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Common asymptomatic and submicroscopic malaria infections in Western Thailand revealed in longitudinal molecular and serological studies: a challenge to malaria elimination



Despite largely successful control efforts, malaria remains a significant public health problem in Thailand. Based on microscopy, the northwestern province of Tak, once Thailand’s highest burden area, is now considered a low-transmission region. However, microscopy is insensitive to detect low-level parasitaemia, causing gross underestimation of parasite prevalence in areas where most infections are subpatent. The objective of this study was to assess the current epidemiology of malaria prevalence using molecular and serological detection methods, and to profile the antibody responses against Plasmodium as it relates to age, seasonal changes and clinical manifestations during infection. Three comprehensive cross-sectional surveys were performed in a sentinel village and from febrile hospital patients, and whole blood samples were collected from infants to elderly adults. Genomic DNA isolated from cellular fraction was screened by quantitative-PCR for the presence of Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale and Plasmodium knowlesi. Plasma samples were probed on protein microarray to obtain antibody response profiles from the same individuals.


Within the studied community, 90.2 % of Plasmodium infections were submicroscopic and asymptomatic, including a large number of mixed-species infections. Amongst febrile patients, mixed-species infections comprised 68 % of positive cases, all of which went misdiagnosed and undertreated. All samples tested showed serological reactivity to Plasmodium antigens. There were significant differences in the rates of antibody acquisition against P. falciparum and P. vivax, and age-related differences in species-specific immunodominance of response. Antibodies against Plasmodium increased along the ten-month study period. Febrile patients had stronger antibody responses than asymptomatic carriers.


Despite a great decline in malaria prevalence, transmission is still ongoing at levels undetectable by traditional methods. As current surveillance methods focus on case management, malaria transmission in Thailand will not be interrupted if asymptomatic submicroscopic infections are not detected and treated.


In Thailand, control efforts have been highly effective in curbing malaria nationwide [1], but malaria remains a significant public health problem along the hilly and forested areas of the country’s borders with Myanmar and Cambodia [24]. High geographical heterogeneity in malaria endemicity and the presence of multiple Plasmodium species that cause human malaria (Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale and Plasmodium knowlesi) are characteristics of malaria epidemiology in the region.

The Government of Thailand aims to achieve malaria elimination by 2030 [5], thus identification (including parasite speciation) and accurate treatment of asymptomatic and symptomatic infections are critical to accomplish this goal. However, this remains a major challenge because light microscopic analysis of blood smears, the gold standard in malaria diagnosis in Thailand, is insensitive at detecting low-level parasitaemia [6]. It is known that as malaria transmission declines, an increasing proportion of individuals are found to have asymptomatic and submicroscopic malaria infections [7], such as in many countries in Southeast Asia and the Amazon [812]. This is important because asymptomatic and submicroscopic malaria infections are known to contribute to transmission [7, 13, 14].

Our study site in the Myanmar-border province of Tak, once Thailand’s highest malaria burden region, experienced a drastic reduction in transmission recently [15, 16], and based on microscopy-estimated parasite prevalence (<1 %), it is now considered to be a low-transmission area. However, in our previous study in the Thai–Myanmar border area, a significant number of asymptomatic and submicroscopic malaria infections amongst the adult population were detected by quantitative polymerase chain reaction (qPCR) [17]. Moreover, amongst adult malaria patients attending a malaria clinic, a large number of cryptic mixed-species infections went undetected and untreated [17].

In the present study, the molecular screening and antibody profiling by microarray were expanded to include samples from all ages and three seasonal time points to obtain a more accurate assessment of the current epidemiology of falciparum and vivax malaria in Tak. For blood samples collected during community mass blood surveys and passive case detection activities at the hospital and clinic, the presence of all five Plasmodium species found in Thailand was investigated by qPCR. The population’s antibody responses against P. falciparum and P. vivax proteins were profiled to examine the development of antibody acquisition relative to age and season, and the targets of antibody responses in persons with asymptomatic and febrile malaria in an age-related manner.


Study sites and samples

The study was conducted in the village Mae Salid Noi (17° 28′ 4.7202″, 98° 1′ 48.5106″) and the town of Mae Tan (17° 13′ 49.0146″, 98° 13′ 55.6212″) in Tak Province, northwestern Thailand along the Thai–Myanmar border [17]. The sites are in a low and unstable transmission area, with higher transmission in the rainy season from May to October [3]. The four human malaria parasites, as well as the simian malaria species P. knowlesi, are known to infect humans in the region [18, 19], but P. vivax and P. falciparum are vastly predominant [16, 20, 21].

Whole blood samples were collected during three cross-sectional mass blood surveys (MBS) in the study village Mae Salid Noi, in March (n = 485) and August (n = 398) of 2013, and January 2014 (n = 464) from individuals ranging from 0.7 to 92 years of age (median, 14; mean 22, 95 % CI 21–23). The health history, including absence or presence of malaria symptoms as described below, was recorded weekly for each study participant from October 2012 to June 2014. The sample size (n = 1347) enabled us to determine parasite prevalence by qPCR with 2.6 % margin of error, using alpha 0.05.

Additionally, 297 whole blood samples were collected from individuals with suspected malaria during passive case detection (PCD) at the Mae Tan malaria clinic and hospital in March (n = 67) and August (n = 53) 2013, and January 2014 (n = 177). Age of PCD participants ranged from 0.8 to 84 years old (median, 23; mean 25, 23–27).

Blood sample preparation for DNA analysis

From each study participant, 300 μL of whole blood was collected from finger prick into a Microvette CB300 capillary blood collector with lithium–heparin (Sarstedt, Newton, NC). Samples were centrifuged to separate cellular and plasma fractions, then immediately frozen at −80 °C for shipment to University of California Irvine for analysis. Upon thawing, plasma was removed and stored at −80 °C until use. Total genomic DNA was isolated from 100 μL of pelleted cellular fraction using DNeasy Blood and Tissue kit in the QIAcube automated system (Qiagen, Valencia, CA), using the Blood and Body Fluid Spin Protocol with Manual Lysis V1. Purified genomic DNA samples were eluted with 200 μL of Buffer AE and kept at −20 °C until use.

Sample analysis by microscopy and quantitative PCR

Field microscopy was performed by local trained staff who provided the first result, and positive cases were treated per national malaria treatment guidelines. Individuals found positive in subsequent expert microscopy and molecular screenings were not treated. Expert microscopic examination was conducted at Mahidol University by an expert microscopist with over three decades of experience. Molecular detection of Plasmodium parasites was performed at University of California Irvine using a two-tier strategy for qPCR consisting of an initial screening of all 1644 samples for the presence of Plasmodium genus-specific products using SYBR Green, followed by TaqMan assays to determine the Plasmodium species in samples positive in the genus-specific assay.

