Molecular surveillance over 14 years confirms reduction of Plasmodium vivax and falciparum transmission after implementation of Artemisinin-based combination therapy in Papua, Indonesia.

Genetic epidemiology can provide important insights into parasite transmission that can inform public health interventions. The current study compared long-term changes in the genetic diversity and structure of co-endemic Plasmodium falciparum and P. vivax populations. The study was conducted in Papua Indonesia, where high-grade chloroquine resistance in P. falciparum and P. vivax led to a universal policy of Artemisinin-based Combination Therapy (ACT) in 2006. Microsatellite typing and population genetic analyses were undertaken on available isolates collected between 2004 and 2017 from patients with uncomplicated malaria (n = 666 P. falciparum and n = 615 P. vivax). The proportion of polyclonal P. falciparum infections fell from 28% (38/135) before policy change (2004-2006) to 18% (22/125) at the end of the study (2015-2017); p<0.001. Over the same period, polyclonal P. vivax infections fell from 67% (80/119) to 35% (33/93); p<0.001. P. falciparum strains persisted for up to 9 years compared to 3 months for P. vivax, reflecting higher rates of outbreeding in the latter. Sub-structure was observed in the P. falciparum population, but not in P. vivax, confirming different patterns of outbreeding. The P. falciparum population exhibited 4 subpopulations that changed in frequency over time. Notably, a sharp rise was observed in the frequency of a minor subpopulation (K2) in the late post-ACT period, accounting for 100% of infections in late 2016-2017. The results confirm epidemiological evidence of reduced P. falciparum and P. vivax transmission over time. The smaller change in P. vivax population structure is consistent with greater outbreeding associated with relapsing infections and highlights the need for radical cure to reduce recurrent infections. The study emphasizes the challenge in disrupting P. vivax transmission and demonstrates the potential of molecular data to inform on the impact of public health interventions.

Introduction

Despite significant progress in reducing the burden of malaria in the Asia-Pacific over the last decade, recent World Malaria Reports have shown that these gains are not universal. And, where they have occurred, they are associated with an increase in the proportion of malaria due to P. vivax [1]. The differential impact of enhanced malaria control activities can be explained, in part, by fundamental biological and epidemiological differences between P. vivax and P. falciparum, including the ability of P. vivax to form dormant liver stages (hypnozoites) and a greater prevalence of low-density infections [2].

Assessment of malaria control interventions through conventional malariometric surveillance focuses on quantifying case numbers and parasite prevalence from which transmission intensity can be inferred. Case surveillance and cross-sectional surveys require comprehensive collection of data on parasitised individuals that is restricted by logistical constraints and the inability to detect very low-density infections. Furthermore, case surveillance has limited ability to identify subtle changes in parasite populations associated with changing epidemiology and selective pressures on the parasites [3].

Genotyping has been proposed as a complementary tool to identify early changes in parasite population structure, gene flow and parasite diversity [2, 4–8]. However, few molecular studies have assessed longitudinal molecular changes in the parasite, and none have compared co-endemic P. falciparum and P. vivax populations over a period longer than four years [9-13]. The lack of a comprehensive longitudinal evaluation of co-endemic parasite populations is a major gap in our understanding of how public health interventions, such as the implementation of new antimalarial drug policies, differentially affect P. vivax and P. falciparum [4].

In 2004, clinical trials undertaken in Papua Indonesia demonstrated that both P. falciparum and P. vivax were highly resistant to sulphadoxine pyrimethamine and chloroquine, which were the first-line treatments against uncomplicated malaria at the time [14]. In March 2006, antimalarial guidelines were changed, and a universal Artemisinin-based Combination Therapy (ACT) policy was implemented. DHA-piperaquine (DP) was advised for uncomplicated malaria and intravenous artesunate for severe malaria. The change in policy to highly efficacious antimalarial treatment was associated with a 51% fall in the incidence of P. falciparum and a 28% fall in the incidence of P. vivax [1]. The aim of the current study was to characterize the temporal changes in the genetic diversity and structure of co-endemic P. vivax and P. falciparum populations over the course of more than a decade (2004–2017) of concerted interventions, including the introduction of a new treatment regimen into an area with multidrug resistant malaria.

Results

Between 2004 and 2017, a total of 1,197 P. vivax and 1,566 P. falciparum clinical isolates were collected from patients with uncomplicated malaria, of which 628 (52%) P. vivax isolates and 671 (43%) P. falciparum isolates were available for molecular analysis. A total of 615 (97.9%) P. vivax and 666 (99.3%) P. falciparum isolates could be genotyped successfully (S1 Fig). The sample size ranged from 93 to 176 in each of the five predefined time intervals: pre-ACT-Policy change (2004–2006), early transition to ACT-Policy (2006–2009), late transition to ACT-Policy implementation (2009–2012), early Post-ACT implementation (2012–2015), and late Post-ACT implementation (2015–2017) (S1 Table). All markers exhibited a minimum 5% minor allele frequency, and a genotyping success rate exceeding >80% (S2 Table).

The parasitaemia, age and gender composition of the successfully genotyped samples were comparable to all samples available during each period (S1 Table). Complete demographic data were available for 96% (588/615) individuals infected with P. vivax and 96% (638/666) with P. falciparum (S3 Table). Age composition was comparable among the five periods for both species (S3 Table). The isolates were collected predominantly from adult patients (aged ≥15 years), contributing 76% (450) of P. vivax isolates and 86% (548) of P. falciparum isolates. Likewise, gender distribution was similar across the five periods for both species (S3 Table), with males comprising approximately half of all patients with P. vivax (45%, 278/588) and P. falciparum infections (49%, 328/640) (S3 Table).

