with non-severe P. knowlesi malaria . In addition, a lower SMD of the IL-10 level was observed in patients with severe P. vivax malaria than in those with non-severe P. vivax malaria , children , and participants of all age groups . No difference was found in the SMD of the IL-10 level between patients with severe malaria and those with non-severe malaria among studies that used bead-based assays for the IL-10’s quantification . The results of the Contour-enhanced funnel plot showed the distribution of the SMDs of the IL-10 in significant and non-significant areas, which indicates that the asymmetry of the funnel plot was likely caused by a publication bias, the heterogeneity of the SMD from the included studies, or other causes  . The resr causes . After tThe present meta-analysis results demonstrated that patients with severe malaria had a higher mean IL-10 level than those with non-severe malaria. This suggests the possibility of using IL-10 as a severity marker for malaria. The high levels of IL-10 in patients with severe malaria indicate the imbalance of this cytokine in the pathogenesis of severe malaria ,39. T ceA previous meta-analysis of 13 studies showed that patients with severe malaria had higher IL-6 levels than those with uncomplicated malaria . NeverthP. knowlesi and P. vivax, which suggests that these anti-inflammatory cytokines play important roles in the schizont rupture and reinvasion processes [P. falciparum [P. knowlesi, and P. vivax [IL-10 can regulate the neutrophil function by the upregulation of the IL-1Ra and can limit the disease severity in patients with uncomplicated malaria . In addirocesses . Furtherrocesses ,45. Neverocesses ,47. A rerocesses . Gal-9 crocesses . Previoulciparum , P. knowP. vivax . NeverthP. vivax . This coP. vivax .P. knowlesi malaria [P. knowlesi malaria was previously suggested to be caused by higher parasitemia and greater exposure to mosquito vectors [In the subgroup analysis disaggregated by age groups, no significant difference in the IL-10 levels was observed between patients with severe malaria and those with non-severe malaria among studies that enrolled adults, children, or both age groups. These results indicated that age was not the source of heterogeneity of the effect estimate among the included studies. The effect of age on the cytokine level has been reported in a previous study. IL-10 and other cytokines, such as IFN, IL-4, IL-6, and IL-12, were reported to increase with age, whereas IL-5 and TNF-α were shown to decrease with age . These r malaria . The eff vectors .Plasmodium spp. demonstrated that the IL-10 levels were higher in patients with severe P. falciparum and P. knowlesi infection. However, we found a paradoxical association between high IL-10 levels and a severe P. vivax infection. Compared to patients with non-severe P. vivax malaria, those with severe P. vivax malaria had lower IL-10 levels. This finding indicates that IL-10 may play a different regulatory role in severe P. vivax malaria compared to P. falciparum and P. knowlesi malaria. Therefore, the distinct regulatory role of the IL-10 levels in malaria may be linked with the age of the participants, areas with different transmission intensities, and Plasmodium spp.The meta-regression analysis showed that the parasitemia levels did not influence the pooled effect estimate, indicating that there was no relationship between the parasitemia and IL-10 levels among the studies included in the meta-analysis. The subgroup analysis of the study design showed no difference in the IL-10 levels between patients with severe and non-severe malaria in all types of study designs, indicating that the study design did influence the pooled effect estimate, and the study design was not the source of the heterogeneity of the meta-analysis outcome. The subgroup analysis of the geographic location also showed no difference in the IL-10 levels between patients with severe and non-severe malaria in the studies conducted in Africa, America, and Europe, but showed higher IL-10 levels in severe malaria than in uncomplicated malaria in the studies conducted in Asia. The difference in the IL-10 levels between patients with severe and non-severe malaria in the studies from different geographical areas might be attributed to an age group variance. It is well-explained that age was related to the acquisition of immunity against malaria. A review suggested that individuals, particularly children, would develop efficient immunity if they lived in areas where the transmission of malaria was reduced; meanwhile, children would develop less immunity if they lived in areas where the transmission of malaria was increased . The subIn the subgroup analysis based on the method for the IL-10′s quantification, increased IL-10 levels were observed in patients with severe malaria than in those with uncomplicated malaria among studies that used an ELISA for the IL-10′s quantification; however, no difference in the