Malaria is caused by five different species of parasites that are transmitted to humans by bites from parasite-carrying mosquitos. Once in human blood, the parasites rapidly multiply. People who live in countries where malaria is common may become infected and never show any symptoms because their immune systems are able to keep parasite numbers low. Repeated infections, or infection with more than one species of malaria parasite also are common. Some species of malaria, including Plasmodium vivax, can hibernate in the liver for weeks or months after the infection and only become active later.

Asymptomatic infections, multi-parasite infections, and reactivating parasites make it hard to measure how often new malaria infections occur. One way scientists can determine if a new infection has occurred is by genotyping the parasites in a person’s blood. Genotyping involves looking for small differences in the parasite DNA. For example, a study in Papua New Guinea, where P. vivax is very common, showed that reactivations of hibernating parasites were more common than new infections.

Now, Hofmann et al. use the same study in Papua New Guinea to compare the frequency and consequences of new infections with P. vivax and another malaria parasite, Plasmodium falciparum. In the study, 466 children from 6 villages were followed for 8 months with tests every 2 to 4 weeks to genotype the parasites in their blood. Some of the children were treated with antimalarial drugs to help wipe out any existing parasites including hibernating ones. While P. vivax was about twice as common in blood samples—likely due to reactivation—genotyping showed that new infections with the two parasites occur at equal rates and often at the same times and locations.

Hofmann et al. also showed that some villages and some children had much higher rates of infection than others. This difference could not fully be explained by use of bednets or other preventive measures. Children were more likely to become ill from P. falciparum than P. vivax even though P. vivax was more common. But children with more frequent infections with P. falciparum seemed better able to manage the parasites and were less likely to develop symptoms that those with infrequent infections. The experiments show that genotyping may help scientists better track new malaria infections and develop better strategies to prevent or treat malaria.

Renewed emphasis on malaria control has resulted in substantial reductions in overall malaria prevalence and incidence in many endemic countries (World Health Organization, 2015). However, where transmission persists, it is highly heterogeneous even on small spatial scales (Bousema et al., 2012). Individual exposure is further influenced by factors such as use of bednets, attractiveness to mosquitoes, or behavioural differences. In Papua New Guinea (PNG), malaria prevalence has sharply declined in the last decade, largely as a result of two nationwide distributions of long-lasting insecticide treated bednets (LLIN) (Hetzel et al., 2015; Hetzel et al., 2014; Hetzel et al., 2012). P. vivax and P. falciparum PCR-prevalence in the general population was reduced from 32% and 39% in 2006 to 13% and 18% in 2010 (Koepfli et al., 2015). Already before this decline in malaria prevalence, studies in PNG had reported significant heterogeneity in malaria transmission attributed to local population structure and geographical diversity (Hetzel et al., 2015; Cattani et al., 1986; Genton et al., 1995; Müller et al., 2003; Mueller et al., 2009a).

Prior to the up-scaling of malaria control, P. vivax endemicity in PNG was among the highest worldwide (Hetzel et al., 2015). Clinical immunity to P. vivax was acquired very rapidly in PNG children, and the incidence of P. vivax clinical episodes peaked in children younger than two years with only very few P. vivax clinical episodes reported in children older than 5 years or adults (Genton et al., 2008; Michon et al., 2007; Lin et al., 2010; Betuela et al., 2012). In contrast, the risk for uncomplicated P. falciparum clinical episodes increased during early childhood (Lin et al., 2010) and significant reductions in incidence of clinical episodes or high-density infections were only observed in children aged 5 years and older (Michon et al., 2007). Compared to the incidence of clinical malaria, prevalence of P. falciparum and P. vivax peaked in older age groups, with asymptomatic infections remaining common until adulthood in PNG (Koepfli et al., 2015; Mueller et al., 2009a). Concordant with the species-specific pattern in the burden of clinical episodes, P. vivax prevalence peaked in younger age groups than P. falciparum prevalence (Koepfli et al., 2015; Mueller et al., 2009a).

As malaria transmission declines, it is important to understand the resulting changes in malaria prevalence and clinical incidence patterns, as well as the extent of heterogeneity in transmission within malaria endemic regions so that high-risk areas can be identified and targeted (Mosha et al., 2014). Most attempts to delineate high and low transmission areas made to-date, by both researchers and control programs, have used passive case surveillance or cross-sectional malaria indicator surveys. These surveillance strategies result in clinical incidence and prevalence estimates, both of which are surrogate markers for transmission. A more accurate understanding of the relationship between exposure to new infections and malaria prevalence or clinical incidence is needed to determine how accurately these surrogate markers represent heterogeneity in transmission at local scales. In addition, quantifying clinical incidence in relation to exposure to blood-stage infections can increase our insight into the development and maintenance of immunity to malaria in a setting of sustained malaria control (Battle et al., 2015; Cameron et al., 2015).

The molecular force of blood-stage infection (_molFOB) describes the number of new genotypes observed in consecutive blood samples from cohort participants over time (Mueller et al., 2012; Koepfli et al., 2013). Genotyping of highly polymorphic markers detects superinfecting parasite clones in asymptomatic (but parasitaemic) or symptomatic individuals. _molFOB thus provides a longitudinal, individual and quantitative measure for exposure to new blood-stage malaria infections (Mueller et al., 2012; Koepfli et al., 2013). For P. falciparum, _molFOB is closely linked to the number of infective mosquito bites and therefore is a direct proxy for the actual force of infection (FOI) and thus for transmission in endemic settings (Smith et al., 2010). For P. vivax, clones appearing in the blood-stream can either originate directly from an infective mosquito bite or from a relapsing liver hypnozoite (Koepfli et al., 2013). For P. vivax, _molFOB is thus a compound measure of exposure to newly acquired infections from mosquito bites and relapsing blood-stage infections.