For the Plasmodium-genus screening, primers were designed to hybridize with a region of 18S rRNA gene conserved amongst P. falciparum (Pf), P. vivax (Pv), P. malariae (Pm), P. ovale (Po) and P. knowlesi (Pk). The forward primer sequence was 5′-GTATTCAGATGTCAGAGGTG-3′, and the reverse primer was 5′-CCTACTCTTGTCTTAAACTAGT-3′. Amplification was performed in 20 μL reactions containing 2 μL of genomic DNA, 10 μL FastStart SYBR Green qPCR Master Mix (Roche, Indianapolis, IN), 0.2 μM of each primer and 3 mM MgCl2, in a CFX96 Touch Real-Time PCR Detection System (BIORAD, Hercules, CA). After initial denaturation at 95 °C for 10 min, 45 cycles of 94 °C for 30 s, 60 °C for 30 s, 68 °C for 1 min were followed by a final step of 95 °C for 10 s and a melting curve from 65 to 95 °C with 0.5 °C increments for 5 s. Samples were considered positive if Cq was smaller than 41 and there was a product with melt peak in the temperature ranging between 74 and 75.5 °C. All assays included positive and negative controls. The detection limit of this method was determined to be 0.05 parasites/μL using serially diluted P. falciparum cultures.

Samples positive in the genus screening were examined in uniplex TaqMan assays using Plasmo 1 and Plasmo 2 primers and species-specific probes for P. falciparum, P. vivax, P. malariae and P. ovale as described in Rougemont et al. [22] and for P. knowlesi as described in Divis et al. [23], with the modification that probes were either FAM/ZEN/Iowa Black FQ (for Pf, Pv and Pk reactions) (Integrated DNA Technologies, San Diego, CA) or FAM/MGBNFQ (for Pm and Po reactions) (Applied Biosystems, Foster City, CA). Amplification was performed in 20 μL reactions containing 2 μL of genomic DNA, 10 μL TaqMan Universal Master Mix II, no UNG (ThermoFisher, Grand Island, NY), 0.2 μM of each primer and 80 ηM of probe, in a CFX96 Touch Real-Time PCR Detection System (BIORAD, Hercules, CA). Samples were tested in duplicate and considered positive if it generated a Cq value smaller than 40.

Sample classification

Samples were classified into four major categories, according to collection site and presence or absence of Plasmodium DNA by qPCR. Samples collected at Mae Salid Noi village during community MBSs were classified as (1) community malaria (if sample was qPCR-positive for Plasmodium), or (2) community healthy (if sample was qPCR-negative). Samples collected at the hospital/malaria clinic from suspected malaria patients were classified as (3) febrile malaria (if qPCR-positive), or (4) non-malaria illness (if qPCR-negative). Malaria symptomatology is defined as fever (>37.5 °C), fatigue, myalgia, headache and nausea, occurring alone or in combination.

Plasmodium falciparum and P. vivax protein microarray

The protein microarray used in this study, named Pf/Pv500 (Antigen Discovery Inc., Irvine CA), was described in detail in Baum et al. [17], and on NCBI’s Gene Expression Omnibus under platform accession number GPL21194. Briefly, the array includes 500 P. falciparum and 515 P. vivax polypeptides printed as in vitro transcription translation (IVTT) reactions, that were down-selected from larger microarray studies based on seroreactivity and antigenicity to humans. Gene accession numbers follow annotation published on PlasmoDB [24]. For large proteins printed on the microarray as overlapping polypeptides or individual exons, the exon position relative to the full molecule and the segment of the ORF are indicated where applicable.

Probing of plasma samples on the Pf/Pv500 microarray

From the community MBS, 298 plasma samples were probed: 41 community malaria, and 257 community healthy samples, including the corresponding longitudinal samples of community malaria samples, and samples from individuals age-, gender-, and season-matched to the community malaria samples. Of these, 150 samples collected in March 2013 were randomly selected for the seroconversion study, 15 samples for each of 10 age groups. Additionally, 83 samples collected during PCD at the malaria clinic and hospital were probed, including 32 malaria fever and 51 age- and gender-matched non-malaria illness patients. As unexposed controls, 16 samples from healthy blood donors from the United States, with no travel history to malaria endemic regions, were used for serology comparisons. All microarray probing was performed on the same day, and as previously described in Baum et al. [17].

Data analysis

For analysis of antibody binding to P. falciparum and P. vivax polypeptides on the microarray the following steps were taken: (1) the median background signal of antibody binding to 24 spots of IVTT reaction without DNA template (no template control, NTC) was calculated for each individual sample; (2) the raw values of antibody binding to P. falciparum and P. vivax polypeptides were divided by their corresponding median NTC value, generating fold-over-control (FOC) values; (3) FOC values were log2-transformed for data normalization. Normalized data was used for statistical analyses and for figure representations of the data. (4) To determine which polypeptides were seroreactive to plasma from the Thai sample cohort, Significance Analysis for Microarrays (SAM) [25] was performed comparing the intensity of antibody binding to the proteins on the array between the samples collected in Thailand from individuals over 15 years-old (n = 211) and 16 USA unexposed controls. The test was performed using MeV 4.8.1, with the following parameters: median and 90th percentile of false discovery rate (FDR), 1.2 and 9.3 %, respectively; median and 90th percentile of number of false significant genes, 3.9 and 30.5, respectively. This resulted in 326 polypeptides being considered significantly seroreactive in exposed Thai samples, and all further analyses considered only this set. Individual plasma samples were considered seropositive for a polypeptide if the sample’s signal intensity value was above the upper 99 % confidence interval value of the unexposed control group. Breadth of response was determined by the number of antigens an individual or group of samples were seropositive to, based on the above criteria for seropositivity. For analysis of antibody responses, the non-parametric multiple comparison Steel–Dwass and the Wilcoxon test were used for pairwise comparisons of means, using JMP11.2. Significance tests were 2-sided and set at 0.05 level for type I error. Z-scores were calculated as the number of standard deviations above or below the mean of the unexposed group. Annual rates of seroconversion and seroreversion for the immunogenic polypeptides were calculated using Systat 11 (Systat Software, Chicago, IL) by fitting age-specific seroprevalence data to a reversible catalytic model using the maximum-likelihood method that assumes binomial error distribution: \(P_{t} = \lambda /\lambda + \rho (1 - e( - (\lambda + \rho )*t))\) [26]; where P t is the proportion of seropositive individuals in each age group t, λ is the annual rate of seroconversion and ρ is the annual rate of reversion to seronegativity. The model was fitted to seroprevalence curves to each seroreactive antigens using ten age groups, shown in Additional file 1. Individuals below 2 years of age were excluded to eliminate pre-existing maternal antibodies from analyses. T test for comparison of slopes of linear regression lines was performed as per Zar [27]. A two-proportion Z-test was used to compare the proportion of samples in different age categories between two populations. The geometric mean value and the 95 % confidence interval of data are reported in parenthesis, i.e. (0.73, 0.72–0.74).


