Malaria cases in Tanzania comprise 3% of globally reported cases, but transmission is heterogeneous, with the coastal mainland witnessing declining but substantial transmission of Plasmodium falciparum (Alegana et al., 2021; World Health Organization, 2022). On the other hand, the archipelago of Zanzibar is a pre-elimination setting, with low-level seasonal transmission (Björkman et al., 2019). This is largely due to the routine implementation of a combination of effective control measures, including robust vector control and routine access to effective antimalarials (Björkman et al., 2019). Despite these efforts, malaria has been difficult to eliminate from the archipelago. There are several reasons this may be the case: (1) frequent importation of malaria from moderate- or high-transmission regions of mainland Tanzania and Kenya (Björkman et al., 2019; Le Menach et al., 2011; Lipner et al., 2011; Monroe et al., 2019; Morgan et al., 2020; Tatem et al., 2009); (2) ongoing local transmission due to residual vector capacity despite strong vector control (Björkman et al., 2019); and (3) a reservoir of asymptomatic infections (Björkman and Morris, 2020; Björkman et al., 2019).

Parasite genomics has the potential to help us better understand malaria epidemiology by uncovering population structure and gene flow, providing insight into the changes in the parasite population including how parasites move between regions (Neafsey et al., 2021). Genomics has previously been used to study importation and transmission chains in other low-transmission settings in Africa and elsewhere (Chang et al., 2019; Fola et al., 2023; Morgan et al., 2020; Patel et al., 2014; Roh et al., 2019; Sane et al., 2019). Previously, we had investigated the importation of malaria into Zanzibar from the mainland using whole-genome sequencing, showing highly similar populations within the mainland and within the archipelago, but also identifying highly related parasite pairs between locations, suggesting a role for importation (Morgan et al., 2020). However, this work lacked sufficient samples to assess transmission of parasites within Zanzibar. The larger and spatially rich sample set analyzed in this article offers an opportunity for more refined analyses of transmission across Zanzibar and how parasites are related to those from coastal mainland.

A panel of molecular inversion probes (MIPs), a highly multiplexed genotyping assay, were designed in a previous study to target single-nucleotide polymorphisms (SNPs) throughout the P. falciparum genome (Aydemir et al., 2018). We leveraged this assay to investigate the genetic epidemiology of parasites in the coastal mainland and Zanzibar utilizing 391 samples collected from cross-sectional surveys of both asymptomatic infections and symptomatic, uncomplicated malaria cases during 2016–2018. Specifically, we use identity by descent (IBD) analyses to compare the genetic relatedness of mainland and Zanzibari parasites, and investigate the geography/spatial relationships of genetically related parasites on the archipelago. We further characterize how the genetic complexity of infections (COIs) differs by clinical status and describe patterns of antimalarial drug resistance polymorphisms in the parasite populations. In this low-transmission setting, these analyses characterize fine-scale local parasite populations that contribute to continued transmission within the region, highlighting a key barrier to malaria elimination in the Zanzibar archipelago.

Samples from coastal Tanzania (178) and Zanzibar (213) were previously sequenced through multiple studies (Table 1, Figure 1—figure supplement 1). These samples include 213 dried blood spots (DBS) collected in Zanzibar between February 2016 and September 2017, coming from cross-sectional surveys of asymptomatic individuals (n = 70) and an in vivo efficacy study of artesunate-amodiaquine (ASAQ) with single low-dose primaquine (SLDP) in pediatric uncomplicated malaria patients in the western and central districts of Unguja island and Micheweni district on Pemba island (n = 143) (Msellem et al., 2020). These samples were geolocalized to shehias, the lowest geographic governmental designation of land in Zanzibar, across its two main islands, Unguja and the northern region of Pemba (Figure 1—figure supplement 1). Mainland Tanzania samples were collected in rural Bagamoyo district, where malaria transmission persists, and residents frequently travel to Dar es Salaam, the major port from where travelers depart for Zanzibar. Of the mainland Bagamoyo samples, 138 were whole blood collected from 2015 to 2017 as part of an in vivo efficacy study of artemether-lumefantrine (AL) in pediatric uncomplicated malaria patients (Topazian et al., 2022), and the remaining 40 samples were leukodepleted blood collected in 2018 from asymptomatic but RDT-positive children who participated in a study investigating the transmission of P. falciparum to colony-reared mosquitoes. This project leveraged MIP data from SRA, including PRJNA926345, PRJNA454490, PRJNA545345, and PRJNA545347.