The usefulness of _molFOB as a surrogate marker of individual exposure was validated originally in a cohort of young PNG children 1–4 years of age, in which species-specific _molFOB was the most important predictor of clinical incidence for both species (Mueller et al., 2012; Koepfli et al., 2013). Although some spatial heterogeneity of transmission was observed in that study for both species, due to the high level of transmission the species-specific difference in rate of immune acquisition was the predominant feature in that study. While P. vivax _molFOB (Pv-_molFOB) did not change with age, the incidence of P. vivax clinical episodes decreased significantly with age, with a faster rate of decrease in children with high Pv-_molFOB [12]. P. falciparum _molFOB (Pf-_molFOB) in that cohort was lower compared to Pv-_molFOB and the incidence of P. falciparum clinical episodes increased in parallel with an increasing Pf-_molFOB in children 1–3 years, reaching a plateau thereafter (Lin et al., 2010; Mueller et al., 2012). These earlier results indicated that (i) immunity to P. vivax is acquired more rapidly in children with higher cumulative exposure, that (ii) this developing immunity led to proportionally fewer clinical P. vivax episodes in older children despite similar exposure to new P. vivax blood-stage infections, and that (iii) higher exposure to P. vivax blood-stage infections, compared to P. falciparum, resulted in a more advanced immunity to P. vivax in this age group compared to P. falciparum (Doolan et al., 2009; Longley et al., 2016). The major challenge to these cross-species comparisons lies within the intrinsic differences of Pf- and Pv-_molFOB: whereas Pf-_molFOB is a direct marker of mosquito-borne transmission, Pv-_molFOB is a composite measure reflecting both newly acquired infections and those caused by relapses of previously acquired infections.

In this study, we extend the analysis of _molFOB’s relationship with incidence of clinical malaria episodes to older PNG children and a lower transmission scenario. In addition, given the unique study design that randomized blood-stage only or blood- plus liver-stage treatment at enrolment (Robinson et al., 2015), we are now, for the first time, able to compare the incidence of newly acquired P. falciparum infections with both the incidence of newly acquired P. vivax infections and relapsing P. vivax infections. Advancing on previous studies that investigated each species individually (Mueller et al., 2012; Koepfli et al., 2013), we now provide a comparative analysis of P. falciparum and P. vivax, directly exploring the role of exposure to multiple Plasmodium species in the development of clinical immunity to P. falciparum and P. vivax malaria. We quantify in detail the extent of heterogeneity in _molFOB on a small geographical scale and relate this to heterogeneity in clinical episode incidence to investigate effects of small-scale variation in malaria transmission on local malaria epidemiology. By combining in-depth molecular parasitological data with demographic and clinical data, this study thus provides detailed insights into the changing epidemiology of malaria in PNG in response to intense malaria control efforts.

In the present study, we describe striking heterogeneity in malaria transmission not only between closely neighboring communities in Maprik district, PNG, but also substantial differences in exposure between individual children from the same village. On village level this heterogeneity is apparent both when using traditional markers such as prevalence of infection, as well as when using the novel `reference standard` marker of individual exposure _molFOB. The increased resolution provided by _molFOB further allows quantifying heterogeneity in exposure to new blood-stage infections between individual children. Extending an earlier study in a neighboring area in which younger children had been enrolled, and that had identified _molFOB as the most important predictor of malaria clinical episodes (Mueller et al., 2012; Koepfli et al., 2013), we confirmed that _molFOB remains significantly associated with the risk for clinical episodes, but other factors such as age (P. vivax), or a mixed Pf/Pv infection and village factors not captured by any of the other parameters assessed (P. falciparum) have a stronger effect on the risk for clinical malaria (Table 5 and 6).

Malaria transmission is often estimated by investigating the more accessible human host rather than the mosquito vector (Tusting et al., 2014). Because P. falciparum blood-stage infections are a direct outcome of mosquito-to-human transmission, infection parameters assessed in the human blood closely reflect P. falciparum transmission. In contrast, relapses arising from dormant hypnozoites contribute substantially to P. vivax blood-stage infections (Robinson et al., 2015), thus complicating the assessment of mosquito-to-human P. vivax transmission via infection parameters measured in the human blood. The unique design of this study, that combined clearance of hypnozoites in half of the study participants with subsequent measurement of Pv-_molFOB, allowed us to identify the burden of P. vivax infections due to mosquito-to-human transmission (in hypnozoite-cleared individuals) and compare it to the total burden of P. vivax infections. We found a highly similar incidence and comparable temporal and spatial heterogeneity of P. falciparum and P. vivax infection acquired through renewed exposure to infected mosquito bites. In children that experienced the full burden of relapses we found two-fold higher P. vivax infection prevalence and 4-times higher incidence (_molFOB) compared to P. falciparum. This first quantitative comparative assessment of P. falciparum and P. vivax transmission using non-entomological molecular parameters thus indicates that the observed differences in epidemiology between the two species are largely due to the high burden of relapsing P. vivax blood-stage infections (Robinson et al., 2015). Our molecular results thus support recent entomological data from Dreikikir district, 50 km from Maprik in East Sepik Province (Reimer et al., 2016) as well as earlier studies in East Sepik (Hii et al., 2001) that found similar sporozoite rates for P. falciparum and P. vivax.

Our previous analysis of this cohort had investigated the contribution of the hypnozoite reservoir to P. vivax infection and disease in order to inform strategies for achieving a sustained reduction of the P. vivax burden in PNG (Robinson et al., 2015). Here, we now describe the post-treatment re-infection dynamics in higher temporal detail. Through a detailed comparison of these patterns for P. vivax and P. falciparum in PQ and placebo-treated children we further elucidate the contribution of relapses to P. vivax prevalence and clinical incidence, which are the most commonly used parameters for planning and monitoring of malaria control strategies. P. vivax relapses accounted for more than half of the observed P. vivax prevalence in this cohort, which is lower than what was previously estimated as the contribution of relapses towards Pv-_molFOB by comparison of treatment arms (77%, Robinson et al., 2015). This difference can be accounted for by the higher number of P. vivax multiple clone infections that will accumulate more rapidly in the placebo-arm, where additional parasite clones from relapses and/or new infections may overlap with without a corresponding change in overall prevalence.