IL-10 levels between patients with severe malaria and those with uncomplicated malaria was observed among studies that used a bead-based assay for the IL-10′s quantification. These subgroup analysis results did not confirm the efficacy of the two methods for the detection and quantification of IL-10. The use of ELISA for detection is suggested to be robust, easy to use, and suited for the measurement of a single cytokine, whereas bead-based assays are multiplex immunoassays for the detection of several cytokines in a single run. Bead-based assays are gaining popularity for the measurement of groups of cytokines and chemokines .P. knowlesi and P. vivax malaria; thus, the pooled analysis of evidence was limited. Third, the heterogeneity of the pooled SMD among the included studies could not be sufficiently explored due to the limitation of information about the included studies. Fourth, the difference in the IL-10 levels between patients with different severe complications of malaria could not be analyzed due to the incomplete and limited information from the included studies. Fifth, the meta-analysis results may have been affected by a publication bias. Although the overall meta-analysis results indicated that IL-10 can be a potential marker for severe malaria, the sensitivity analysis demonstrated outliers that affect the robustness of the results of the meta-analysis. More information from additional studies is required to demonstrate the performance of IL-10 as a marker for severe P. falciparum, P. knowlesi, and P. vivax malaria. In addition, if another novel or well-studied severity parameter is considered along with the IL-10 levels, it could have more impact on the robustness of the inclusion criteria.This systematic review has limitations in evidence and in the review processes. First, some full texts of the relevant studies that reported IL-10 in patients with severe malaria may have been missed due to the limitation of access to some specific databases. Second, a limited number of studies were included in the meta-analysis, particularly studies that investigated the IL-10 levels in patients with The meta-analysis results demonstrated that patients with severe malaria have increased IL-10 levels compared to those with non-severe malaria. However, with the heterogeneity of the meta-analysis results, further studies are required to confirm the changes in the IL-10 levels according to the severity of malaria and to investigate whether a combination of other severity parameters with IL-10 levels could be an alternative marker for severe malaria.


Interleukin 6 (IL6) levels and SARS-CoV-2 viremia have been correlated with COVID-19 severity. The association over time between them has not been assessed in a prospective cohort. Our aim was to evaluate the relationship between SARS-CoV-2 viremia and time evolution of IL6 levels in a COVID-19 prospective cohort.twoway of Stata.Secondary analysis from a prospective cohort including COVID-19 hospitalized patients from Hospital Universitario La Princesa between November 2020 and January 2021. Serial plasma samples were collected from admission until discharge. Viral load was quantified by Real-Time Polymerase Chain Reaction and IL6 levels with an enzyme immunoassay. To represent the evolution over time of both variables we used the graphic command A total of 57 patients were recruited, with median age of 63 years (IQR [53–81]), 61.4% male and 68.4% Caucasian. The peak of viremia appeared shortly after symptom onset in patients with persistent viremia (more than 1 sample with > 1.3 log10 copies/ml) and also in those with at least one IL6 > 30 pg/ml, followed by a progressive increase in IL6 around 10 days later. Persistent viremia in the first week of hospitalization was associated with higher levels of IL6. Both IL6 and SARS-CoV-2 viral load were higher in males, with a quicker increase with age.In those patients with worse outcomes, an early peak of SARS-CoV-2 viral load precedes an increase in IL6 levels. Monitoring SARS-CoV-2 viral load during the first week after symptom onset may be helpful to predict disease severity in COVID-19 patients. One of the most feared complications of the disease caused by the coronavirus SARS-CoV-2 COVID-19), is the development of an Acute Respiratory Distress Syndrome (ARDS), which can affect 15.6–31% of patients  has been described as one of the most useful biomarkers . In a prIn a previous study of our group, we found that viremia was associated with Intensive Care Unit (ICU) admission and in-hospital death, and it was a better biomarker than IL6 . In thisConsidering our previous results, the aim of this study was to evaluate the relationship between the presence of SARS-CoV-2 viremia and the time evolution and IL6 levels in a prospective cohort of COVID-19 hospitalized patients.This work is a secondary analysis of samples from a prospective observational cohort assembled to validate the predictive value of SARS-CoV-2 