Infection rates and comparison between qPCR and microscopy results

From the community of Mae Salid Noi, DNA of Plasmodium parasites was detected by qPCR in 41 (3.0 %) of 1347 blood samples tested. The highest infection rate was observed in March 2013, with 4.3 % positive samples (21/485), and decreased significantly in August to 1.3 % (5/398), and then increased to 3.2 % in January 2014 (15/464). Four individuals had confirmed malaria in repeat samples, 5 (P. falciparum, n = 1) and 10 (P. vivax, n = 3) months apart. Of the 41 community malaria cases, three (two children and one adult) (7.3 %) presented fever at sample collection, while 33 (80.5 %) were asymptomatic and had no malaria symptoms or illness complaints during the 5-month period preceding and following sample collection. The remainder five (12.2 %) developed fever 1–4 months after presenting a positive qPCR for Plasmodium parasites.

Table 1 shows a comparison of results from molecular and microscopy screening for Plasmodium parasites for the samples collected. Amongst samples from the community surveys, P. vivax and P. falciparum were the most commonly detected species, while P. ovale and P. malariae were detected only in mixed-species infections (qPCR results, Table 1). DNA of P. knowlesi was not detected. Only three samples were positive by microscopy, diagnosed as P. vivax, thus 92.7 % of Plasmodium-infected samples had submicroscopic parasitaemia (microscopy results, Table 1).

Table 1 Comparison of screening results for blood samples from community mass blood surveys and passive case detection in the Thai–Myanmar border area

From the blood collected at the hospital/clinic from febrile patients, malaria infections were confirmed in 32 (10.8 %) of 297 samples by qPCR, with the infection rate declining from 19.4 % (13/67) to 11.3 % (6/53) and 7.3 % (13/177) along the March–August–January time line. Most frequently found in these patients were mixed-species infections of P. falciparum and P. vivax, which accounted for 68.7 % of febrile malaria cases (qPCR results, Table 1). All mixed-species infections (22/22) were misdiagnosed by light microscopy, usually by not detecting the secondary species: of P. falciparum diagnoses by microscopy, 50 % contained cryptic P. vivax infections; while 93.3 % of P. vivax diagnoses contained cryptic P. falciparum infections (microscopy results, Table 1). Plasmodium malariae was detected in one patient as single infection, but neither P. ovale nor P. knowlesi were detected by qPCR in these samples.

The age distribution of all samples collected in the community MBS and hospital PCD are shown in Additional file 2. The median age for individuals with Plasmodium infections in the Mae Salid Noi community was 14 years, with a mean of 22.8 (17.1–28.5); while the median age of febrile malaria patients in the hospital/clinic was 26.5, with a mean of 27.6 (22.6–32.6). The proportion of qPCR-positive samples in the age groups of 11–20, 21–30 and 31–40 years was significantly higher in the hospital/clinic samples than in the community samples, resulting in the higher median age for malaria patients (Additional file 3).

Plasma antibody reactivity to P. falciparum and P. vivax proteins on the microarray

A subset of plasma samples from the community mass blood surveys and hospital/clinic PCD were probed on a protein microarray, resulting in 184 P. falciparum and 142 P. vivax proteins being recognized as immunogenic by antibodies. A heatmap shows the antibody reactivities to these proteins by the individual samples, segregated by collection site, malaria infection status and age (Fig. 1); Additional file 4 presents the gene ID, names and description of all 326 immunogenic proteins, along with their seroprevalence rates for the different age groups in the community and PCD samples.

Fig. 1
figure 1

Heatmap of Z-scores of intensity of antibody binding to P. falciparum and P. vivax proteins on the microarray, segregated by decade of age. Samples were collected from the community in Mae Salid Noi (n = 298) and from febrile patients of the hospital/malaria clinic in Mae Tan (n = 83). Healthy community and non-malaria illness samples were qPCR-negative for Plasmodium sp., community malaria and febrile malaria samples were qPCR-positive for Plasmodium sp.. The decade of age of sample donors is shown at the top of heatmap signals, and correspond to years of age as: 1 (up to 10 years old), 2 (11–20), 3 (21–30), 4 (31–40), 5 (41–50) and 6 (51–60+). The youngest age available from community samples was 3 years old, and from PCD, 5 years old. The colored gradient indicates the number of standard deviations above the mean signal intensity of unexposed adult controls from the USA (Z-score). Individual samples appear as columns, seroreactive proteins appear as rows

Many well-known antigens, such as several P. falciparum merozoite surface proteins (MSP), apical membrane antigen 1 (AMA1), liver stage antigen 1 (LSA1), serine repeat antigens (SERA) 3, 4 and 5; erythrocyte membrane protein 1 (PfEMP1); and P. vivax circumsporozoite protein (CSP), early transcribed membrane proteins (ETRAMP), multiple MSP and SERA proteins, amongst many others were recognized, and detailed seroprevalence rates for these antigens are presented in Additional file 4. The top ten most immunogenic proteins of P. falciparum and P. vivax for the entire Thai cohort are presented in Table 2.

Table 2 Top ten most immunogenic proteins of P. falciparum and P. vivax for plasma samples from the Thai–Myanmar border area

Compared to the background reactivity of unexposed controls, Thai samples had significantly higher responses to the proteins on the microarray (Steel–Dwass test, p < 0.0001) (Additional file 5). Amongst the four Thai sera groups based on collection site and malaria status, the intensity and breadth of response was highest in febrile malaria patients, followed by community malaria samples, non-malaria illness patients and healthy community samples (Additional file 5). All plasma samples were reactive to Plasmodium proteins on the array, with the number of antigens recognized by individual samples ranging from 11 to 324, median 124.

Differential antibody acquisition rates for P. falciparum and P. vivax antigens

For the samples from the community MBS (n = 298), children in the youngest age group had the lowest magnitude and breadth of antibody responses, while adults over 50 had the highest; the averaged antibody responses to the 326 Plasmodium proteins increased significantly (Steel–Dwass test, p < 0.0001) in intensity and breadth (with one exception) with the increase of age, as shown in Additional file 6. However, the rate by which antibodies against P. falciparum and P. vivax were acquired differed between the two species, as shown in Fig. 2.

Fig. 2
figure 2

Age-dependent increase in intensity (a) and breadth (b) of antibody binding to Plasmodium antigens by 298 plasma samples collected during community MBS in Mae Salid Noi. The average of responses to 184 P. falciparum antigens are shown in blue, responses to 142 P. vivax antigens are shown in red. Top of bars represent the mean value, error bars represent 95 % confidence interval of the mean. Wilcoxon p values for comparison of responses to the two Plasmodium species are shown as asterisks: *<0.05, **≤0.01, ***<0.001; p values greater than 0.05 are not shown. Linear regression lines show 95 % CI as shaded area outside line, the equation for each regression is shown

Up to 20 years of age, the intensity of response (Fig. 2a) was significantly stronger against P. vivax antigens (Wilcoxon p < 0.0001); but by the age of 21–30 a switch in the dominant target species is observed, with antibodies against P. falciparum exceeding those against P. vivax, a trend that increased with age. As breadth is partially defined by intensity of the response to individual antigens, a similar trend was observed for the number of antigens recognized by the different age groups (Fig. 2b). In the youngest group, the repertoire of antibodies was larger against P. vivax (p = 0.04), but this difference began to disappear in samples from donors in their second decade of life; from age 21 to over 60, samples showed a broadening of their antibody repertoires that recognized significantly more P. falciparum antigens than P. vivax.