The geometric mean parasitaemia differed significantly over the time intervals for both P. vivax (p<0.001) and P. falciparum infections (p<0.001; S3 Table). There was a trend of increasing P. falciparum parasitaemia over the study period, rising from 10,423 parasites/μL [95%CI 8,750–124,164] in 2004–2006 to 18,981 parasites/μL [95%CI 15,856–22,722] in 2015–2017 (rho = 0.196, p<0.001). However, there was no correlation between parasitaemia and multiplicity of infection (MOI) for either species (P. vivax: rho = -0.077, p = 0.877; P. falciparum: rho = -0.058, p = 0.148).

The proportion of polyclonal infections decreased significantly over time for both species. Polyclonal P. vivax infections fell from 67% (80/119) in 2004–2006 to 35% (33/93) in 2015–2017; (p < 0.001), while polyclonal P. falciparum infections fell from 28% (38/135) in 2004–2006 to 18% (22/125) in 2015–2017; (p = 0.009); Table 1). There was also a decrease in the complexity of infection over time in both the P. vivax and P. falciparum populations (S2 Fig). In the P. vivax population, the mean (SD) multiplicity of infection (MOI) decreased from 1.9 (0.7) in 2004–2006 to 1.4 (0.7) in 2015–2017 (p<0.001). In the P. falciparum population, the corresponding change was 1.3 (0.5) to 1.2 (0.4) (p = 0.046; Table 1). The median (range) number of multiallelic loci per infection (MLOCI) fell in the P. vivax population (from 3 (1–7) to 1 (1–5); p = 0.005) but remained low in the P. falciparum population throughout the study period (1 (1–5) in 2004–2006 and 1 (1–5) in 2015–2017 (p = 0.161; Fig 1).

Table 1

Within-host and population diversity.

Period	N	Polyclonal N [%; CI95%]	MOIMean (SD)	MOIMedian (Max)	MLOCIMedian (Max)	HEMean (SD)	RsMean (SD)
P. vivax
2004–2006	119	80 [67; 59–76]	1.9 (0.7)	2 (4)	3 (7)	0.864 (0.06)	14.6 (6.2)
2006–2009	143	81 [57; 49–65]	1.8 (0.8)	2 (4)	3 (7)	0.858 (0.06)	15.6 (6.5)
2009–2012	114	44 [38; 29–47]	1.4 (0.6)	1 (4)	2 (7)	0.852 (0.07)	16.2 (7.2)
2012–2015	146	58 [40; 32–48]	1.4 (0.6)	1 (4)	2 (7)	0.854 (0.06)	15.8 (6.1)
2015–2017	93	33 [35; 26–45]	1.4 (0.7)	1 (5)	1 (5)	0.860 (0.07)	17.0 (8.3)
P. falciparum
2004–2006	135	38 [28; 21–36]	1.3 (0.5)	1 (3)	1 (5)	0.594 (0.2)	7.3 (2.5)
2006–2009	128	38 [29; 21–37]	1.3 (0.5)	1 (3)	2 (5)	0.628 (0.3)	7.5 (2.5)
2009–2012	102	28 [27; 19–36]	1.3 (0.4)	1 (2)	1 (4)	0.623 (0.3)	6.4 (2.2)
2012–2015	176	35 [20; 14–26]	1.2 (0.4)	1 (2)	1 (5)	0.545 (0.3)	5.2 (2.1)
2015–2017	125	22 [18; 11–24]	1.2 (0.4)	1 (3)	1 (5)	0.602 (0.2)	7.0 (2.3)

MOI: Multiplicity of Infection; MLOCI: Number of multiallelic loci; CI95%: 95% Confidence interval of the proportion of polyclonal infections Rs: Allelic richness; H: Expected heterozygosity

Fig 1

Proportion of multiallelic loci per infection (MLOCI) by temporal period.

Bar charts illustrating the percentage of polyclonal infections with the given number of multiallelic loci for each of the 5 temporal periods in a) P. vivax and b) P. falciparum. Both species exhibit an overall decline over time in the percentage of infections with 2 or more multiallelic loci.

There was no temporal trend in genetic diversity, as measured by allelic richness (Rs), with moderate fluctuations observed in both the P. vivax and P. falciparum populations (Table 1). Over the study period, there was a slight increase in Rs in the P. vivax population from a mean (SD) of 14.6 (6.2) to 17 (8.3), but this was not significant (p = 0.999). In the P. falciparum population, Rs was 7.3 (2.5) in 2004–2006 and 7.0 (2.3) in 2015–2017 (p = 0.518). Similar trends were observed for the expected heterozygosity (H) in both species (Table 1).

Multi-locus genotypes (MLGs) were assembled from 636 P. falciparum isolates and 461 P. vivax isolates with complete genotyping data. In the P. falciparum population, 69 MLGs were multiply observed (repeated MLGs) among 206 individuals, and the proportion of these individuals increased from 32% (40/126) in 2004–2006 to 45% (54/119) in 2015–2017; (p<0.001; Table 2). In the P. vivax population, only 4 repeated MLGs were observed among 8 individuals; however, the proportion of these individuals also increased over time, from 0% during 2004–2006 to 7.7% (6/78) in 2015–2017 (p = 0.004; Table 2). Two of the 69 P. falciparum repeated MLGs persisted for up to 9 years (n = 15; Fig 2), while none of the four P. vivax repeated MLGs persisted for more than 3 months (n = 8; Fig 2, Table 2).

Table 2

Frequency of infections with repeated multi-locus genotypes (MLGs).