viremia (ongoing manuscript). The study included patients hospitalized for COVID-19 in Hospital Universitario La Princesa (HUP) between November 1st 2020 and January 15th 2021.The inclusion criteria were: (a) positive Real-Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) for SARS-CoV-2 in nasopharyngeal and throat swabs at most 48 h prior to hospitalization; (b) acceptance to participate in the study and oral or written consent; (c) age older than 18 years. The exclusion criteria were: (a) patients without an initial viremia determination in the first 24–36 h after admission; (b) patients unlikely to be followed-up because they were candidates to be transferred to other health facilities.Clinical, laboratory and therapeutic data were collected from electronic clinical records and included in an anonymized database. Baseline clinical and laboratory data are to those obtained at admission day.The need for hospitalization was decided by the physicians at the emergency room based on clinical criteria, without the intervention of the research team. Patient’s treatment and management was decided by each attending physician based on the hospital protocols and their clinical judgment. Attending physicians were blind to the viremia results.The sample size was estimated in 49 patients to validate the primary objective of the study “SARS-CoV-2 viremia as a biomarker of disease severity” (ongoing manuscript) based on the results of our previous retrospective studies , 15; nev°C and stored in the Microbiology Department facilities.Serial plasma and serum samples were collected from admission until discharge. In the first week, samples were collected every 48 h, with the first sample in the first 24–36 h. Thereafter, samples were collected twice a week. All samples were frozen at –80® Nucleic Acid Extraction System . Detection of viremia was performed with rRT-PCR using TaqPath™ COVID-19 CE81 IVD RT-PCR Kit , according to the manufacturer’s instructions, by a QuantStudio™ 5 Real Time PCR System . Amplification curves were analyzed with QuantStudio™ Design and Analysis software version 2.4.3  and interpreted by a clinical microbiologist. To increase sensitivity, two wells were used for each sample. Two positive controls  and two negative controls were added in each run in duplicate.Firstly, a nucleic acid extraction of samples was performed by the automatic eMAGA standard curve was established using a positive control with a known concentration (TaqPath Positive Control from TaqPath™ COVID-19 Control Kit) of 10,000 copies/μl of the SARS-CoV-2 genomic regions targeted by the TFS assay. Ten-fold serial dilutions of the positive control were made up to 1 copy. Nine wells of each of the concentrations and of the negative control were added to the run. The rRT-PCR was performed by QuantStudio™ 5 Real Time PCR System and a standard curve was obtained plotting DNA concentration against cycle threshold (Ct) values. The amplification curves were analyzed with QuantStudio™ Design and Analysis software version 2.4.3 . The results of the nine wells with 1 copy were omitted because they were widely dispersed.Viral load was calculated from Ct values using the standard curve as reference and was expressed as copies/ml and the logarithm with base 10 (log10). Due to the variability and lack of accuracy obtained with the lower levels of SARS-CoV-2 viremia, only viremias > 1.3 log10 (20 copies/ml) were considered quantifiable.Serum samples collected in the same extraction as the plasma used for SARS-CoV-2 viremia determinations were used to assess IL6 levels. IL6 levels were retrospectively quantified in triplicate with the Human IL6 Duoset enzyme-immunoassay from R&D Systems Europe Ltd. , following the manufacturer’s instructions.For analysis using viremia as a quantitative variable, all values were used. However, to define viremia as categorical, positive viremia was considered when values were higher than 1.3 log10  and negative when values were below this threshold. Persistent viremia was defined as more than one positive viremia in the first week of hospitalization.Two different variables were used to evaluate IL6 levels: (a) a quantitative variable defined as IL6 concentration, expressed in pg/ml, (b) a dichotomic variable, which considered levels of IL6 as high when at least one IL6 determination was higher than 30 pg/ml or low if all determinations were below 30 pg/ml. This threshold was based on our previous study, where we showed that IL6 > 30 pg/ml was associated with poor respiratory outcomes . The aveWe used Stata 14.0 for Windows  for all the analysis described below. Quantitative variables were represented as median and Interquartile Range (IQR), and the Mann Whitney or Kruskall Wallis tests were used to assess significant differences, since all quantitative variables followed a non-normal distribution. Qualitative variables were described as counts and proportions and Chi square or Fisher’s exact test was used for