Although the higher intercept values in regression equations of P. vivax indicated that in the youngest age groups the antibody response started higher against P. vivax, the linear regression lines in Fig. 2a and b showed significantly steeper slopes for the antibody response to P. falciparum (slope comparison T-test, p < 0.0001), indicating that the rate of accumulation of antibodies against P. falciparum is faster than against P. vivax in this population.

Similarly, calculated annual seroconversion (SCR) and seroreversion (SRR) rates (Additional file 4) indicated significant differences in the development of antibody responses against P. falciparum and P. vivax antigens. SCR values for P. vivax antigens (mean 0.048, CI 0.035–0.062) were significantly higher than for P. falciparum antigens (0.029, 0.026–0.033) (Wilcoxon p = 0.03), indicating that seropositive rates to P. vivax tended to start higher at the youngest age and continued relatively high throughout life. Lower SCR values for P. falciparum antigens indicate a low seroprevalence in the youngest groups, but steady acquisition of seropositive states with aging. Loss of antibodies, as determined by the SRR, was higher for vivax antigens (0.022, 0.009–0.035) than for falciparum (0.002, −0.001–0.005) (p < 0.01), indicating that antibodies generated against P. falciparum were significantly more stable, while antibodies to P. vivax were less stable and were lost at a faster rate than those against P. falciparum.

Temporal changes in antibody responses to Plasmodium in the community

Temporal changes in antibody responses were investigated in children (<15 years old) and adults (>15 years old) from the village using longitudinal samples collected from the same individuals at three time-points, 5 months apart. For confirmed Plasmodium cases, individuals had qPCR-positive samples in March 2013 followed by two qPCR-negative samples, in August 2013 and January 2014; age- and gender-matched controls had three consecutive qPCR-negative samples at these time points.

For each of the 326 antigens examined for changes in antibody binding along the 10 months studied, only the 32 shown in Fig. 3 had significant (p < 0.05) fluctuations at one or more time points. Children showed the most temporal changes in their antibody responses, regardless of infection status, usually as significant boosting of their responses by August 2013 that sometimes dropped or continued to rise at the last time point (Fig. 3a, b). Some notable antigens that were boosted in August in the children’s responses were the P. falciparum gametocyte-specific protein Pf111 (index 432 and 435), SERA 3 (index 450) and MSP7 (index 466). Plasmodium falciparum erythrocyte membrane protein 1 and SERA 4 (index 613 and 982, respectively) showed a significant decay between the time points before and after the rainy season (March 2013 and January 2014). Adults had more stable responses and fewer antigens varied significantly along the seasons (Fig. 3c and d), but their responses usually were boosted to peak in January 2014.

Fig. 3
figure 3

Antibody responses to antigens with significant seasonal changes. The intensity of antibody responses of children and adults with confirmed malaria in March 2013 (cases, panels a, c), and matched uninfected controls (b, d) are shown for antigens with at least one significant change (p < 0.05) in antibody levels between two time points. Antigens are identified in the figures by their microarray index number, and their corresponding gene ID and protein description are found in Additional file 4. The box indicates the first and third quartiles, the line inside the box the median pixel intensity value. The whiskers represent the minimum and maximum value

The overall trend for antibody responses to Plasmodium antigens in these samples was a rise from the March 2013 levels that peaked in August 2013 for controls and January 2014 for cases, in both children and adults (Additional file 7). This is coincident with before, during and after the long rainy season (May to October), which is associated with low, peak and decline of parasite transmission season in the region. Although controls were qPCR-negative at the three sample collection times, the possibility of asymptomatic Plasmodium infections in the intervening months cannot be ruled out, and could result in the boosting of their antibody responses, as seen.

Comparison of antibody responses between asymptomatic and febrile malaria cases

Asymptomatic cases were defined by the lack of malaria symptoms up to 5 months after a Plasmodium infection confirmed by qPCR and consisted of 33 individuals from the community, with a median age of 14 and 60 % of them below 20 years of age. Febrile malaria cases were defined by the presence of fever and other symptoms at the time of confirmed Plasmodium infection, and consisted of 32 hospital/clinic patients, with a median age of 26.5 years.

Mixed Pf + Pv infections were significantly more frequent amongst febrile patients than amongst asymptomatic cases (OR 12.46 (3.96–39.19), p < 0.0001); while single P. vivax infections were significantly more frequent amongst the asymptomatic cases than in febrile malaria cases (OR 7.35 (2.18–24.73), p = 0.001). Single infections with P. falciparum were found in equal proportions between the two groups (OR 1.74 (0.53–5.73), p = 0.36).

The intensity and breadth of their antibody responses are shown in Fig. 4. In general, asymptomatic cases showed antibody responses significantly elevated from the healthy community baseline (Wilcoxon test for intensity, p < 0.0001; breadth, p < 0.05); however, it was still significantly lower than in febrile patients up to age 50 (Fig. 4a). Febrile patients also had significantly broader antibody repertoires than asymptomatic cases up to the age of 30 years, but this difference disappeared in older adults (Fig. 4b). The antibody response was also significantly higher (p < 0.0001 for each comparison) in malaria fever cases when samples were grouped by the infecting Plasmodium species, i.e., in P. falciparum, in P. vivax and in mixed Pf + Pv infections, regardless of age.

Fig. 4
figure 4

Comparisons of intensity (a) and breadth (b) of antibody responses to Plasmodium antigens by plasma samples from asymptomatic (purple) and febrile (green) malaria cases, segregated by age group. Healthy community samples (qPCR-negative for Plasmodium) are shown in grey. The number of samples (n) in each age group appears in parenthesis. Top of bars represent the mean pixel intensity value of antibody binding to 326 antigens, error bars represent 95 % confidence interval of the mean. For box whisker plots, the box indicates the first and third quartiles, the line inside the box the median. The whiskers represent the minimum and maximum value. Wilcoxon test p values comparing the means between the asymptomatic and febrile cases within each age group are shown as asterisks: **<0.01, ***<0.001; p values greater than 0.05 are not shown

In the oldest age group, plasma from febrile malaria patients aged 51 and older showed a drop in intensity of binding significantly below the level of antibody responses by asymptomatic cases. It is only in this group that individual antigens were found to elicit significantly higher antibody responses in asymptomatic carriers than febrile patients. These 21 P. falciparum and 7 P. vivax antigens and their Wilcoxon p values are presented in Additional file 4. Apart from these antigens, no other serological markers could distinguish individuals protected from symptomatic manifestations of malaria.


In a pilot study in the village of Mae Salid Noi and town of Mae Tan, a high level of submicroscopic infections was found amongst asymptomatic and symptomatic adults [17]. Now, from an age-stratified cohort six-times larger, new observations from this low transmission area are added regarding the frequency of asymptomatic and submicroscopic infections, differences in rates of antibody acquisition to P. falciparum and P. vivax antigens, and antibodies profiles associated with seasonal changes and with clinical and subclinical malaria.