While we previously described a sustained effect of PQ treatment with significant reductions in Pv-_molFOB observed up to eight months post treatment (Robinson et al., 2015), here, we describe temporal variation in relapse rate with a rapid and wave-like recurrence of P. vivax in children from the placebo arm, who had retained their hypnozoites (Figure 1A). The concurrent, modest peaks in Pf-_molFOB and Pv-_molFOB in the PQ arm represent seasonal variation in transmission, which is highest in December and January in the study area (Mueller et al., 2012); corresponding to weeks 8–14 of follow-up). A much higher peak and subsequent drop in appearance of new clones within three months after blood-stage only treatment, which was mirrored by a corresponding peak and drop in P. vivax prevalence (Figure 1B andC), suggests that the incidence of relapse infections in the blood was not constant during follow-up. P. vivax infections are often observed following treatment of P. falciparum malaria (Douglas et al., 2011), and it can thus been hypothesized that the frequency of relapses may be temporarily increased after blood-stage antimalarial treatment (White and Imwong, 2012). It is thus conceivable that the blood-stage antimalarial at baseline either triggered P. vivax relapses directly, or indirectly by allowing more hypnozoites to establish blood-stage infections in parasite-free hosts. Alternatively, blood-stage P. vivax infections from hypnozoites relapsing shortly after baseline treatment (during a period when antimalarial drugs were present at sub-curative levels) may be suppressed to sub-detectable densities until complete waning of drug levels, resulting in simultaneous proliferation and detection of many new blood-stage clones within the first weeks after treatment (Douglas et al., 2011; Tarning et al., 2014). More detailed modeling of the dynamics of individual P. vivax blood-stage infections and their association with potential triggers such as treatment or febrile illness will be required to determine the existence and importance of proposed relapse-triggers.

Malaria transmission showed high micro-spatial heterogeneity with more than 10-fold differences in Pf- and Pv-_molFOB (in the PQ arm) between villages despite an overall high LLIN use by the study participants (during follow-up; village average use,>90%; individual use,>50%). Individual LLIN use at enrolment was nevertheless associated with a reduced risk of recurrent P. falciparum and P. vivax in univariate analyses. However, after adjustment for other related variables (i.e., village of residence or infection status) this association became non-significant. Children living in Bolumita, where both P. falciparum and P. vivax _molFOB and prevalence were highest, had a modestly lower LLIN use (mean during follow-up, 92%; at enrolment, 77%) compared to children from other villages (mean during follow-up, 97–100%; at enrolment, 91–100%). It is conceivable that LLIN use in the Bolumita community may be less effective in reducing malaria transmission (Killeen et al., 2007; Smith et al., 2009). Potential differences between villages in mosquito density, behavior, sporozoite rate, proximity of house or play areas to mosquito breeding sites, or human behavioral factors (related to LLIN use or other risk factors) are however likely to be more important determinants for exposure to infective bites. Small-scale variations in vector species and distribution between and within villages in PNG have been described previously (Cattani et al., 1986; Reimer et al., 2016; Charlwood et al., 1986; Hii et al., 1997; Burkot et al., 1988) and likely account in a large part for the micro-geographic heterogeneity in malariological parameters observed in this and other studies.

Assessing the incidence of new infections from consecutive blood samples using molecular methods (as is necessary to determine _molFOB), is complicated by fluctuating densities of clonal parasitemia that may temporarily fall below the limit of detection of the genotyping PCR, leading to imperfect detectability of clones (Bretscher et al., 2010; Felger et al., 2012; Koepfli et al., 2011). For P. falciparum, periodical sequestration of clones and absence from the peripheral blood at time of sampling may further contribute to imperfect detectability. For P. vivax, generally low parasite densities aggravate the problem of imperfect detectability, and dis- and re-appearance of clones may be a result of imperfect detectability or relapsing hypnozoites. The overall estimates of _molFOB presented here may thus be biased. Accurately assessing the effects of this imperfect detectability on parameters estimated from longitudinal genotyping data, such as _molFOB, requires complex mathematical modeling (Bretscher et al., 2010; Felger et al., 2012; Sama et al., 2005; Sama et al., 2006). However, although clonal detectability has been shown to decrease with age (Felger et al., 2012; Sama et al., 2006) and MOI (Koepfli et al., 2011), it is unlikely to vary substantially within our cohort’s age range and transmission setting. Hence the observed differences in _molFOB are likely to accurately reflect the relative differences in individual exposure as well as in population transmission levels within the study area.

Evaluating the impact of malaria control efforts requires monitoring changes in malariological metrics over extended periods of time. Drawing comparisons between studies performed at different times in different age groups is particularly challenging because of the interplay of past and current exposure to infective bites and the resulting anti-malarial immunity in the study population of a certain age. In our cohort, fewer P. vivax clinical episodes than P. falciparum clinical episodes were detected despite a higher incidence of P. vivax blood-stage infections, which is consistent with earlier studies in children of similar age (Michon et al., 2007). The very low incidence of clinical P. vivax episodes in our cohort, at 0.16 clinical episodes/year (placebo arm [Robinson et al., 2015]), contrasts drastically with that of 2.46 P. vivax clinical episodes/year observed in an earlier observational cohort of younger children aged 1–4 years from the same area (Lin et al., 2010). The 3-fold difference in Pv-_molFOB between the two cohorts seems modest when compared to the 15-fold difference in the incidence of clinical Pv episodes (Koepfli et al., 2013). This suggests that the much lower incidence of P. vivax clinical illness in 5–10 years old children of this study is more likely explained by an advanced state of immunity to P. vivax compared to the younger children of the earlier cohort than the drop in P. vivax transmission. Consistently, age emerged as the strongest factor associated with protection against P. vivax clinical episodes, Pv-_molFOB and P. vivax parasite density. Like in the previous cohort of younger children from neighboring villages (Lin et al., 2010) the incidence clinical P. vivax clinical episodes dropped significantly with age. Unlike in the previous cohort of younger children (Koepfli et al., 2013), in this cohort we additionally observed a drop in Pv-_molFOB as well as P. vivax densities with age (Table 4 and Supplementary file 3). This is a further indication of the substantial clinical immunity to P. vivax acquired during years of past exposure in the children of this study, which is still ongoing after the age of five.