comparisons.twoway from Stata with the option fractional polynomial prediction with 95% confidence interval (CI). Since it is well known that blockade of IL6 receptor with tocilizumab can result in an increase of IL6 serum levels , and it was carried out following the ethical principles established in the Declaration of Helsinki. As proposed by AEMPS , only oral consent was required due to the COVID-19 emergency . HoweverThis article was written following the STROBE initiative .A total of 57 patients were recruited, with median age of 63 years (IQR 53–81), 61.4% were male, 68.4% Caucasian, and 75.4% had previous comorbidities. The median time from symptom onset to first sample was 8 days (IQR 4–10). Baseline clinical characteristics according to IL6 levels are shown in Nine patients were treated with tocilizumab, who on average, showed data suggesting a more severe disease, although differences did not reach statistical significance .Most patients who progressed to a severe disease started this evolution 7–14 days after the symptoms onset. In addition, patients with a more benign course were discharged at the end of the first week after admission. For clinical consistency, we decided to analyze only the samples corresponding to the first 2 weeks of hospitalization, a maximum of 5 samples per patient. Thus, IL6 levels were measured in 228 samples, with a median of 3.6 pg/ml (IQR 0–21 pg/ml). Baseline clinical characteristics of patients depending on IL6 status are shown in r = 0.41, p = 0.0014; The average serum levels of IL6 and SARS-CoV-2 viral load were moderately but significantly correlated (p = 0.001), with an odds ratio of 9.1 (95%CI 2.5–32.6)  . In the .5–32.6) , an earlp < 0.001).When the prediction of IL6 and viral load was calculated according to persistent viremia status, remarkable differences were obtained . In the p = 0.005). In the group of patients with high IL6, 84.2% were male compared to 50% in the group with low IL6 (p = 0.02). No differences were found in the average viral load  or in the percentage of patients with persistent viremia  between males and females, respectively. However, predicting curves for IL6 and viral load were substantially different depending on sex . Moreover, no differences were found when age was categorized as < 75 years and > 75 years . In the group with high IL6, 31.6% of patients were older than 75 years, the same percentage as in the low IL6 group (p = 1). Regarding persistent viremia, the proportion of patients older than 75 years was 29.3% in the group with non-persistent viremia and 37.5% in those with persistent viremia (p = 0.55). In the prediction curves according to age and sex, all parameters increased with age except for viral load in males, which peaked between 40 and 50 years , and the percentage of patients with high IL6   . No diffp = 0.005); conversely, female sex  and Latin-American origin  had a protective effect.Finally, a multivariable analysis showed that the presence of high IL6 was associated with persistent viremia (OR 10.0 [95%CI 2.0–49.5]; This study assessed the relationship between IL6 and SARS-CoV-2 viremia. The most relevant finding was the different time course of IL6 and viremia: the peak of viremia appeared shortly after symptom onset in patients with persistent viremia and also in those with at least one measure of IL6 > 30 pg/ml, in which it was followed days later by a progressive increase in IL6. Moreover, the presence of persistent viremia in the first week of hospitalization was associated with higher levels of IL6. Both IL6 and SARS-CoV-2 viral load were higher in males, with a progressive increase with age that occurred earlier in males.Our findings are consistent with the COVID-19 phases first described by Siddiqui and Mehra . These aSince the beginning of the pandemic, CRP, and IL6 levels have been used as prognostic biomarkers in COVID-19; in addition, IL6 activity has been targeted for treatment by the anti-IL-6 receptor antibody but also to guide treatment and predict response to tocilizumab , 20, 21.Nevertheless, these findings might not be extensive to all patients. A more severe course of COVID-19 and higher levels of IL6 have been previously described in older males . In thisRegarding ethnicity, patients with a Latin-American origin had lower levels of IL6 in our cohort. In this sense, other cytokines and chemokines such as MCP-1, IL-10, IL-15, CXCL10, and CCL2 have been associated with SARS-CoV-2 viremia , 18. It This study has several limitations. First of all, the sample size of our cohort was small, although it was sufficient to find different patterns in the kinetics of IL6 and viral load. Secondly, all patients included were hospitalized and their first sample was obtained at a median of 7 days after symptom onset, therefore, data from the first days of the disease were limited. In addition, information about different variants of SARS-CoV-2 in our cohort could not be obtained because viral sequencing was not available in our facilities. However, at the time our study was performed, the most prevalent variant in Madrid was the original strain .In conclusion, in those patients with worse outcomes, an early peak of SARS-CoV-2 viral load precedes around 5–10 days a prominent increase in IL6 levels. This finding was very clear in males older than 40 years. Therefore, monitoring SARS-CoV-2 viral load during the first week after symptom onset may be helpful to stratify the severity of patients and predict those who are at high risk of developing hyperinflammatory syndrome and ARDS.