In order to place coastal Tanzanian and Zanzibari samples in the context of African P. falciparum population structure across multiple regions, MIP data from 147 whole blood samples collected in Ahero District, Kenya, from the same parasite clearance study were used (Topazian et al., 2022) in conjunction with a subset of data from 2537 samples genotyped for a study of the 2013 Demographic Health Survey of the Democratic Republic of the Congo (DRC), which included samples from DRC, Ghana, Tanzania, Uganda, and Zambia (Verity et al., 2020; see Figure 1—figure supplement 2).

Analysis of population relatedness and structure

To investigate genetic relatedness of parasites across regions, IBD estimates were assessed using the within-sample major alleles (coercing samples to monoclonal by calling the dominant allele at each locus) and estimated utilizing a maximum likelihood approach using the inbreeding_mle function from the MIPanalyzer package (Verity et al., 2020). This approach has previously been validated as a conservative estimate of IBD (Verity et al., 2020). Next, principal component analysis (PCA) was performed to query the comparative genetic variation of the samples by utilizing the genome-wide SNP panel. We pruned 51 samples that had a pairwise IBD of >0.90 to one randomly selected sample as a representative of the clonal population to avoid clonal structure from dominating the analysis. Within-sample allele frequencies were calculated, with an imputation step replacing missing values with the median per each locus, and PCA was performed using the prcomp function (Verity et al., 2020; R version 4.2.1).

To include geographic information with querying genetic variation, discriminant analysis of principal components (DAPC) was used (Jombart, 2008). Pseudohaplotypes were created by pruning the genotype calls at all loci for each sample into a single haplotype, and redundant haplotypes were removed (282 reduced to 272 with unique pseudohaplotypes). DAPC was conducted at the district level, and samples from districts with less than five samples (272 samples to 270 samples in six districts) were retained (Figure 1—figure supplement 4B). For the main DAPC (Figure 1B), highly related isolates were pruned to a single representative infection (272 reduced to 232) and then included districts with at least five samples (232 reduced to 228 samples in five districts). The DAPC was performed using the adegenet package (Jombart, 2008) with the first 80 PCs based on the cross-validation function xvalDapc. To perform K-means clustering, a cluster K of 1 was assigned to the mainland samples while the kmeans package was used to find the optimal K to cluster the Zanzibar shehias by latitude and longitude. The K-means clustering experiment was used to cluster a continuous space of geographic coordinates in order to compare genetic relatedness in different regions. We selected K = 4 as the inflection point based on the elbow plot (Figure 2—figure supplement 1) and based the number to obtain sufficient subsections of Zanzibar to compare genetic relatedness.

Figure 1 with 4 supplements see all

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To investigate how genetic relatedness varies as the distance between pairs increases, isolation by distance was performed across all of Zanzibar and within the islands of Unguja and Pemba. The greater circle (GC) distances between each shehia centroid were calculated (within shehia distances were equal to 0) to find the distance between each shehia in geographic space. Distances were then binned at increments reflecting the max GC distances between regions, which was smallest in Pemba at 12 km and much larger for both Unguja (58 km) and all of Zanzibar (135 km). Within each binned group, the mean IBD with 95% CIs was plotted. For graphing IBD connections at the between and within shehia level, an IBD threshold of 0.25 (half-siblings) or greater was used (Figure 4—figure supplements 1 and 2). In graphing IBD connections at larger distances between islands or between coastal mainland Tanzania and Zanzibar, a between IBD value of 0.125 (quarter-siblings) or greater was used (Figure 4—figure supplements 3 and 4). These plots were created utilizing ggraph in R with the nodes being samples and the edges being IBD estimates.

COI, or the number of parasite clones in a given sample, was determined using THE REAL McCOIL (v2) categorical method (Chang et al., 2017) and the 95% CI was calculated utilizing a nonparametric bootstrap. Fws statistic, which is used to compare the diversity within and between samples in a population, was calculated in R version 4.2.1 through the formula, ${\displaystyle (1-H_w)/H_p}$, where H_w is the within-sample heterozygosity and H_p is the heterozygosity across the population, and 95% CIs were calculated utilizing a nonparametric bootstrap.