In sharp contrast to the age-dependent decline of P. vivax clinical episode incidence, no age-dependent decrease in the incidence of clinical episodes was observed for P. falciparum. In a previous cohort study conducted in 2004 in 5–14 year old children in an area from PNG with substantially higher transmission levels (mean incidence risk 5.0 versus 0.8 infections/year, Michon et al., 2007; Robinson et al., 2015), the risk of moderate- to high-density P. falciparum infections decreased significantly with age (Michon et al., 2007). Clinical immunity to P. falciparum in children of this earlier cohort was not only significantly further advanced compared to children of this cohort, but in addition, transmission was more homogeneous in the area of that study. As a consequence age was a much better marker of life-time exposure and thus immune status compared to the present cohort.

In this cohort, exposure to P. falciparum infections was highly heterogeneous between study participants. Mathematical modeling suggests that at heterogeneous transmission, changes of parasite prevalence and clinical episode incidence with age are less pronounced compared to settings with homogeneous transmission (Ross and Smith, 2010). Although no age trends were observed for P. falciparum in this cohort, when children were stratified into groups ranging from low to high exposure we found that the proportion of P. falciparum clinical episodes relative to new infections decreased with increasing exposure. This could either reflect the development of clinical immunity in highly exposed children, or premunition, a proposed mechanism by which established infections help to control superinfections by immunological cross-protection (Sergent and Parrot, 1935; Smith et al., 1999). In settings of decreasing and heterogeneous transmission, age alone may therefore not be a suitable marker of immunity to P. falciparum. Instead, combining age and _molFOB to estimate cumulative life-time exposure may provide a more accurate surrogate measure of the extent of acquired clinical immunity. With P. falciparum transmission declining in PNG due to successful malaria control strategies (Koepfli et al., 2015), it is conceivable that immunity against P. falciparum will develop more slowly, shifting the burden of disease towards older age groups or towards more complex, non-linear age patterns. This delay in immune acquisition is however more than compensated by the overall much lower incidence of clinical malaria clinical episodes: in cohort studies in children younger than 4 years from Maprik district, clinical P. falciparum incidence had dropped from 2.56 clinical episodes/year before (observational cohort, [Lin et al., 2010; Mueller et al., 2012]) to 0.67 clinical episodes/year immediately after the free LLIN distribution campaign (placebo arm, [Betuela et al., 2012]).

Finally, given that four Plasmodium species co-exist in PNG, there has long been considerable interest in potential mechanisms of cross-species immunity and mixed species interactions (Mueller et al., 2009a; Bruce et al., 2000; Smith et al., 2001; Mehlotra et al., 2000). However, there is so far no consistent evidence for the presence or absence of cross-protection among Plasmodium species. P. vivax and P. falciparum infections in our study were concentrated in the same children and villages (Figure 3), likely due to overlapping focal transmission for P. falciparum and P. vivax and thus potentially high co-infection rates in mosquitoes. Contrary to an earlier cohort in younger PNG children that found a decreased risk of P. falciparum clinical episodes after PQ radical cure (Betuela et al., 2012), we found indications for an increased risk of P. falciparum illness after clearance of P. vivax hypnozoites using PQ. Our data suggests that in individuals with substantial clinical immunity against P. vivax, a concurrent P. vivax infection may provide protection against P. falciparum clinical episodes by limiting P. falciparum densities (Table 6, Supplementary file 3). However, the comparably small number of clinical episodes in this and the earlier contrasting study does not allow an in-depth analysis of causal relationships and therefore does not allow firm conclusions on the potential effects and mechanisms of cross-species interactions in mixed infections.

In conclusion, this study provides detailed insight into the changing epidemiology of malaria in PNG children under sustained malaria control, by using _molFOB as a powerful measure to quantitatively investigate patterns of new mosquito-derived P. falciparum and P. vivax infections versus those for P. vivax relapsing infections, as well as spatial and age trends in exposure to these infections. Striking heterogeneity in malaria transmission between villages as well as in individual exposure to new P. falciparum and P. vivax infections persisted in our study area despite very high use of LLINs. This presents a significant challenge for on-going malaria control efforts. The comparable patterns of new mosquito-derived P. falciparum and P. vivax infections indicate that sustained use of LLINs does result in a comparable reduction in transmission of both species. The higher incidence and prevalence of P. vivax infections observed in our data is thus directly linked to its ability to cause relapsing infections, highlighting the crucial role of hypnozoites for P. vivax epidemiology and the need to effectively intervene against these hidden stages. Together, these insights provide a crucial link to evaluate the level of P. vivax mosquito-based transmission against that of P. falciparum and serve to calibrate other standard malaria indicators such as parasite prevalence or incidence of clinical episodes and to ultimately inform new approaches to surveillance and response systems.