Periods	P. vivax		P. falciparum
	Total infections with MLGsa; n	Proportion of infections with repeated MLGs; %, [CI95]b	Total infections with MLGsa; n	Proportion of infections with repeated MLGs; %, [CI95]b
2004–2006	96	0 [0–0]	126	32 [24–40]
2006–2009	125	2 [1–4]	124	18 [11–24]
2009–2012	69	0 [0–0]	98	28 [19–36]
2012–2015	93	0 [0–0]	169	37 [30–45]
2015–2017	78	8 [2–14]	119	45 [36–54]
Total	461	1.7 [0.5–2.9]	636	32 [29–36]

a: Total number of infections with complete multi-locus genotypes (MLGs)

b. Proportion of individuals infected with repeated MLGs and corresponding 95% Confidence interval (CI95).

Fig 2

Persistence of repeated MLGs over time.

Dot points illustrating the year when repeated MLGs were detected in each of a) P. vivax and b) P. falciparum. The P. vivax repeated MLGs were constructed across 8 loci, and the P. falciparum infections were constructed across 9 loci. The persistence of P. falciparum strains (repeated MLGs) reached up to 9 years (green) and was markedly greater than for P. vivax, which did not persist for over a year. However, most P. falciparum strains (repeated MLGs) had shorter duration (less than a year).

The multi-locus linkage disequilibrium (LD) in the P. vivax population was low throughout the study period, although the index of association (I) increased 2.2-fold from 0.0046 in 2004–2006 to 0.0102 in 2015–2017 (p<0.01) (Table 3). The LD in the P. falciparum population was consistently higher than the LD in P. vivax, and the index of association increased 5.6-fold between 2004–2006 and 2015–2017, from 0.0415 to 0.2340 (p<0.01). The trends of increasing LD over time remained after restricting the analysis to low complexity infections, confirming that the results were not affected by potential MLG reconstruction errors (Table 3). There was no evidence of a clonal outbreak in the P. falciparum population (S3 Fig).

Table 3

Multi-locus Linkage Disequilibrium.

Subgroups	All infections, N	All infections, IAS	Low complexity, N	Low complexity, IAS
P. vivax
2004–2006	96	0.0046*	51	0.0064NS
2006–2009	125	0.0038NS	74	0.0036NS
2009–2012	70	0.0116**	52	0.0113*
2012–2015	92	-0.0043NS	65	-0.0092NS
2015–2017	78	0.0102**	66	0.0157*
P. falciparum
2004–2006	126	0.0415**	116	0.0452**
2006–2009	124	0.0433**	106	0.0461**
2009–2012	98	0.0504**	91	0.0527**
2012–2015	169	0.0375**	161	0.0381**
2015–2017	119	0.234**	113	0.2257**

Only samples with no missing data were included in the analyses.

* p<0.05

** p< 0.01

NS: not significant.

The Bayesian clustering algorithm implemented in STRUCTURE software was unable to detect population substructure among the P. vivax isolates analysed (S4 Fig). In contrast, delta K analysis predicted between 2 and 4 P. falciparum subpopulations (S4 Fig). When assuming 2 subpopulations, 70% (n = 466) of the isolates showed predominant (i.e., not mixed) ancestry to one of the two subpopulations (Fig 3A). Most of the temporal periods had a 3:2 ratio composition of isolates belonging to K1 or K2, respectively (S4 Table). When assuming four subpopulations, 50% (n = 337) of the isolates showed predominant ancestry to one of the four subpopulations. Amongst these non-mixed isolates (n = 337), in the first two temporal periods (2004–2006 and 2006–2009), 93% (57/61) and 76% (53/70) of the isolates had ancestry to either the K1 or K3 subpopulations (S4 Table). In contrast, in the late post-ACT transition period (2015–2017), 78% (n = 67/86) of the isolates had ancestry to either the K2 or K4 subpopulations (S4 Table). Notably, all isolates collected at the end of 2016 and throughout 2017 had ancestry to the minor K2 subpopulation (Fig 3B).

Fig 3

Temporal trends in the prevalence of P. falciparum sub-populations.

STRUCTURE bar plots illustrating the distribution of P. falciparum isolates with ancestry to the given K sub-populations over time. Panel a) presents the data assuming K = 2, and panel b) presents the data assuming K = 4. Each vertical bar presents a single isolate, whose relative ancestry to each of the given K sub-populations is illustrated by the proportionate colour-coded segments. Isolates are ordered from left to right on the x-axis by date of collection (oldest to most recent). At K = 2, each temporal period exhibits an approximate 3:2 ratio composition of isolates with predominant ancestry (>85%) to K1 and K2 respectively. At K = 4, majority of isolates in the first two temporal periods have predominant ancestry to K1 or K3, whilst the majority in the later periods have predominant ancestry to K2 or K4. Isolates with predominant ancestry to K2 prevail in late 2016 and throughout 2017.

The pattern of sub-structure in the P. falciparum clinical isolates mirrored patterns observed in a previous study conducted in Papua between 2011 and 2014 [15]. Using genotyping data generated on symptomatic and asymptomatic P. falciparum cases from Papua and three other regions of Indonesia (Bangka Belitung, West Kalimantan and Nusa Tenggara), the 2011–14 study found a notable sub-population of asymptomatic P. falciparum cases presenting in 2013 (defined as Papua asymptomatic K1) that appeared to have been imported from a region close to Nusa Tenggara [15]. We sought to determine whether the Papuan symptomatic K2 subpopulation observed here and the previously described asymptomatic K1 subpopulation reflected the same reservoir. Multiple correspondence analysis (MCA) on the current and previously described P. falciparum datasets [15, 16] revealed higher genetic relatedness between the Papuan symptomatic K2 subpopulation, the putatively imported Papuan asymptomatic K1 subpopulation and the infections from Nusa Tenggara than the other Papuan infections (S5 Fig).