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Committee of Hospital Universitario La Princesa, Madrid. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.ER-V, LC, IG-A, and DR-S designed the study and wrote the first draft of the manuscript. MCL, EÁ, AB, JH, JO, SR-G, MCS, CM, EG, BG, RM, II, AV, AS-A, DA, JÁ, PP, MT, MU, JG-R, RG-V, JA, RC, and CS included patients in the study and collected data. BQ, CA, CM-C, and EF-R extracted and processed samples. ER-V, AT-M, NZ, AM-R, and LF performed laboratory determinations. ER-V, AS-G, and IG-A analyzed the data. All authors reviewed the final draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Plasmodium knowlesi is the predominant species causing human malaria infection, including hospitalisations for severe disease and death, in Malaysian Borneo. By contrast, there have been only a few case reports of knowlesi malaria from Indonesian Borneo. This situation seems paradoxical since both regions share the same natural macaque hosts and Anopheles mosquito vectors, and therefore have a similar epidemiologically estimated risk of infection. To determine whether there is a true cross-border disparity in P. knowlesi prevalence, we conducted a community-based malaria screening study using PCR in Kapuas Hulu District, West Kalimantan. Blood samples were taken between April and September 2019 from 1000 people aged 6 months to 85 years attending health care facilities at 27 study sites within or close to jungle areas. There were 16 Plasmodium positive samples by PCR, five human malarias  and 11 in which no species could be definitively identified. These data suggest that, if present, simian malarias including P. knowlesi are rare in the Kapuas Hulu District of West Kalimantan, Indonesian Borneo compared to geographically adjacent areas of Malaysian Borneo. The reason for this discrepancy, if confirmed in other epidemiologically similar regions of Indonesian Borneo, warrants further studies targeting possible cross-border differences in human activities in forested areas, together with more detailed surveys to complement the limited data relating to monkey hosts and Anopheles mosquito vectors in Indonesian Borneo.The simian parasite  Malaysia is one of the countries to have achieved this goal, with zero reported indigenous non-zoonotic malaria cases since 2018. However, zoonotic malaria due to Plasmodium knowlesi increased from 1600 cases to > 4000 between 2016 and 2018 before declining to 3213 and 2609 cases in 2019 and 2020, respectively1. Malaysia is the nation with the highest reported incidence of human P. knowlesi infections in South-east Asia3 and, in the Malaysian Borneo states of Sabah and Sarawak, the majority of malaria cases and most malaria-associated hospitalisations are caused by P. knowlesi6. The presence of this parasite in jungle areas harbouring its natural macaque monkey hosts and Anopheles mosquito vectors is a clear impediment to achieving malaria eradication once human malarias have been eliminated8.One of the aims of the Global Technical Strategy for malaria 2016–2030 developed by the World Health Organisation (WHO) was for a reduction in malaria case incidence and mortality rate of at least 40% between 2015 and 20209, and 70 years later there were still large estimated numbers of clinical cases each year 11. However, enhanced control measures had reduced the burden of malaria to an estimated 0.3 million clinical cases of P. falciparum and 0.4 million clinical cases of P. vivax by 2021.12 The Indonesian government aims to eliminate malaria in stages by 2030, from the most to the least developed islands13. As in the example of Malaysia, transmission of P. knowlesi is likely to impede eradication efforts in Indonesia. Indeed, three Indonesian provinces  share a border with the Malaysian states of Sabah and Sarawak 14 and mosquito vectors (including Anopheles leucosphyrus)15 of P. knowlesi occur on both sides of the border. The predicted risk of knowlesi malaria is, therefore, similar for both Malaysian and Indonesian Borneo16.Indonesia is another South-east Asian country that has come a long way in controlling malaria. In the late 1940s, malaria was identified as a major public health problem with substantial implications for economic activity20, data relating to the prevalence and clinical sequelae of this infection in humans in Indonesian Borneo are