1. 1. Natalie E Hofmann
   2. Stephan Karl
   3. Rahel Wampfler
   4. Benson Kiniboro
   5. Albina Teliki
   6. Jonah Iga
   7. Andreea Waltmann
   8. Inoni Betuela
   9. Ingrid Felger
   10. Leanne J Robinson
   11. Ivo Mueller

   (2017) Data from: The complex relationship of exposure to new Plasmodium infections and incidence of clinical malaria in Papua New Guinea

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

   http://dx.doi.org/10.5061/dryad.f9154

1. 1. Robinson LJ
   2. Wampfler R
   3. Betuela I
   4. Karl S
   5. White MT
   6. Li Wai Suen CSN
   7. Hofmann NE
   8. Kiniboro B
   9. Waltmann A
   10. Brewster J
   11. Lorry L
   12. Tarongka N
   13. Samol L
   14. Silkey M
   15. Bassat Q
   16. Siba PM
   17. Schofield L
   18. Felger I
   19. Mueller I

   (2015) Data from: Strategies for understanding and reducing the Plasmodium vivax and Plasmodium ovale hypnozoite reservoir in Papua New Guinean children: a randomised placebo-controlled trial and mathematical model

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

   http://dx.doi.org/10.5061/dryad.m1n03

1. 1. Battle KE
   2. Cameron E
   3. Guerra CA
   4. Golding N
   5. Duda KA
   6. Howes RE
   7. Elyazar IR
   8. Price RN
   9. Baird JK
   10. Reiner RC
   11. Smith DL
   12. Gething PW
   13. Hay SI

   (2015) Defining the relationship between plasmodium vivax parasite rate and clinical disease

   Malaria Journal 14:191.

   https://doi.org/10.1186/s12936-015-0706-3

   • PubMed
   • Google Scholar
2. 1. Betuela I
   2. Rosanas-Urgell A
   3. Kiniboro B
   4. Stanisic DI
   5. Samol L
   6. de Lazzari E
   7. Del Portillo HA
   8. Siba P
   9. Alonso PL
   10. Bassat Q
   11. Mueller I

   (2012) Relapses contribute significantly to the risk of plasmodium vivax infection and disease in papua new guinean children 1-5 years of age

   The Journal of Infectious Diseases 206:1771–1780.

   https://doi.org/10.1093/infdis/jis580

   • PubMed
   • Google Scholar
3. 1. Bousema T
   2. Griffin JT
   3. Sauerwein RW
   4. Smith DL
   5. Churcher TS
   6. Takken W
   7. Ghani A
   8. Drakeley C
   9. Gosling R

   (2012) Hitting hotspots: spatial targeting of malaria for control and elimination

   PLoS Medicine 9:e1001165.

   https://doi.org/10.1371/journal.pmed.1001165

   • PubMed
   • Google Scholar
4. 1. Bretscher MT
   2. Valsangiacomo F
   3. Owusu-Agyei S
   4. Penny MA
   5. Felger I
   6. Smith T

   (2010) Detectability of Plasmodium falciparum clones

   Malaria Journal 9:234.

   https://doi.org/10.1186/1475-2875-9-234

   • PubMed
   • Google Scholar
5. 1. Bruce MC
   2. Donnelly CA
   3. Packer M
   4. Lagog M
   5. Gibson N
   6. Narara A
   7. Walliker D
   8. Alpers MP
   9. Day KP

   (2000) Age- and species-specific duration of infection in asymptomatic malaria infections in Papua New Guinea

   Parasitology 121:247–256.

   https://doi.org/10.1017/S0031182099006344

   • PubMed
   • Google Scholar
6. 1. Burkot TR
   2. Graves PM
   3. Paru R
   4. Wirtz RA
   5. Heywood PF

   (1988) Human malaria transmission studies in the Anopheles punctulatus complex in Papua New Guinea: sporozoite rates, inoculation rates, and sporozoite densities

   The American Journal of Tropical Medicine and Hygiene 39:135–144.

   https://doi.org/10.4269/ajtmh.1988.39.135

   • PubMed
   • Google Scholar
7. 1. Cameron E
   2. Battle KE
   3. Bhatt S
   4. Weiss DJ
   5. Bisanzio D
   6. Mappin B
   7. Dalrymple U
   8. Hay SI
   9. Smith DL
   10. Griffin JT
   11. Wenger EA
   12. Eckhoff PA
   13. Smith TA
   14. Penny MA
   15. Gething PW

   (2015) Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria

   Nature Communications 6:8170.

   https://doi.org/10.1038/ncomms9170

   • PubMed
   • Google Scholar
8. 1. Cattani JA
   2. Moir JS
   3. Gibson FD
   4. Ginny M
   5. Paino J
   6. Davidson W
   7. Alpers MP

   (1986)

   Small-area variations in the epidemiology of malaria in madang province

   Papua and New Guinea medical journal 29:11–17.

   • PubMed
   • Google Scholar
9. 1. Charlwood JD
   2. Graves PM
   3. Alpers MP

   (1986)

   The ecology of the anopheles punctulatus group of mosquitoes from Papua New Guinea: a review of recent work

   Papua and New Guinea Medical Journal 29:19–26.

   • PubMed
   • Google Scholar
10. 1. Doolan DL
    2. Dobaño C
    3. Baird JK

    (2009) Acquired immunity to malaria

    Clinical Microbiology Reviews 22:13–36.

    https://doi.org/10.1128/CMR.00025-08

    • PubMed
    • Google Scholar
11. 1. Douglas NM
    2. Nosten F
    3. Ashley EA
    4. Phaiphun L
    5. van Vugt M
    6. Singhasivanon P
    7. White NJ
    8. Price RN

    (2011) Plasmodium vivax recurrence following falciparum and mixed species malaria: risk factors and effect of antimalarial kinetics

    Clinical Infectious Diseases 52:612–620.

    https://doi.org/10.1093/cid/ciq249

    • PubMed
    • Google Scholar
12. 1. Falk N
    2. Maire N
    3. Sama W
    4. Owusu-Agyei S
    5. Smith T
    6. Beck HP
    7. Felger I

    (2006)

    Comparison of PCR-RFLP and Genescan-based genotyping for analyzing infection dynamics of Plasmodium falciparum

    The American Journal of Tropical Medicine and Hygiene 74:944–950.