While the detection limit of expert microscopy is approximately 100 parasites per μL [6], the qPCR technique used here detected as few as 0.05 parasites per μL of whole blood. In the community mass blood surveys, Plasmodium infection rates estimated by qPCR were nearly 14-times higher than those estimated by expert microscopy, and despite the overall low parasite prevalence in the population (3.0 %), over 90 % of infections were both asymptomatic and submicroscopic at the time of the survey. This is consistent with the mounting evidence from other low transmission areas in the Amazon, Africa and Southeast Asia, where a previously unrecognized reservoir of Plasmodium infection is rising in the form of asymptomatic submicroscopic parasitaemia [812, 2831]. In areas of very low transmission submicroscopic carriers are estimated to be the source of 20–50 % of all human-to-mosquito transmissions [14]. Although the importance of submicroscopic infections for malaria transmission is still unclear [32], the Malaria Eradication Research Agenda suggests that any parasitaemia, no matter how small, may be potentially a source of transmission and thus a threat to malaria elimination efforts [7].

Mixed-species infections were common in both the community survey and passive case surveillance in the hospital and clinic, where 25 % of asymptomatic infections and 68 % of febrile malaria cases were caused by co-infections of primarily P. falciparum and P. vivax. All mixed-species infections detected by qPCR were misdiagnosed as single-species infections by light microscopy, primarily by not detecting cryptic P. falciparum in the blood smears. The inability to detect and treat malaria infections is a serious threat to the health care of individual patients and to malaria control and elimination. Molecular diagnosis is not practical in the field and hospital or clinic settings, therefore training of hospital technicians and improvement of malaria rapid diagnostic tests are urgently needed, particularly in light of the presence of multidrug-resistant parasites in Thailand [33, 34].

Notwithstanding the small number of parasitaemic samples found in the three MBSs of the community by qPCR, serological measures indicated exposure to malaria parasites in all individuals tested. However, the youngest children showed limited responses, reacting to a small number of antigens, a serological indication of the success of malaria control efforts in the area because very low antibody titers are expected to correlate with reduced parasite exposure events. These children are an attractive target group for serosurveillance because detection of antibodies in this group, after drastic reduction or elimination of local transmission, indicates resurgence of malaria in a population [35, 36]. Serology provides a view of present and past parasite exposures, and seroprevalence rates can be used to define malaria endemicity in an area [37, 38].

It could be expected that the low levels of antibodies against Plasmodium in the youngest group would render it especially susceptible to malaria infection and disease, and indeed, in this study half of community malaria cases were of persons below 15 years old. However, despite the lack of high titers or broad antibody repertoires against the parasite, the vast majority of the infections were submicroscopic and asymptomatic, and remained so for at least 5 months. This challenges the notion that individuals with little exposure to malaria have limited ability to suppress parasitaemia and would become symptomatic, but is consistent with epidemiological findings from other low transmission areas [8, 14, 39, 40]. In fact, high levels of antibodies against P. falciparum in children from Papua New Guinea were not associated with protection from malaria, rather with higher exposure to parasites [41]. The high prevalence of submicroscopic and asymptomatic infections in the 3–15 years old cohort demonstrates that despite the low antibody titers, efficient parasite-controlling immunity developed after few exposures.

Experimental and natural infection studies have shown that complete strain-specific immunity to vivax malaria may be acquired after two or three exposures, but the rate of acquisition of disease protective immunity differs between P. falciparum and P. vivax (reviewed in [42]). Abundant evidence showed that the number of clinical episodes of vivax malaria decreased earlier in life than that of falciparum malaria, suggesting a faster acquisition of protective immunity against P. vivax [42]. In this study, the intensity and breadth of antibody response in the youngest age groups was significantly higher against P. vivax, but a reversal in species-specific immunodominance occurred in adults. Two possible reasons can be suggested to explain this age-related difference in immunodominance, which are not necessarily mutually exclusive. First, the shift in species-specific response with increasing age may result from differences in the biology of the falciparum and vivax infections, such as relapses in P. vivax and higher parasitaemia in P. falciparum. The estimated SCR and SRR showed that antibodies against P. falciparum accumulated continuously and decayed at a slow rate throughout life, whereas antibodies against P. vivax were quickly acquired but were less stable. Second, the historical relative prevalence of the two species also could play a role in this difference. From the 1970s to the mid-1990s, the prevalence of P. falciparum exceeded P. vivax in the region [15], a trend that was reverted in the recent past years, resulting in P. vivax currently exceeding P. falciparum by 2:1. Hence, adults in the cohort would have historically experienced more falciparum infections than the children, who now are comparatively more frequently exposed to P. vivax. Additionally, antibody cross-reactivity between P. falciparum and P. vivax could add confounding factors to this analysis, but this could not be addressed in this study due to the co-endemicity of these two species in the region.

As transmission levels drop further, serology is being recognized as a valuable tool to monitor malaria epidemiology [7]. Examination of age-specific seroprevalence profiles can be used to detect transmission hot-spots [43] and detect changes in local transmission [35, 38]. Furthermore, absence of antibodies against Plasmodium has been used to show the success of elimination programmes in several countries [4446]. As the longevity of antibodies against Plasmodium varies between antigens [47, 48] and has both short- [4951] and long-lived [5254] components, it is imperative to choose antigenic markers with the appropriate kinetics to detect recent parasite exposure, rather than memory responses from past infections.

The kinetics of antibody responses to hundreds of proteins were explored in the longitudinal cohort, but there was little change at the level of individual markers in the 10 months studied. Children showed the most changes across time, indicative of reinforcement of memory responses or acquisition of new antibodies with time, while there were minimal changes in the adults’ responses. This is similar to what was observed in Kenya, where significant increase in breadth and intensity of antibody response after the malaria season was observed in children below 8 years old, but not in adults [55]. Nonetheless, there was significant overall increase in antibody levels from before to after the rainy season in both adults and children, suggesting that some may have had asymptomatic infections in the intervening months between sampling times. Thus, in order to accurately determine antibody kinetics for use in serological surveillance, more frequent sampling accompanied by molecular screening is required, such as in the study recently published by Helb et al. [56].

Another aim of this study was to identify antigenic targets that elicited higher antibody levels in asymptomatic carriers when compared to febrile patients, and that could be suggested as to play a role in protective responses. However, antibody titers were significantly higher in those who experienced clinical symptoms, especially in the younger age groups. Here, it may be once again the case that high levels of antibodies to Plasmodium are indicative of higher exposure rather than of protection to malaria [41]. Not only did symptomatic patients have, in the majority of cases, parasite levels that were detectable by microscopy while asymptomatic carriers had subpatent parasitaemia, the majority of symptomatic cases also carried multi-species infections. These factors likely would augment the antibody response in febrile patients, while asymptomatic carriers are capable of controlling parasite levels effectively by some other mechanism. Interestingly, recent studies showed evidence of upregulation of inhibitory receptors eliciting exhaustion-related phenotypes on T cells associated with defective effector function during chronic parasitaemia or regular reinfections with P. falciparum [57] and P. vivax [58]. The exhaustion of CD4 T cells may be associated with failure to develop a fully differentiated B cell response in these cases, such as in the subpatent asymptomatic infections common in our cohort, and result in lower levels of antibodies [59]. Nonetheless, other studies designed to discover markers of disease protection in malaria in high and low transmission settings have identified several serological correlates of protection [55, 60, 61].