Discussion

This study presents a comprehensive longitudinal genetic investigation of P. falciparum and P. vivax, comprising data from over 1,200 parasite isolates collected over 14 years. It is the first longitudinal genetic analysis documenting the diversity and structure of co-endemic P. falciparum and P. vivax populations before, during, and after the implementation of a universal ACT policy. The results reveal important molecular cues consistent with differential patterns in the decline in transmission of P. vivax and P. falciparum following policy change in a region with multidrug resistant malaria; these findings have been confirmed with complementary epidemiological data from a large-scale case surveillance study in the same area [1, 15]. The genetic results also highlight the emergence of a subpopulation of potentially adaptive clinical P. falciparum infections in the late post-ACT transition period.

Multiple clone infections can arise by superinfection in the mosquito (e.g. due to interrupted feeding) as well as superinfection in the patient following serial infected mosquito-bites. The risk of superinfection is likely to be greater in high transmission settings. Previous studies have demonstrated a positive correlation between the complexity and/or proportion of polyclonal malaria infections and transmission intensity [17-19]. Consequently, reduction in the complexity or prevalence of polyclonal malaria infections has been proposed as an early marker of decreasing transmission in the given population [18]. Our study revealed significant reductions in the proportion of polyclonal infections in P. vivax and P. falciparum between the earliest and latest ACT transition periods (1.8- and 1.6-fold, respectively). The difference in the magnitude of the reduction in polyclonal infections between the species likely represents the higher pre-ACT baseline prevalence of polyclonal P. vivax infections compared to P. falciparum. These findings highlight constraints in the utility of the complexity or prevalence of polyclonal infection to monitor reductions in malaria transmission in low endemic settings where the complexity is low at the start of monitoring. Although not observed here, it should also be noted that some studies have observed high complexity of infection in low endemic settings, potentially reflecting factors such as polyclonal imported infections [20].

In P. falciparum, population genetic diversity has been proposed to correlate positively with endemicity [17]. Although our study found a trend of declining allelic richness in the P. falciparum population from 2004 until 2015, this did not reach statistical significance. Indeed, Nkhoma and colleagues also found population diversity to be limited as a measure of endemicity in their longitudinal survey of P. falciparum on the Thai-Myanmar border [18]. This finding may reflect the high human movement between Papua and other Indonesian islands, which could provide a diverse reservoir of new alleles being introduced into Papua [14, 15]. A study of P. vivax population diversity in Sri Lanka reported increasing diversity despite declining transmission, potentially reflecting imported cases amongst other factors. [20]. The available evidence suggests that low P. vivax diversity may not be a prerequisite for elimination of this species. Indeed, in contrast to P. falciparum, there was no change in genetic diversity in the Papuan P. vivax population over time, likely reflecting a combination of importation and enhanced transmission opportunities afforded by the dormant liver stage.

Increasing proportions of individuals harbouring repeated multi-locus genotypes (MLGs) and long persistence of repeated MLGs are strong predictors of declining malaria transmission [18, 21, 22]. Although only 4 P. vivax repeated MLGs were detected in the study, there was a modest increase in their prevalence in the late temporal periods, suggesting a discrete increase in inbreeding events in this species. A total of 69 repeated MLGs were detected in the P. falciparum population, and their proportions demonstrated a significant increase (from 32 to 45%) between the pre-ACT and late post-ACT transition periods, providing strong evidence of increasing self-fertilization events over time in this species. Strikingly, two of the P. falciparum repeated MLGs (3%) persisted for 8 and 9 years. A previous longitudinal study of P. falciparum conducted in a low-endemic area demonstrated a similar distribution in repeated MLGs prevalence over time, and also observed persistence of strains for up to 8 years [22].

Since mixed-clone infections are a main contributor of cross-fertilisation events, LD is expected to correlate negatively with the proportion of polyclonal infections and thus, theoretically, should increase as the transmission intensity decreases in the absence of outbreaks. There was no evidence of large outbreaks of one or a few genetic strains in the P. vivax or P. falciparum populations in our study. Although overall LD levels remained low, there was a modest (2.2-fold) increment in I estimates in the P. vivax population between the pre-ACT and late post-ACT transition periods, likely reflecting the decline in polyclonal infections and subtle increase in frequency of repeated MLGs. In P. falciparum, a larger (5.6-fold) increase in I estimates was observed between the pre-ACT and late post-ACT period. In conjunction with the increasing prevalence of repeated MLGs and long persistence of strains, the LD results suggest a substantial decline in cross-fertilisation over time in the P. falciparum population.

Together, the molecular data on complexity and prevalence of polyclonal infections, repeated MLGs prevalence and LD, suggest declining transmission and increasing inbreeding over time in P. falciparum and, to a lesser extent, in P. vivax. These results are in line with epidemiological surveillance data collected from 2004 to 2009 [1], which also found larger reductions in the incidence of P. falciparum cases (51%), than in P. vivax cases (28%) [1]. These findings highlight the utility of molecular data on complexity or prevalence of polyclonal infections, repeated MLGs prevalence or LD to characterise long-term changes in parasite transmission intensity in regions with comparable endemicity to Mimika [8].