    • PubMed
    • Google Scholar
13. 1. Felger I
    2. Maire M
    3. Bretscher MT
    4. Falk N
    5. Tiaden A
    6. Sama W
    7. Beck HP
    8. Owusu-Agyei S
    9. Smith TA

    (2012) The dynamics of natural plasmodium falciparum infections

    PLoS One 7:e45542.

    https://doi.org/10.1371/journal.pone.0045542

    • PubMed
    • Google Scholar
14. 1. Genton B
    2. al-Yaman F
    3. Beck HP
    4. Hii J
    5. Mellor S
    6. Narara A
    7. Gibson N
    8. Smith T
    9. Alpers MP

    (1995) The epidemiology of malaria in the wosera area, East Sepik Province, Papua New Guinea for vaccine trials. I. Malariometric indices and immunity

    Annals of Tropical Medicine & Parasitology 89:359–376.

    https://doi.org/10.1080/00034983.1995.11812965

    • PubMed
    • Google Scholar
15. 1. Genton B
    2. D'Acremont V
    3. Rare L
    4. Baea K
    5. Reeder JC
    6. Alpers MP
    7. Müller I

    (2008) Plasmodium vivax and mixed infections are associated with severe malaria in children: a prospective cohort study from Papua New Guinea

    PLoS Medicine 5:e127.

    https://doi.org/10.1371/journal.pmed.0050127

    • PubMed
    • Google Scholar
16. 1. Hetzel MW
    2. Gideon G
    3. Lote N
    4. Makita L
    5. Siba PM
    6. Mueller I

    (2012) Ownership and usage of mosquito nets after four years of large-scale free distribution in Papua New Guinea

    Malaria Journal 11:192.

    https://doi.org/10.1186/1475-2875-11-192

    • PubMed
    • Google Scholar
17. 1. Hetzel MW
    2. Choudhury AA
    3. Pulford J
    4. Ura Y
    5. Whittaker M
    6. Siba PM
    7. Mueller I

    (2014) Progress in mosquito net coverage in Papua New Guinea

    Malaria Journal 13:242.

    https://doi.org/10.1186/1475-2875-13-242

    • PubMed
    • Google Scholar
18. 1. Hetzel MW
    2. Morris H
    3. Tarongka N
    4. Barnadas C
    5. Pulford J
    6. Makita L
    7. Siba PM
    8. Mueller I

    (2015) Prevalence of malaria across Papua New Guinea after initial roll-out of insecticide-treated mosquito nets

    Tropical Medicine & International Health 20:1745–1755.

    https://doi.org/10.1111/tmi.12616

    • PubMed
    • Google Scholar
19. 1. Hii JL
    2. Smith T
    3. Mai A
    4. Mellor S
    5. Lewis D
    6. Alexander N
    7. Alpers MP

    (1997) Spatial and temporal variation in abundance of Anopheles (Diptera:Culicidae) in a malaria endemic area in Papua New Guinea

    Journal of Medical Entomology 34:193–205.

    https://doi.org/10.1093/jmedent/34.2.193

    • PubMed
    • Google Scholar
20. 1. Hii JL
    2. Smith T
    3. Vounatsou P
    4. Alexander N
    5. Mai A
    6. Ibam E
    7. Alpers MP

    (2001) Area effects of bednet use in a malaria-endemic area in Papua New Guinea

    Transactions of the Royal Society of Tropical Medicine and Hygiene 95:7–13.

    https://doi.org/10.1016/S0035-9203(01)90315-3

    • PubMed
    • Google Scholar
21. 1. Killeen GF
    2. Smith TA
    3. Ferguson HM
    4. Mshinda H
    5. Abdulla S
    6. Lengeler C
    7. Kachur SP

    (2007) Preventing childhood malaria in Africa by protecting adults from mosquitoes with insecticide-treated nets

    PLoS Medicine 4:e229.

    https://doi.org/10.1371/journal.pmed.0040229

    • PubMed
    • Google Scholar
22. 1. Koepfli C
    2. Schoepflin S
    3. Bretscher M
    4. Lin E
    5. Kiniboro B
    6. Zimmerman PA
    7. Siba P
    8. Smith TA
    9. Mueller I
    10. Felger I

    (2011) How much remains undetected? Probability of molecular detection of human Plasmodia in the field

    PLoS One 6:e19010.

    https://doi.org/10.1371/journal.pone.0019010

    • PubMed
    • Google Scholar
23. 1. Koepfli C
    2. Colborn KL
    3. Kiniboro B
    4. Lin E
    5. Speed TP
    6. Siba PM
    7. Felger I
    8. Mueller I

    (2013) A high force of plasmodium vivax blood-stage infection drives the rapid acquisition of immunity in papua new guinean children

    PLoS Neglected Tropical Diseases 7:e2403.

    https://doi.org/10.1371/journal.pntd.0002403

    • PubMed
    • Google Scholar
24. 1. Koepfli C
    2. Robinson LJ
    3. Rarau P
    4. Salib M
    5. Sambale N
    6. Wampfler R
    7. Betuela I
    8. Nuitragool W
    9. Barry AE
    10. Siba P
    11. Felger I
    12. Mueller I

    (2015) Blood-stage parasitaemia and age determine plasmodium falciparum and p. vivax gametocytaemia in Papua New Guinea

    PLoS One 10:e0126747.

    https://doi.org/10.1371/journal.pone.0126747

    • PubMed
    • Google Scholar
25. 1. Lin E
    2. Kiniboro B
    3. Gray L
    4. Dobbie S
    5. Robinson L
    6. Laumaea A
    7. Schöpflin S
    8. Stanisic D
    9. Betuela I
    10. Blood-Zikursh M
    11. Siba P
    12. Felger I
    13. Schofield L
    14. Zimmerman P
    15. Mueller I

    (2010) Differential patterns of infection and disease with P. falciparum and P. vivax in young Papua New Guinean children

    PLoS One 5:e9047.

    https://doi.org/10.1371/journal.pone.0009047

    • PubMed
    • Google Scholar
26. 1. Longley RJ
    2. Sattabongkot J
    3. Mueller I

    (2016) Insights into the naturally acquired immune response to Plasmodium vivax malaria