In summary, the findings of the present study have implications for surveillance, control and elimination of malaria in Thailand. In particular, the data suggests children aged 3–15 years old are a sensitive group for serosurveillance monitoring changes in transmission due to malaria interventions. Additionally, in the community 100 % of P. falciparum and mixed species infections, and 90 % of P. vivax infections went unrecognized and untreated. Amongst the febrile cases, microscopy failed to detect 75 % of P. vivax infections; and of the mixed-species infections, all went unrecognized, being misdiagnosed as a single-species infection or completely undetected. Because current surveillance methods focus on case management, malaria transmission cannot be interrupted if asymptomatic infections are infectious to mosquitoes and contribute to transmission [7, 11]. Currently, implementation of molecular screening methods for population-wide surveillance is not feasible in developing countries, and the field applicable serological surveillance tools are deficient because of lack of optimal serological markers with the appropriate kinetics [36]. The mounting evidence of the shortcomings of the current malaria surveillance, detection and treatment methods argues for a major change in malaria control approaches. For example, mass drug administration to largely healthy populations has been controversial and resistance to this approach is considerable [28, 62, 63]. However, there is an urgent need for discussion and exploration of such alternative approaches by the scientific and public health communities.


The high prevalence of undetected and untreated Plasmodium infections in Tak is a challenge to Thailand’s efforts to eliminate malaria by 2030. The shortcomings of current diagnostic and treatment methods argues for a urgent change in malaria control approaches, with mass drug administration, serological and high-throughput detection methods needing to be explored as potential next-generation tools for malaria control and elimination in areas of low-transmission.



mass blood survey


passive case detection


seroconversion rate


seroreversion rate


Plasmodium falciparum


Plasmodium vivax


Plasmodium ovale


Plasmodium malariae


  1. Malaria statistics for Thailand [].

  2. WHO. World Malaria Report. Geneva: World Health Organization; 2012.

    Google Scholar 

  3. Konchom S, Singhasivanon P, Kaewkungwal J, Chupraphawan S, Thimasarn K, Kidson C, et al. Trend of malaria incidence in highly endemic provinces along the Thai borders, 1991–2001. Southeast Asian J Trop Med Public Health. 2003;34:486–94.

    PubMed  Google Scholar 

  4. Thailand–Malaria Factsheets—Thailand National Strategic Plan [].

  5. Thailand–Malaria Factsheets—Thailand Malaria Plan [].

  6. Wongsrichanalai C, Barcus MJ, Muth S, Sutamihardja A, Wernsdorfer WH. A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT). Am J Trop Med Hyg. 2007;77:119–27.

    PubMed  Google Scholar 

  7. malERA Consultative Group on Diagnoses and Diagnostics. A research agenda for malaria eradication: diagnoses and diagnostics. PLoS Med. 2011;8:e1000396.

    Article  Google Scholar 

  8. Harris I, Sharrock WW, Bain LM, Gray KA, Bobogare A, Boaz L, et al. A large proportion of asymptomatic Plasmodium infections with low and sub-microscopic parasite densities in the low transmission setting of Temotu Province, Solomon islands: challenges for malaria diagnostics in an elimination setting. Malar J. 2010;9:254.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lindblade KA, Steinhardt L, Samuels A, Kachur SP, Slutsker L. The silent threat: asymptomatic parasitemia and malaria transmission. Expert Rev Anti Infect Ther. 2013;11:623–39.

    Article  CAS  PubMed  Google Scholar 

  10. Alves FP, Durlacher RR, Menezes MJ, Krieger H, Silva LH, Camargo EP. High prevalence of asymptomatic Plasmodium vivax and Plasmodium falciparum infections in native Amazonian populations. Am J Trop Med Hyg. 2002;66:641–8.

    PubMed  Google Scholar 

  11. Okell LC, Ghani AC, Lyons E, Drakeley CJ. Submicroscopic infection in Plasmodium falciparum-endemic populations: a systematic review and meta-analysis. J Infect Dis. 2009;200:1509–17.

    Article  PubMed  Google Scholar 

  12. Steenkeste N, Rogers WO, Okell L, Jeanne I, Incardona S, Duval L, et al. Sub-microscopic malaria cases and mixed malaria infection in a remote area of high malaria endemicity in Rattanakiri province, Cambodia: implication for malaria elimination. Malar J. 2010;9:108.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Moonen B, Cohen JM, Snow RW, Slutsker L, Drakeley C, Smith DL, et al. Operational strategies to achieve and maintain malaria elimination. Lancet. 2010;376:1592–603.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Okell LC, Bousema T, Griffin JT, Ouedraogo AL, Ghani AC, Drakeley CJ. Factors determining the occurrence of submicroscopic malaria infections and their relevance for control. Nat Commun. 2012;3:1237.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhou G, Sirichaisinthop J, Sattabongkot J, Jones J, Bjornstad ON, Yan G, et al. Spatio-temporal distribution of Plasmodium falciparum and P. vivax malaria in Thailand. Am J Trop Med Hyg. 2005;72:256–62.

    PubMed  Google Scholar 

  16. Carrara VI, Sirilak S, Thonglairuam J, Rojanawatsirivet C, Proux S, Gilbos V, et al. Deployment of early diagnosis and mefloquine-artesunate treatment of falciparum malaria in Thailand: the Tak Malaria Initiative. PLoS Med. 2006;3:e183.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Baum E, Sattabongkot J, Sirichaisinthop J, Kiattibutr K, Davies D, Jain A, et al. Submicroscopic and asymptomatic Plasmodium falciparum and Plasmodium vivax infections are common in western Thailand—molecular and serological evidence. Malaria Journal. 2015;14:95.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Putaporntip C, Hongsrimuang T, Seethamchai S, Kobasa T, Limkittikul K, Cui L, et al. Differential prevalence of Plasmodium infections and cryptic Plasmodium knowlesi malaria in humans in Thailand. J Infect Dis. 2009;199:1143–50.

    Article  CAS  PubMed  Google Scholar 

  19. Jongwutiwes S, Buppan P, Kosuvin R, Seethamchai S, Pattanawong U, Sirichaisinthop J, et al. Plasmodium knowlesi malaria in humans and macaques Thailand. Emerg Infect Dis. 2011;17:1799–806.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Coleman RE, Kumpitak C, Ponlawat A, Maneechai N, Phunkitchar V, Rachapaew N, et al. Infectivity of asymptomatic Plasmodium-infected human populations to Anopheles dirus mosquitoes in western Thailand. J Med Entomol. 2004;41:201–8.