The change in treatment policy to ACT as first-line treatment for all species of malaria, intense vector control, and annual bed net distribution (implemented since 2004), may have all contributed to the epidemiological and genetic changes observed in the P. vivax and P. falciparum populations in Mimika. However, we hypothesise that the implementation of ACT had the greatest impact [23]. Artemisinins clear parasitaemia faster than less potent drugs such as chloroquine and have demonstrated potency against the transmissible gametocyte stages. Therefore, in regions where they remain effective, ACTs have a greater impact in reducing parasite biomass and overall transmission than other conventional drugs [24]. Vector control has been shown to be effective in reducing transmission in Papua New Guinea [25]. However, given the bionomics of the local Anopheles species in Mimika (mostly exophilic behaviour), it is likely that bed-nets may have had little impact on malaria in this population [1, 14].

In addition to the molecular cues of declining transmission intensity, parasite genotyping revealed temporal changes in the frequency of four P. falciparum sub-populations detected in the study. The most notable change was the fluctuation in prevalence of a divergent subpopulation, defined as K2, which was observed in small clusters in 2009 and 2011, before re-emerging and predominating towards the end of the study period (late 2016 and throughout 2017). It remains unclear whether the K2 subpopulation was introduced from elsewhere in Indonesia or overseas, or emerged locally, or whether its recent expansion was neutral or driven by favourable selective pressures from antimalarial drugs or other forces. Cluster-based analyses demonstrated that the K2 sub-population was genetically closely related to isolates from Nusa Tengarra, suggestive of importation from this province or a nearby region. However, as the microsatellite markers may be limited in their ability to determine geographic origin, we cannot exclude the possibility that the divergent infections reflect local adaptations in response to the changing epidemiology. Indeed, the recent expansion of the K2 subpopulation is particularly interesting in the context of a recent genomic study, which revealed closer genetic relatedness between three artemisinin-resistant P. falciparum infections detected in Papua New Guinea with infections derived from Mimika than with other Papua New Guinean isolates [26]. Further temporal investigation of the P. falciparum infections in Mimika are needed with appropriate phenotypic data.

In summary, our study demonstrates that enhanced malaria control activities in a co-endemic setting had a significant impact on the local transmission dynamics of both P. falciparum and P. vivax. The greater genetic changes observed in the P. falciparum population likely reflect a parasite population more susceptible to schizontocidal antimalarial drugs whereas the lesser impact on the P. vivax population emphasises the need for the radical cure of this species. The recent emergence and predominance of a divergent P. falciparum subpopulation highlights the importance of surveillance to inform control programs of potential new threats.

Materials and methods

Ethics statement

Ethical approval for the study was obtained from the Eijkman Institute Research Ethics Commission, Eijkman Institute for Molecular Biology, Jakarta, Indonesia (EIREC-47, EIREC-67, and EIREC-75), the Ethics committee of the National Institute of Health Research and Development, Indonesian Ministry of Health, Jakarta, Indonesia (NIHRD: KS.01.01.6.591, NIHRD: KS.02.01.2.3.4579, NIHRD: KS.02.01.2.1.4042, NIHRD: KS.02.01.2.1.1615 and NIHRD: LB.03.02/KE/4099/2007), and the Human Research Ethics Committee of the Northern Territory (NT) Department of Health & Families and Menzies School of Health Research, Darwin, Australia (MSHR: 02/55, MSHR: 07/06, MSHR: 03/64, MSHR: 05/16, MSHR: 07/14 and HREC 2010–1396).

Study site

The study was conducted in Mimika District, located in the south of Papua Province, Indonesia (S6 Fig). Details on the epidemiology of the study site have been reported previously [1, 15]. Briefly, malaria transmission is perennial in Mimika, but almost exclusive to the lowlands. Most malaria infections are caused by P. falciparum and P. vivax, but P. malariae and P. ovale are also endemic. Papua Province has historically harboured high levels of antimalarial drug resistance in both P. vivax and P. falciparum [27]. Surveys conducted between 2004 and 2006 demonstrated 65% treatment failure against CQ monotherapy at day-28 for vivax malaria and 48% failure against CQ plus sulphadoxine-pyrimethamine (SP) for P. falciparum [14]. Epidemiological surveillance data collected from local health facilities and cross-sectional studies in Mimika District showed an overall decrease of malaria incidence assuming shifts in treatment-seeking behaviour, from 889 infections per 1,000 person-years in 2004–2006 to 522 in 2010–2013 [1]. The incidence of P. falciparum cases fell from 511 per 1,000 person-years in 2004–2006, to 249 in 2010–2013 and, the incidence of P. vivax cases fell from 331 to 239 per 1000 person-years over the same periods [1].

Patient sampling framework

Samples were sourced from patients recruited to clinical and ex vivo surveillance studies carried out in Mimika between 2004 and 2017 [14, 28–32]. The same sampling strategy was applied throughout the study period and ensures a homogenous patient catchment areas and demographics. Briefly, blood samples were collected from consenting, symptomatic patients with uncomplicated malaria attending the Rumah Sakit Mitra Masyarakat (RSMM) hospital. Peripheral parasitaemia and species identity were determined by light microscopy examination of Giemsa-stained blood smears. Available samples were selected for parasite genotyping. Genomic DNA (gDNA) was extracted from either 2 mL of venous blood using the QIAamp DNA Midi Kit (Qiagen), or 100 μL of red blood cell pellet using the QIAamp DNA Mini Kit. Species confirmation was performed using a nested PCR protocol [33].