    Parasitology 143:154–170.

    https://doi.org/10.1017/S0031182015000670

    • PubMed
    • Google Scholar
27. 1. Mehlotra RK
    2. Lorry K
    3. Kastens W
    4. Miller SM
    5. Alpers MP
    6. Bockarie M
    7. Kazura JW
    8. Zimmerman PA

    (2000) Random distribution of mixed species malaria infections in Papua New Guinea

    The American Journal of Tropical Medicine and Hygiene 62:225–231.

    https://doi.org/10.4269/ajtmh.2000.62.225

    • PubMed
    • Google Scholar
28. 1. Michon P
    2. Cole-Tobian JL
    3. Dabod E
    4. Schoepflin S
    5. Igu J
    6. Susapu M
    7. Tarongka N
    8. Zimmerman PA
    9. Reeder JC
    10. Beeson JG
    11. Schofield L
    12. King CL
    13. Mueller I

    (2007)

    The risk of malarial infections and disease in Papua New Guinean children

    The American Journal of Tropical Medicine and Hygiene 76:997–1008.

    • PubMed
    • Google Scholar
29. 1. Mosha JF
    2. Sturrock HJ
    3. Brown JM
    4. Hashim R
    5. Kibiki G
    6. Chandramohan D
    7. Gosling RD

    (2014) The independent effect of living in malaria hotspots on future malaria infection: an observational study from misungwi, tanzania

    Malaria Journal 13:445.

    https://doi.org/10.1186/1475-2875-13-445

    • PubMed
    • Google Scholar
30. 1. Mueller I
    2. Widmer S
    3. Michel D
    4. Maraga S
    5. McNamara DT
    6. Kiniboro B
    7. Sie A
    8. Smith TA
    9. Zimmerman PA

    (2009a) High sensitivity detection of plasmodium species reveals positive correlations between infections of different species, shifts in age distribution and reduced local variation in Papua New Guinea

    Malaria Journal 8:41.

    https://doi.org/10.1186/1475-2875-8-41

    • Google Scholar
31. 1. Mueller I
    2. Widmer S
    3. Michel D
    4. Maraga S
    5. McNamara DT
    6. Kiniboro B
    7. Sie A
    8. Smith TA
    9. Zimmerman PA

    (2009b) High sensitivity detection of plasmodium species reveals positive correlations between infections of different species, shifts in age distribution and reduced local variation in Papua New Guinea

    Malaria Journal 8:41.

    https://doi.org/10.1186/1475-2875-8-41

    • PubMed
    • Google Scholar
32. 1. Mueller I
    2. Schoepflin S
    3. Smith TA
    4. Benton KL
    5. Bretscher MT
    6. Lin E
    7. Kiniboro B
    8. Zimmerman PA
    9. Speed TP
    10. Siba P
    11. Felger I

    (2012) Force of infection is key to understanding the epidemiology of plasmodium falciparum malaria in Papua New Guinean children

    PNAS 109:10030–10035.

    https://doi.org/10.1073/pnas.1200841109

    • PubMed
    • Google Scholar
33. 1. Müller I
    2. Bockarie M
    3. Alpers M
    4. Smith T

    (2003) The epidemiology of malaria in Papua New Guinea

    Trends in Parasitology 19:253–259.

    https://doi.org/10.1016/S1471-4922(03)00091-6

    • PubMed
    • Google Scholar
34. 1. Reimer LJ
    2. Thomsen EK
    3. Koimbu G
    4. Keven JB
    5. Mueller I
    6. Siba PM
    7. Kazura JW
    8. Hetzel MW
    9. Zimmerman PA

    (2016) Malaria transmission dynamics surrounding the first nationwide long-lasting insecticidal net distribution in Papua New Guinea

    Malaria Journal 15:25.

    https://doi.org/10.1186/s12936-015-1067-7

    • PubMed
    • Google Scholar
35. 1. Robinson LJ
    2. Wampfler R
    3. Betuela I
    4. Karl S
    5. White MT
    6. Li Wai Suen CS
    7. Hofmann NE
    8. Kinboro B
    9. Waltmann A
    10. Brewster J
    11. Lorry L
    12. Tarongka N
    13. Samol L
    14. Silkey M
    15. Bassat Q
    16. Siba PM
    17. Schofield L
    18. Felger I
    19. Mueller I

    (2015) Strategies for understanding and reducing the plasmodium vivax and plasmodium ovale hypnozoite reservoir in Papua New Guinean children: a randomised placebo-controlled trial and mathematical model

    PLOS Medicine 12:e1001891.

    https://doi.org/10.1371/journal.pmed.1001891

    • PubMed
    • Google Scholar
36. 1. Rosanas-Urgell A
    2. Mueller D
    3. Betuela I
    4. Barnadas C
    5. Iga J
    6. Zimmerman PA
    7. del Portillo HA
    8. Siba P
    9. Mueller I
    10. Felger I

    (2010) Comparison of diagnostic methods for the detection and quantification of the four sympatric Plasmodium species in field samples from Papua New Guinea

    Malaria Journal 9:361.

    https://doi.org/10.1186/1475-2875-9-361

    • PubMed
    • Google Scholar
37. 1. Ross A
    2. Smith T

    (2010) Interpreting malaria age-prevalence and incidence curves: a simulation study of the effects of different types of heterogeneity

    Malaria Journal 9:132.

    https://doi.org/10.1186/1475-2875-9-132

    • PubMed
    • Google Scholar
38. 1. Sama W
    2. Owusu-Agyei S
    3. Felger I
    4. Vounatsou P
    5. Smith T

    (2005) An immigration-death model to estimate the duration of malaria infection when detectability of the parasite is imperfect

    Statistics in Medicine 24:3269–3288.

    https://doi.org/10.1002/sim.2189

    • PubMed
    • Google Scholar
39. 1. Sama W
    2. Owusu-Agyei S
    3. Felger I
    4. Dietz K
    5. Smith T

    (2006) Age and seasonal variation in the transition rates and detectability of Plasmodium falciparum malaria

    Parasitology 132:13–21.

    https://doi.org/10.1017/S0031182005008607

    • PubMed
    • Google Scholar
40. 1. Schoepflin S
    2. Valsangiacomo F
    3. Lin E
    4. Kiniboro B
    5. Mueller I
    6. Felger I

    (2009) Comparison of plasmodium falciparum allelic frequency distribution in different endemic settings by high-resolution genotyping

    Malaria Journal 8:250.

    https://doi.org/10.1186/1475-2875-8-250

    • PubMed
    • Google Scholar
41. 1. Sergent ED
    2. Parrot LL

    (1935)

    Immunité, la prémunition et la résistance Innée

    Arch Inst Pasteur Algér 23:279–319.