sparse. Only 12 confirmed cases have been reported to date25 and all of these are from South and Central Kalimantan rather than the provinces of West and North Kalimantan bordering Malaysian Borneo. Most data relating to P. knowlesi in Indonesia come from the approximately 400 cases found in Sumatra26 and Aceh27 provinces. The reasons for the disparity in the burden of P. knowlesi between Indonesian and Malaysian Borneo is unclear. Misdiagnosis may be contributory since the microscopic appearances of P. knowlesi can be interpreted as those of Plasmodium malariae or P. falciparum, and the more accurate but expensive nested PCR-based testing is rarely done outside research settings in Indonesia29. There could be greater awareness of knowlesi malaria as a diagnostic possibility in Malaysian Borneo than in Kalimantan given that it was first discovered as a significant public health problem around 20 years ago in the Kapit Division of Sarawak30. However, there may also be differences between Indonesian and Malaysian Borneo in demographic and environmental conditions impacting transmission.Although there is a significant burden of knowlesi malaria in both Sabah and Sarawak, including severe and fatal casesP. knowlesi prevalence, we conducted a community-based screening study using PCR in Kapuas Hulu District, West Kalimantan, the closest Indonesian district to the Kapit Division of Sarawak. We hypothesized that there would be previously unrecognised cases of P. knowlesi and perhaps cases of other simian malarias among people attending health care facilities and living and working in areas of the district in close proximity to forested areas.To investigate the cross-border disparity in A total of 1000 individuals were recruited from 27 sites in Kapuas Hulu District of West Kalimantan and, of these, 431 (43.1%) were from government health clinics (GHCs), 143 (14.3%) from palm oil plantation health clinics, 102 (10.2%) from satellite health clinics, 65 (6.5%) from villages, and 53 (5.3%) from longhouses  and the other two with a positive blood slide . All were adults who presented with fever. Five cases, four of whom presented with fever and three of whom had an RDT which was negative, were positive by PCR and the infecting species was identified . Two of these cases were children and one an infant aged 6 months—all of these cases were febrile when recruited.There were four participants diagnosed with malaria at the recruitment site but with negative PCR  genes and generating 233–234 base pair (bp) and 272–294 bp amplicons with two different pairs of Nest-2 PCR primers for direct sequencing. Analysis of the DNA sequences, after the PCR primer sequences were removed, was based on the Basic Local Alignment Search Tool (BLAST) from the National Center for Biotechnology Information (NCBI) database revealed the presence of Plasmodium species, with high sequence similarity to human and simian Plasmodium species  for seven of the 11 cases .Our failure to identify the species of 44, and can occur even in the context of clinical trials45. False positive RDTs can result from persistence of antigen well after successful treatment46 or rarely in patients with cross-reacting antibodies47, while false negatives are usually due to parasitaemias below the limit of detection of the test48. The overall numbers of such cases in the present study were, therefore, consistent with other studies.There were four cases of malaria (0.4%) diagnosed by microscopy or RDT who were negative by subsequent PCR, and three of the participants (0.3%) were PCR positive with a species identified but who were negative by RDT. False positive and negative microscopy results are well recognised in areas of low and unstable transmission such as Kapuas Hulu District25 which do not include any from the Kapuas Hulu District of West Kalimantan where the survey was conducted. Although we cannot rule out low-level and largely asymptomatic simian malarias including that due to P. knowlesi, evidence from Malaysian Borneo40 and other parts of Indonesia27 suggests that this would be expected to parallel symptomatic cases requiring treatment which are not observed in Kapuas Hulu District. There are likely to be significant differences in sociodemographic, economic and environmental factors that underlie the substantial disparity between the burden of knowlesi malaria either side of the Malaysian-Indonesian border on the island of Borneo. Nevertheless, there is a need to survey other areas along the Kalimantan-Sarawak and Kalimantan-Sabah border to determine whether the low prevalence of malaria, including simian Plasmodium infections, is confirmed.In conclusion, the results of the present survey are consistent with the very limited number of cases of knowlesi malaria reported to date from Indonesian Borneo2 and a population of 253,740 in the latest census by Indonesian Bureau of Statistics49. It was selected as the study site based on its close proximity to the Kapit