Microsatellite typing

Nine short tandem repeat (STR) markers (ARAII, PfPK2, poly-alpha, TA1, TA42, TA60, TA81, TA87 and TA109) described by Anderson et al were used to genotype P. falciparum isolates [34]. For P. vivax, a panel comprising eight STR markers (MS1, MS5, MS10, MS12, MS20, MS16, msp1F3, and PV3.27) described by Koepfli et al and Karunaweera et al. were used [35, 36]. The primers and PCR conditions for the assays are described elsewhere [37]. The labelled PCR products were sized on an ABI 3100 Genetic Analyser with GeneScan LIZ-600 size standard (Applied Biosystems). The resulting electrophoretograms were analysed using the online, open-access vivaxGEN platform [38]. All genotypes can be accessed in vivaxGEN. The P. vivax genotypes are available under the batch codes IDPV-XXV, IDPV-TES, IDPV-ACT and IDPV-ACT2, and the P. falciparum genotypes are available under IDPF-XXV, IDPF-TES, IDPF-ACT and IDPF- ACT2. An arbitrary intensity threshold of 100 relative fluorescence units (RFU) and minimal 33% peak intensity of minor relative to predominant peaks was used to reduce background noise/artefacts. Only samples with information in at least 50% of the loci were considered successfully genotyped.

Data analysis

Sample grouping

Samples were grouped according to their collection date into 5 predefined periods: pre-ACT-Policy change (April 2004 to March 2006–24 months); early transition to ACT-Policy (April 2006 to March 2009–36 months); late transition to ACT-Policy implementation (April 2009 to March 2012); early Post-ACT implementation (April 2012 to March 2015–36 months); and late Post-ACT implementation (April 2015 to May 2017–24 months).

Population genetic analysis

Multiple parasite clones were defined if more than one allele at one or more loci was present in an individual sample. The MOI was defined as the maximum number of alleles at any locus for a given sample. The number of MLOCI was also estimated for closer inspection of the complexity of individual infections in the effort to identify any subtle changes in transmission patterns over time.

Temporal analysis of the genetic relatedness between infections was conducted by assessment of the proportion of shared alleles, frequency and duration of repeated MLGs, and the proportion of repeated MLGs per period and illustrated with neighbour-joining trees generated using the ape package in R [39]. Isolates without missing data were used to build MLGs from the predominant allele at each locus. The frequency and temporal duration of repeated MLGs was estimated using the R packages adegenet and RClone [40].

LD, which is the non-random association of alleles at different loci, was measured using the standardised index of association (I). I compares the observed variance of the number of mismatched loci between haplotypes to the expected variance if the loci were randomly associated.[21]. The web-based LIAN 3.5 software was used to calculate the estimates [41]. Briefly, multi-locus LD was compared between groups in search of evidence of increasing LD. Ten thousand permutations of the data was used to assess the significance of the estimates. LD analysis was performed on all MLGs and using low complexity infections (maximum of 1 multi-allelic locus) only.

Population genetic diversity was estimated using the allelic richness (Rs), a measure of the number of alleles at a given locus with normalisation (rarefaction) for sample size. Rs was calculated using the hierfstat package in R [42]. Measures of the expected heterozygosity (HE) were also provided for comparison with previous studies.

Population structure was assessed using STRUCTURE software version 2.3.3 [43]. The simulation was run using 20 replicates, with 100,000 burn-in and 100,000 post burn-in iterations for each estimate of K (number of sub-populations), ranging from 1–10. The model parameters included admixture with correlated allele frequencies. The delta K method was used to derive the most probable K, implemented with STRUCTURE HARVESTER [44, 45]. An arbitrary threshold of 85%> was used to define ancestry to the different K subgroups. Distruct software version 1.1 was used to display the results from STRUCTURE as bar plots [46].

Statistical tests

SPSS software (version 24) was used for statistical analysis. Differences in MOI, percentage of polyclonal infections, proportion of infections with multiply observed MLGs, allelic richness (Rs), and expected heterozygosity (HE) between subgroups and species were assessed using the Mann-Whitney U or Kruskal-Wallis test, spearman correlation for continuous trends and chi-square test for trends and differences in proportion.

Demographic data by temporal period for P. falciparum and P. vivax isolates included in the study versus all cases screened. GM: Geometric mean; 95%CI: 95% Confidence interval; # Superscript indicate number of missing data for Age, Sex and Parasitaemia.

(DOC)

Click here for additional data file.

Marker diversity and genotyping success rate in P. vivax and P. falciparum.

(DOC)

Click here for additional data file.

Demographic data by temporal period for P. vivax and P. falciparum isolates included in the study.

GM: Geometric mean; 95%CI: 95% Confidence interval.

(DOC)

Click here for additional data file.

Ancestry of P. falciparum isolates assuming 2 and 4 sub-populations.

(DOC)

Click here for additional data file.

Flowchart illustrating the sample selection process.

(TIFF)

Click here for additional data file.

Mean multiplicity of Infection (MOI) over time in P. vivax and P. falciparum.

(TIFF)

Click here for additional data file.

Neighbour-joining plots illustrating the genetic diversity in the P. falciparum populations across the five periods.

(TIFF)

Click here for additional data file.

STRUCTURE results in P. vivax and P. falciparum.

Panel a) provides a STRUCTURE bar plot constructed from the P. vivax data at K = 2, illustrating a lack of notable sub-structure. Panel b) provides a scatter plot of K against Delta K for the P. falciparum data, illustrating peaks at K = 2 and K = 4.

(TIFF)

Click here for additional data file.

Multiple Correspondence Analysis plot illustrating the relatedness between the Papuan P. falciparum subpopulations (K1 and K2) and other Indonesian Islands.

Higher genetic relatedness was observed between the putatively imported Papuan asymptomatic K1 subpopulation (Papua Asymp K1, green circles) [15], the Papuan symptomatic K2 subpopulation (Papua Symp K2, red circles), and the infections from Nusa Tenggara Timur (aquamarine circles)[16] than the other Papuan symptomatic infections from the current study (Papua Symp Other, pink circles).