    • Google Scholar
42. 1. Smith T
    2. Felger I
    3. Tanner M
    4. Beck HP

    (1999) Premunition in plasmodium falciparum infection: insights from the epidemiology of multiple infections

    Transactions of the Royal Society of Tropical Medicine and Hygiene 93:59–64.

    https://doi.org/10.1016/S0035-9203(99)90329-2

    • PubMed
    • Google Scholar
43. 1. Smith T
    2. Genton B
    3. Baea K
    4. Gibson N
    5. Narara A
    6. Alpers MP

    (2001) Prospective risk of morbidity in relation to malaria infection in an area of high endemicity of multiple species of Plasmodium

    The American Journal of Tropical Medicine and Hygiene 64:262–267.

    https://doi.org/10.4269/ajtmh.2001.64.262

    • PubMed
    • Google Scholar
44. 1. Smith DL
    2. Hay SI
    3. Noor AM
    4. Snow RW

    (2009) Predicting changing malaria risk after expanded insecticide-treated net coverage in Africa

    Trends in Parasitology 25:511–516.

    https://doi.org/10.1016/j.pt.2009.08.002

    • PubMed
    • Google Scholar
45. 1. Smith DL
    2. Drakeley CJ
    3. Chiyaka C
    4. Hay SI

    (2010) A quantitative analysis of transmission efficiency versus intensity for malaria

    Nature Communications 1:108.

    https://doi.org/10.1038/ncomms1107

    • PubMed
    • Google Scholar
46. 1. Tarning J
    2. Thana P
    3. Phyo AP
    4. Lwin KM
    5. Hanpithakpong W
    6. Ashley EA
    7. Day NP
    8. Nosten F
    9. White NJ

    (2014) Population Pharmacokinetics and Antimalarial Pharmacodynamics of Piperaquine in Patients With Plasmodium vivax Malaria in Thailand

    CPT Pharmacometrics Syst. Pharmacol. 3:e132.

    https://doi.org/10.1038/psp.2014.29

    • PubMed
    • Google Scholar
47. 1. Tusting LS
    2. Bousema T
    3. Smith DL
    4. Drakeley C

    (2014) Measuring changes in Plasmodium falciparum transmission: precision, accuracy and costs of metrics

    Advances in Parasitology 84:151–208.

    https://doi.org/10.1016/B978-0-12-800099-1.00003-X

    • PubMed
    • Google Scholar
48. 1. Wampfler R
    2. Mwingira F
    3. Javati S
    4. Robinson L
    5. Betuela I
    6. Siba P
    7. Beck HP
    8. Mueller I
    9. Felger I

    (2013) Strategies for detection of Plasmodium species gametocytes

    PLoS One 8:e76316.

    https://doi.org/10.1371/journal.pone.0076316

    • PubMed
    • Google Scholar
49. 1. White NJ
    2. Imwong M

    (2012) Relapse

    Advances in Parasitology 80:113–150.

    https://doi.org/10.1016/B978-0-12-397900-1.00002-5

    • PubMed
    • Google Scholar
50. Report

    1. World Health Organization

    (2015)

    World Malaria Report

    Geneva, Switzerland: World Health Organization.

    • Google Scholar

Author details

1. Natalie E Hofmann

   1. Swiss Tropical and Public Health Institute, Basel, Switzerland
   2. University of Basel, Basel, Switzerland

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing—original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6282-5364

2. Stephan Karl

   1. Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
   2. Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea

   Contribution

   Formal analysis, Writing—original draft

   Competing interests

   No competing interests declared

3. Rahel Wampfler

   1. Swiss Tropical and Public Health Institute, Basel, Switzerland
   2. University of Basel, Basel, Switzerland

   Contribution

   Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Benson Kiniboro

   Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea

   Contribution

   Data curation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. Albina Teliki

   Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea

   Contribution

   Data curation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

6. Jonah Iga

   Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea

   Contribution

   Data curation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

7. Andreea Waltmann

   1. Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
   2. University of Melbourne, Melbourne, Australia

   Contribution

   Data curation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

8. Inoni Betuela

   Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea

   Contribution

   Supervision, Investigation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

9. Ingrid Felger

   1. Swiss Tropical and Public Health Institute, Basel, Switzerland
   2. University of Basel, Basel, Switzerland

   Contribution

   Conceptualization, Supervision, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

10. Leanne J Robinson

    1. Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    2. Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea
    3. University of Melbourne, Melbourne, Australia
    4. Burnet Institute, Melbourne, Australia

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Writing—original draft, Project administration, Writing—review and editing

    Contributed equally with

    Ivo Mueller

    For correspondence

    robinson@wehi.edu.au

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9903-1023

11. Ivo Mueller

    1. Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    2. University of Melbourne, Melbourne, Australia
    3. ISGlobal, Barcelona Centre for International Health Research, Hospital Clínic-University of Barcelona, Barcelona, Spain
    4. Institut Pasteur, Paris, France

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing—review and editing

    Contributed equally with

    Leanne J Robinson

    For correspondence

    ivomueller@fastmail.fm

    Competing interests

    No competing interests declared

• 1,467

  views

• 271

  downloads

• 31

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.