    Article  PubMed  Google Scholar 

  21. Kritsiriwuthinan K, Ngrenngarmlert W. Molecular screening of Plasmodium infections among migrant workers in Thailand. J Vector Borne Dis. 2011;48:214–8.

    CAS  PubMed  Google Scholar 

  22. Rougemont M, Van Saanen M, Sahli R, Hinrikson HP, Bille J, Jaton K. Detection of four Plasmodium species in blood from humans by 18S rRNA gene subunit-based and species-specific real-time PCR assays. J Clin Microbiol. 2004;42:5636–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Divis PC, Shokoples SE, Singh B, Yanow SK. A TaqMan real-time PCR assay for the detection and quantitation of Plasmodium knowlesi. Malar J. 2010;9:344.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. PlasmoDB—Plasmodium genomics resource []. Accessed 12 Mar 2015.

  25. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001;98:5116–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Drakeley CJ, Corran PH, Coleman PG, Tongren JE, McDonald SL, Carneiro I, et al. Estimating medium- and long-term trends in malaria transmission by using serological markers of malaria exposure. Proc Natl Acad Sci USA. 2005;102:5108–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zar JH. Biostatistical analysis. 4th ed. Upper Saddle River: Prentice Hall; 1999.

    Google Scholar 

  28. Imwong M, Nguyen TN, Tripura R, Peto TJ, Lee SJ, Lwin KM, et al. The epidemiology of subclinical malaria infections in South-East Asia: findings from cross-sectional surveys in Thailand–Myanmar border areas, Cambodia, and Vietnam. Malar J. 2015;14:381.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Lo E, Zhou G, Oo W, Afrane Y, Githeko A, Yan G. Low parasitemia in submicroscopic infections significantly impacts malaria diagnostic sensitivity in the highlands of Western Kenya. PLoS One. 2015;10:e0121763.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Lee PW, Liu CT, Rampao HS, Rosario VE, Shaio MF. Pre-elimination of malaria on the island of Principe. Malar J. 2010;9:26.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lee PW, Liu CT, do Rosario VE, de Sousa B, Rampao HS, Shaio MF. Potential threat of malaria epidemics in a low transmission area, as exemplified by Sao Tome and Principe. Malar J. 2010;9:264.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lin JT, Saunders DL, Meshnick SR. The role of submicroscopic parasitemia in malaria transmission: what is the evidence? Trends Parasitol. 2014;30:183–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Imwong M, Jindakhad T, Kunasol C, Sutawong K, Vejakama P, Dondorp AM. An outbreak of artemisinin resistant falciparum malaria in Eastern Thailand. Sci Rep. 2015;5:17412.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. World Health Organization. World Malaria Report. Geneva: World Health Organization; 2014.

    Google Scholar 

  35. Drakeley C, Cook J. Potential contribution of sero-epidemiological analysis for monitoring malaria control and elimination: historical and current perspectives. Adv Parasitol. 2009;69:299–352.

    Article  PubMed  Google Scholar 

  36. Elliott SR, Fowkes FJ, Richards JS, Reiling L, Drew DR, Beeson JG. Research priorities for the development and implementation of serological tools for malaria surveillance. F1000Prime Rep. 2014;6:100.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Corran P, Coleman P, Riley E, Drakeley C. Serology: a robust indicator of malaria transmission intensity? Trends Parasitol. 2007;23:575–82.

    Article  PubMed  Google Scholar 

  38. Stewart L, Gosling R, Griffin J, Gesase S, Campo J, Hashim R, et al. Rapid assessment of malaria transmission using age-specific sero-conversion rates. PLoS One. 2009;4:e6083.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Vallejo AF, Chaparro PE, Benavides Y, Alvarez A, Quintero JP, Padilla J, et al. High prevalence of sub-microscopic infections in Colombia. Malar J. 2015;14:201.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Golassa L, Enweji N, Erko B, Aseffa A, Swedberg G. Detection of a substantial number of sub-microscopic Plasmodium falciparum infections by polymerase chain reaction: a potential threat to malaria control and diagnosis in Ethiopia. Malar J. 2013;12:352.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Stanisic DI, Fowkes FJ, Koinari M, Javati S, Lin E, Kiniboro B, et al. Acquisition of antibodies against Plasmodium falciparum merozoites and malaria immunity in young children and the influence of age, force of infection, and magnitude of response. Infect Immun. 2015;83:646–60.

    Article  PubMed  Google Scholar 

  42. Mueller I, Galinski MR, Tsuboi T, Arevalo-Herrera M, Collins WE, King CL. Natural acquisition of immunity to Plasmodium vivax: epidemiological observations and potential targets. Adv Parasitol. 2013;81:77–131.

    Article  PubMed  Google Scholar 

  43. Bousema T, Youssef RM, Cook J, Cox J, Alegana VA, Amran J, et al. Serologic markers for detecting malaria in areas of low endemicity, Somalia, 2008. Emerg Infect Dis. 2010;16:392–9.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bruce-Chwatt LJ, Draper CC, Konfortion P. Seroepidemiological evidence of eradication of malaria from Mauritius. Lancet. 1973;2:547–51.

    Article  CAS  PubMed  Google Scholar 

  45. Bruce-Chwatt LJ, Draper CC, Avramidis D, Kazandzoglou O. Sero-epidemiological surveillance of disappearing malaria in Greece. J Trop Med Hyg. 1975;78:194–200.

    CAS  PubMed  Google Scholar 

  46. Cook J, Reid H, Iavro J, Kuwahata M, Taleo G, Clements A, et al. Using serological measures to monitor changes in malaria transmission in Vanuatu. Malar J. 2010;9:169.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Fowkes FJ, McGready R, Cross NJ, Hommel M, Simpson JA, Elliott SR, et al. New insights into acquisition, boosting, and longevity of immunity to malaria in pregnant women. J Infect Dis. 2012;206:1612–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Akpogheneta OJ, Duah NO, Tetteh KK, Dunyo S, Lanar DE, Pinder M, et al. Duration of naturally acquired antibody responses to blood-stage Plasmodium falciparum is age dependent and antigen specific. Infect Immun. 2008;76:1748–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Struik SS, Riley EM. Does malaria suffer from lack of memory? Immunol Rev. 2004;201:268–90.

    Article  PubMed  Google Scholar 

  50. Kinyanjui SM, Bejon P, Osier FH, Bull PC, Marsh K. What you see is not what you get: implications of the brevity of antibody responses to malaria antigens and transmission heterogeneity in longitudinal studies of malaria immunity. Malar J. 2009;8:242.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Achtman AH, Bull PC, Stephens R, Langhorne J. Longevity of the immune response and memory to blood-stage malaria infection. Curr Top Microbiol Immunol. 2005;297:71–102.