(TIFF)

Click here for additional data file.

Map illustrating the location of the study site.

Map adapted from Kenangalem et al. 2019 illustrating the location of Mimika District within Papua Province, Indonesia.

(TIFF)

Click here for additional data file.

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

5 Feb 2020

Dear Dr Auburn,

Thank you very much for submitting your manuscript "Longitudinal molecular surveillance confirms interruption of P. vivax and P. falciparum transmission following implementation of a universal policy of Artemisinin-based Combination Therapy in Papua, Indonesia" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Gregory Deye

Guest Editor

PLOS Neglected Tropical Diseases

Walderez Dutra

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Methods are mostly adequate for the aims of the study. I note, however, that a population bottleneck is mentioned (e.g., Author Summary and Discussion, line 292) but no formal test for bottleneck was applied to the samples. A LD test was correctly used, but described in a rather weird way. In fact, the standardised index of association implemented in LIAN software does not "compare the observed variance in the numbers of alleles shared between parasites with that expected when parasites share no alleles at different loci", as stated in lines 393-395. In fact, the index compares the observed variance of the number of alleles at which each pair of haplotypes differ in the population (i.e., the allele mismatch distribution) with the variance expected under random association of alleles. This is very important, because parasites may share identical alleles in a panmictic population -- therefore, the null hypothesis does not imply "no alleles shared"!

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Overall, results are well presented in the main text. Tables are clear and serve the purpose of presenting the main findings.

Figure 1 is ok, but may be misleading; a pie chart would possibly be more adequate for the purpose of showing the, among multiple-clone infections, the proportion of those with 1 or more alleles found at a single or a few loci tends to increase with time. The bar chart may give the impression of an increasing overall prevalence of multiple-clone infections, which is clearly not true.

Figure 2 is not very effective in presenting the results. A new figure inspired in Figures 1 and 3 of reference 9, for example, can be a better option. The point here is to show that particular haplotypes shared by two or more isolates persist over time.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Most conclusions are supported by the data, but a few important exceptions must be mentioned. First, the title: "Longitudinal molecular surveillance confirms interruption ...". Overall, the article is nicely written, the sample size is large enough for the proposed analysis, results are clearly presented... but the title is completely misleading. Data is this paper does not "confirm" that malaria transmission has been interrupted in Papua! Such a "confirmation" would require evidence that all remaining cases are imported, not locally transmitted, and no data support this! I would suggest that the data are consistent with a decrease in malaria transmission (mostly that of P. falciparum) following the implementation of the universal ACT policy. That is all.

What is consistent with transmission decline? Essentially, the decrease in the prevalence of multiple-clone infections and in average multiplicity of infection over time. No change in genetic diversity was observed during the study period. This is exactly what the study in reference 17 has shown along the Thai-Myanmar border. Interestingly, no change in genetic diversity of parasites (and no evidence of bottleneck) was found in another setting, Sri Lanka, with a much more drastic decrease in malaria transmission (doi: 10.1017/S0031182013002278, not cited in the text).

Is increased LD consistent with decreased transmission. Not necessarily. LD is a consequence of reduced recombination and, of course, any factor reducing recombination will affect LD estimates. For example, malaria incidence may increase due to a clonal outbreak -- with increased transmission and increased LD. Therefore, it is important to document changes in LD over time, but they do not "confirm" that malaria transmission has declined.

I have previously mentioned that the study does not provide formal evidence for a population bottleneck in parasite populations. (I would guess that, even if properly tested, the results would still be negative.) However, even if the authors found evidence for bottleneck, this does not necessarily "confirm" that transmission has been interrupted or even decreased. A selective sweep induced by the widespread of a new drug, for example, might lead to genetic changes at the population level that are consistent with a bottleneck, even if transmission levels have not been greatly affected.

Finally, there is throughout the paper a relatively loose use of the term "structure". If there is significant LD, of course malaria parasites are structured into lineages or subpopulations that do not recombine as much as expected for a randomly mating population. Therefore, stating that "there was no population structure among the P. vivax isolates (line 207) is simply incorrect. One can cautiously say that the Bayesian clustering algorithm implemented in STRUCTURE software was unable to detect population structure in the sample analysed. Whether STRUCTURE is the best strategy for detecting "structure" in populations at LD is debatable (see the STRUCTURE manual for a nice discussion on this topic).

The most likely cause of LD is the reduced proportion of multiple-clone infections documented during the study period, although it is biologically not true that "cross-fertilisation is only possible in mixed-clone infections" (as stated in line 280). Superinfection in the mosquito (due, e.g., to interrupted feeding) may also allow for cross-fertilisation. However, other factors may favour LD -- for example, if patient recruitment strategies have changed over time given the declining number of available patients, samples in more recent years may have been collected from a more (e.g., geographically) heterogeneous population where panmixia would be much less likely. These limitations must be recognised instead of overinterpreting LD results.

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: Overall, the paper is nicely written and describes important findings that are surely interesting to the broad audience of PLoS NTD. A better discussion of the data and, more specifically, their implications for malaria epidemiology, would render the paper even more interesting.

--------------------

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see https://journals.plos.org/plosntds/s/submission-guidelines#loc-methods

6 Feb 2020

Submitted filename: PrePost_ACT_Revision_Cover_Letter_23012020.pdf

Click here for additional data file.