division in Sarawak, Malaysia30 with which it shares its northern border , five villages and two longhouses  and at seven of the GHCs  gave witnessed informed consent to sampling and had been resident in the area for at least 6 months. In addition to a DBS for PCR, basic demographic details and a history of symptoms were taken, and testing for malaria by microscopy (single read as negative or positive with speciation but without quantification or semi-quantitative ‘plus’ system grading of parasite density) and/or RDT  was performed depending on the capacity of the local facilities. The same requirements and procedures were followed for community screening in which sample collection took place at the same time as outreach services to villages and longhouses by government health clinics. The outreach service was a routine monthly activity by nurses or health workers for villages with limited access to health care facilities. The individual sites and numbers of samples taken are summarised in Table Screening was conducted between April and September 2019. Between April and June, staff at the government health facilities were requested to take a dried blood spot (DBS) on filter paper, in addition to malaria blood film and/or or Rapid Diagnostic Test (RDT), from all patients presenting with fever. Because the numbers of participants recruited in this way was low, and given emerging evidence that asymptomatic cases of knowlesi malaria would be missedMalaria DBS samples were collected from individuals with or without fever who had been resident in the area for a minimum of six months. A total of 1000 blood samples were obtained by finger prick using a spring-loaded lancet which were spotted onto filter papers  for subsequent molecular analysis. Three separate blood spots per filter paper were collected from the same sampling site for each participant. The spotted filter paper was air dried and stored in an individual sealed plastic bag at room temperature until DNA extraction in the laboratory. Participants diagnosed with malaria by microscopy or RDT were treated according to national guidelines.https://www.bio-rad.com) as described previously52. Nested PCR assays based on the Plasmodium SSU rRNA genes were used to screen DNA extracted from DBS53. Screening for presence of malaria parasites using Plasmodium-genus specific primers for the first amplification Nest-1 (rPLU1 and 5) and Nest-2 amplification (rPLU3 and 4) was first performed on all samples to generate an approximately 1.6 kb amplicon and a 240 base pair amplicon, respectively. For Nest-1, each sample was tested using reaction mixture of 50 μL consisting 0.25 μM of each primer (rPLU1 and rPLU5), PCR buffer , 3 mM MgCl2, 200 mM each deoxynucleotide triphosphate, 1.25 U of Taq DNA polymerase  and 15 μL of DNA template. PCR amplification was performed using a ProFlex Thermal Cycler  under the following conditions: 94 °C for 4 min, 35 cycles of 94 °C for 30 s, 55 °C for 1 min and 72 °C for 1 min, followed by 72 °C for 4 min. Two microlitres of the Nest-1 products were used as DNA template in the 20μL Nest-2 Plasmodium genus-specific assays under the following conditions: 94 °C for 4 min, 40 cycles of 94 °C for 30 s, 62 °C for 1 min and 72 °C for 30 s, followed by 72 °C for 4 min. Nest-2 PCR products were analysed by gel electrophoresis and SYBR® Safe  gel staining. For the Plasmodium-positive samples, the Nest-1 amplification products were reused for species-specific Nest-2 amplifications. All genus positive samples were analysed at least twice by two independent laboratory staff. Positive species-specific samples were confirmed by retesting and samples with inconsistent results were re-extracted before being re-analysed.DNA was extracted from DBS by using Instagene® Matrix 55, 65 °C for P. vivax, 62 °C for P. knowlesi and P.coatneyi, 60 °C for P. cynomolgi and P. inui, and 66 °C for P. fieldi. Similarly, the species-specific Nest-2 products were subjected to gel electrophoresis and were stained using SYBR Safe™. Precautions to prevent cross-contamination in nested PCR assays were taken as described previously30.Plasmodium-specific primers rPLU3 and rPLU4 as described above. In addition, we also amplified the partial SSU rRNA gene of approximately 252 bp long by independent hemi-nested PCR assays using primers PlasmoM_N1F and PlasmoM_N1R in the first PCR amplification, followed by primers PlasmoM_N2F and PlasmoM_N1R in the second PCR amplification as described previously56. Both amplified products were sent to Apical Scientific Sdn. Bhd.  for direct DNA sequencing. The DNA sequences generated were confirmed for Plasmodium DNA sequence using BLAST from the NCBI website. DNA sequences were then aligned using the MegAlign software  together with other reference sequences of primate Plasmodium species (see Supplementary Fig. 57 with a bootstrap of 1000 replications.PCR amplification of partial SSU rRNA genes was conducted using the Supplementary Information.