    CAS  PubMed  Google Scholar 

  52. Wipasa J, Suphavilai C, Okell LC, Cook J, Corran PH, Thaikla K, et al. Long-lived antibody and B Cell memory responses to the human malaria parasites, Plasmodium falciparum and Plasmodium vivax. PLoS Pathog. 2010;6:e1000770.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Clark EH, Silva CJ, Weiss GE, Li S, Padilla C, Crompton PD, et al. Plasmodium falciparum malaria in the Peruvian Amazon, a region of low transmission, is associated with immunologic memory. Infect Immun. 2012;80:1583–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ndungu FM, Lundblom K, Rono J, Illingworth J, Eriksson S, Farnert A. Long-lived Plasmodium falciparum specific memory B cells in naturally exposed Swedish travelers. Eur J Immunol. 2013;43:2919–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Crompton PD, Kayala MA, Traore B, Kayentao K, Ongoiba A, Weiss GE, et al. A prospective analysis of the Ab response to Plasmodium falciparum before and after a malaria season by protein microarray. Proc Natl Acad Sci USA. 2010;107:6958–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Helb DA, Tetteh KK, Felgner PL, Skinner J, Hubbard A, Arinaitwe E, et al. Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities. Proc Natl Acad Sci USA. 2015;112:E4438–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Illingworth J, Butler NS, Roetynck S, Mwacharo J, Pierce SK, Bejon P, et al. Chronic exposure to Plasmodium falciparum is associated with phenotypic evidence of B and T cell exhaustion. J Immunol. 2013;190:1038–47.

    Article  CAS  PubMed  Google Scholar 

  58. Costa PA, Leoratti FM, Figueiredo MM, Tada MS, Pereira DB, Junqueira C, et al. Induction of inhibitory receptors on T cells during Plasmodium vivax malaria impairs cytokine production. J Infect Dis. 2015;212:1999–2010.

    Article  PubMed  Google Scholar 

  59. Wykes MN, Horne-Debets JM, Leow CY, Karunarathne DS. Malaria drives T cells to exhaustion. Front Microbiol. 2014;5:249.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Dent AE, Nakajima R, Liang L, Baum E, Moormann AM, Sumba PO, et al. Plasmodium falciparum protein microarray antibody profiles correlate with protection from symptomatic malaria in Kenya. J Infect Dis. 2015;212(9):1429–38.

    Article  PubMed  Google Scholar 

  61. Torres KJ, Castrillon CE, Moss EL, Saito M, Tenorio R, Molina DM, et al. Genome-level determination of Plasmodium falciparum blood-stage targets of malarial clinical immunity in the Peruvian Amazon. J Infect Dis. 2015;211:1342–51.

    PubMed  Google Scholar 

  62. von Seidlein L. The failure of screening and treating as a malaria elimination strategy. PLoS Med. 2014;11:e1001595.

    Article  Google Scholar 

  63. Newby G, Hwang J, Koita K, Chen I, Greenwood B, von Seidlein L, et al. Review of mass drug administration for malaria and its operational challenges. Am J Trop Med Hyg. 2015;93:125–34.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors’ contributions

EB performed all experiments (except microarray probing), statistical analysis, and wrote the manuscript. JS, JS, KK collected blood samples and donor symptom history. AJ and OT performed microarray probing. ML database management. HD contributed to some data analysis. LC, PF, GY helped conceive the study and reviewed drafts of the manuscript. All authors read and approved the final manuscript.


We want to thank all study participants, and the field malaria workers and health care workers for collecting samples and surveillance data. We thank also Dr. Alan G. Barbour at UCI for allowing usage of QIAcube equipment for DNA extraction of blood samples.

Competing interests

DHD and PLF declare a financial interest in Antigen Discovery, Inc. DHD and PLF and the University of California, Irvine may financially benefit from this interest if the company is successful in marketing its products that are related to this research. The terms of this arrangement have been reviewed and approved by the University of California, Irvine in accordance with its conflict of interest policies. The remaining authors declare that they have no competing interests.

Availability of data and material

The complete list of features present on the Pf/Pv500 protein microarray used in this study is available on NCBI’s Gene Expression Omnibus under platform accession number GPL21194. The raw and normalized data of antibody binding to proteins on the Pf/Pv500 microarray is publicly available through NCBI’s Gene Expression Omnibus Series accession number GSE75593.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the Institutional Review Boards of the Pennsylvania State University protocol (number 34319); University of California Irvine (number 2012-9123); Thai Ministry of Health (number 0435.3/857), and EC of the Department of Disease Control Ministry of Public Health, Thailand protocol number 7/54-479. Blood samples were collected from donors who gave informed consent or assent for minors.


This study was supported by NIH grants U19AI089672, U19AI089686, R44AI066791, R01AI095916. The funding agency did not participate in the design of the study, data collection, analysis and interpretation of data, or in writing the manuscript.

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Correspondence to Elisabeth Baum.

Additional files


Additional file 1. Age-fitted seroprevalence plots for seroconversion and reversion calculations. The average seroprevalence for each age group is plotted for the seroreactive Plasmodium proteins, denoted as P1 through P326. Their corresponding gene ID and protein description are found in Additional file 4.


Additional file 2. Age distribution in years of samples collected at community mass blood survey in Mae Salid Noi (blue) and hospital/malaria clinic PCD (orange). The darkened areas represent Plasmodium-positive qPCR samples. The box plot above the histogram indicates the first and third quartiles, the line inside the box the median. The 1.5× interquartile range is indicated by the horizontal line bisecting the box.


Additional file 3. Percentage of Plasmodium-positive samples amongst all samples collected per age group in years from the community MBS (blue) and hospital/clinic PCD (orange). The number of samples collected in each age group appears inside the corresponding bar. The p-value for Two-Proportion Z-test is shown as asterisks: ** <0.001; p values greater than 0.05 are not shown.


Additional file 4. List of 326 seroreactive P. falciparum and P. vivax polypeptides recognized by antibodies in plasma from exposed villagers and malaria patients in Tak Province, Thailand; and their respective seroprevalence rates, seroconversion (SCR) and seroreversion (SRR) rates, and Wilcoxon p values for comparison of mean antibody responses in asymptomatic and febrile malaria senior cases.


Additional file 5. Average and 95% confidence interval of intensity and breadth of antibody response to 326 Plasmodium sp. proteins by plasma samples from the community and hospital/clinic patients, segregated by malaria status.


Additional file 6. Average and 95% confidence interval of intensity and breadth of antibody responses to 326 Plasmodium sp. proteins by 298 plasma samples from the community, segregated by age groups in decades.


Additional file 7. Average temporal changes in intensity of antibody binding to Plasmodium antigens in longitudinal samples collected from adults (top) and children (bottom) from Mae Salid Noi community. Cases had confirmed Plasmodium infections in March 2013 and negative qPCR at the other two time-points; controls were qPCR-negative at all 3 time-points. The line represent the mean pixel intensity value of antibody binding to 326 antigens, error bars represent 95% confidence interval of the mean. Wilcoxon test p-values comparing the means between time-points are shown as asterisks: *** <0.0001; p values greater than 0.05 are not shown.

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Baum, E., Sattabongkot, J., Sirichaisinthop, J. et al. Common asymptomatic and submicroscopic malaria infections in Western Thailand revealed in longitudinal molecular and serological studies: a challenge to malaria elimination. Malar J 15, 333 (2016).

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