3 Apr 2020

Dear Dr Auburn,

Thank you very much for submitting your manuscript "Longitudinal molecular surveillance confirms reduction of P. vivax and P. falciparum transmission following implementation of a universal policy of Artemisinin-based Combination Therapy in Papua, Indonesia" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Gregory Deye

Guest Editor

PLOS Neglected Tropical Diseases

Walderez Dutra

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #2: (No Response)

Reviewer #3: The objectives of the study, to measure changes over time in population parameters in Plasmodium falciparum and Plasmodium vivax that may reflect changes in transmission, and to relate these to the introduction of artemisinin-based Combination Therapy (ACT) in Papua New Guinea, is testable and clearly stated. Statistical analyses used are appropriate. However, it’s not clear to me that the population bottleneck analyses add much. The comprehensive longitudinal sampling over 14 years of approximately 600 samples for each species is sufficient to address the hypothesis being tested.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #2: (No Response)

Reviewer #3: The results are clearly presented although Table 1 would be more appropriate as a Supplementary Table with a consolidated summary presented in the text. In Figure 3 the various colors are too similar to each other.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #2: (No Response)

Reviewer #3: The conclusions are in large part supported by the data. However, although the rationale for assuming a bottleneck may have occurred due to the introduction of a more effective drug therapy is reasonable, the analyses do not bear it out. Depending on the model used in the Bottleneck software either several bottlenecks are detected or none. By the authors own admission, the test of excess heterozygosity may not be sensitive enough to detect subtle changes. I would recommend removing bottleneck analyses altogether or using a more sensitive test if available.

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #2: According to the journal policies, shouldn’t ‘Plasmodium’ be spelled out in the title?

The authors state that all data is available, and I assume it is saved in VivaxGEN. Please clarify.

Reviewer #3: (No Response)

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #2: Main comment:

The authors argue that “Genetic epidemiology is gaining widespread interest as a tool that can enhance conventional malaria surveillance”, as obtaining classical metrics such as case numbers can be difficult. While I certainly agree, I am puzzled that the authors do make attempts to compare their genotyping data to incidence or prevalence data. They give some incidence data in the methods section (though these numbers appear not to sum up, see comment below). I imagine that for all periods of sampling test positivity rate or incidence for both species was collected. A figure comparing such number to genotyping indices would provide information on the additional benefits of genotyping.

Minor comments:

1) The title could be more informative, e.g. by changing to

“Surveillance over 14 years confirms reduction of P. vivax and P. falciparum transmission following implementation of a universal policy of Artemisinin-based Combination Therapy in Papua, Indonesia”

“Longitudinal” is a broad term, and, for example, is often used for cohort studies of much shorter duration.

2) Line 49-50: The higher level of outbreeding has already been mentioned above in lines 42-43. No need to repeat it in the abstract.

3) Line 175: Does the word ‘bottlenecking’ really exist?

4) Instead of the term ‘Multi-locus genotype’, ‘haplotype’ is often used. Following that, I am not sure whether the term ‘moMLG’ should be introduced to the field of molecular epidemiology. ’Repeated haplotype’ would be an easier term.

5) Lines 263-265: It is correct that a reduction in complexity of infections has been proposed as a marker of declining transmission. Indeed, the study cited by Nkhoma et al found such a pattern, in a setting where the reduction in transmission was very pronounced (1-fold). However, other studies (from Africa, the South Pacific, and elsewhere) have found complexity to change slowly. Taken all available data into account, I would argue that high complexity even when transmission is reduced is a common pattern.

6) Lines 281-283: It could be mentioned that high Pv diversity was not only found during ‘declining transmission’, but essentially to the point of elimination. It appears low Pv diversity is not a prerequisite for elimination.

7) Lines 391-194: “an overall decrease of malaria incidence, from 406 infections per 1,000 person-years in 2004-2006 to 351 in 2010-2013 [1]. The incidence of P. falciparum cases fell from 511 per 1,000 person-years in 2004-2006, to 141 per 1,000 in 2010-2013 and, the incidence of P. vivax cases fell from 331 to 146 per 1000 person-years over the same periods [1].”

How can the overall decrease (which is the sum of Pf and Pv) be around 15%, when incidence of Pf fell 3-fold and incidence of Pv fell 2-fold? This seems impossible. The overall change should be some kind of average. Pf incidence alone is higher in 2004-2006 than overall incidence!

8) Discussion: In a previous study (reference 15) the authors have identified the K1 and K2 P. falciparum populations, and suggested importation along with other possibilities as explanation. I am surprised the current manuscript does not make any references to these hypotheses. Given their additional data, can the authors confirm or reject some the their previous hypotheses?

Reviewer #3: The authors are able to measure a number of alterations in population parameters for P. falciparum and P. vivax: a reduction in both the proportion and complexity of polyclonal infections, an increase in the proportion of moMLG in P. falciparum, sub-population structure in P. falciparum but not P. vivax, and no reduction in diversity for either. The results are of interest and have public health relevance as they highlight the differential reduction in transmission for the two species in a co-endemic setting.

--------------------

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosntds/s/submission-guidelines#loc-materials-and-methods

15 Apr 2020

Dear Dr Auburn,

We are pleased to inform you that your manuscript 'Molecular surveillance over 14 years confirms reduction of Plasmodium vivax and falciparum transmission after implementation of Artemisinin-based Combination Therapy in Papua, Indonesia' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Gregory Deye

Guest Editor

PLOS Neglected Tropical Diseases

Walderez Dutra

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

28 Apr 2020

Dear Dr Auburn,

We are delighted to inform you that your manuscript, "Molecular surveillance over 14 years confirms reduction of Plasmodium vivax and falciparum transmission after implementation of Artemisinin-based Combination Therapy in Papua, Indonesia," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Serap Aksoy

Editor-in-Chief

PLOS Neglected Tropical Diseases

Shaden Kamhawi

Editor-in-Chief

PLOS Neglected Tropical Diseases