In this study, we leverage high-throughput targeted sequencing using MIPs to characterize the populations and the relationships of P. falciparum isolates in Zanzibar and coastal mainland Tanzania. The parasite populations appear to be highly related to each other (Figure 1A) when evaluated using SNPs in the core genome. Interestingly, within Zanzibar, structure could be observed, with parasites closer to the main ferry terminal in Zanzibar town clustered more closely with coastal mainland parasites (Figure 1B, Figure 1—figure supplement 1) compared to parasites that were more geographically distant. This, in combination with the evidence of rapid decline of genetic relatedness with distance on the archipelago (Figure 3), is consistent with population microstructure within the island chain. This microstructure within the archipelago is supported by K-means clustering where Zanzibari isolates show higher within-cluster than between-cluster IBD (Figure 2). It is also consistent with isolates with higher IBD (Figure 4A and B) in Unguja and Pemba compared to a maximum IBD of 0.20 between Zanzibar and coastal mainland Tanzania (Figure 4—figure supplement 3) or between Unguja and Pemba (Figure 4—figure supplement 4). Parasite populations within the very low transmission region of Zanzibar may be more isolated than expected, allowing them to differentiate from each other. This may be indicative of very effective local malaria control, yet with continued micro-transmission remaining. Thus, directly targeting local malaria transmission, including the asymptomatic reservoir which contributes to sustained transmission (Barry et al., 2021; Sumner et al., 2021), may be an important focus for ultimately achieving malaria control in the archipelago (Björkman and Morris, 2020). Currently, a reactive case detection program within index case households is being implemented, but local transmission continues and further investigation into how best to control this is warranted (Mkali et al., 2023).

Despite the overall genetic similarity between archipelago populations, we did not find parasite pairs with high levels of IBD between the coastal mainland and Zanzibar, with the highest being 0.20. While this level still represents a significant amount of genetic sharing, similar to a cousin, the lack of higher levels does not allow us to identify specific importation events. This is largely due to the study design, which is based on convenience sampling, the relatively low numbers of samples, and lack of sampling from all mainland travel hubs (Bisanzio et al., 2023). Sampling was also denser in Unguja compared to Pemba. On the other hand, we see clear transmission of highly related parasites within each population (IBD > 0.99). In Zanzibar, highly related parasites mainly occur in the range of 20–30 km. These results are similar to our previous work using whole-genome sequencing of isolates from Zanzibar and mainland Tanzania, showing increased within-population IBD compared to between-population IBD (Morgan et al., 2020). The network of highly related P. falciparum parasites from six shehias in north Pemba provides an excellent example of likely recent near-clonal transmission, consistent with an outbreak (Figure 4C). A recent study investigating population structure in Zanzibar also found local population microstructure in Pemba (Holzschuh et al., 2023). Furthermore, both studies found near-clonal parasites within the same district, Micheweni, and found population microstructure over Zanzibar. Overall, given the findings of microstructure with significant local sharing of highly related strains, these small clusters still potentially drive much of the malaria transmission that occurs within the archipelago through routine human movement or mosquito travel between locales (Huestis et al., 2019). Less frequent longer distance transmission events also occur, likely due to longer range human migration within the islands.

Asymptomatic parasitemia has been shown to be common in falciparum malaria around the globe and has been shown to have increasing importance in Zanzibar (Lindblade et al., 2013; Morris et al., 2015). What underlies the biology and prevalence of asymptomatic parasitemia in very low transmission settings where antiparasite immunity is not expected to be prevalent remains unclear (Björkman and Morris, 2020). Similar to a few previous studies, we found that asymptomatic infections had a higher COI than symptomatic infections across both the coastal mainland and Zanzibar parasite populations (Collins et al., 2022; Kimenyi et al., 2022; Sarah-Matio et al., 2022). Other studies have found lower COI in severe vs. mild malaria cases (Robert et al., 1996) or no significant difference between COI based on clinical status (Conway et al., 1991; Earland et al., 2019; Kun et al., 1998; Lagnika et al., 2022; Tanabe et al., 2015). In Zambia, one study suggested that infections that cause asymptomatic infection may be genetically different from those that cause symptomatic infection (Searle et al., 2017). However, this study included samples collected over different time periods and relied on a low-density genotyping assay that only investigated the diversity of 24 SNPs across the genome. Here, based on SNPs throughout the core genome, we did not see differential clustering of asymptomatic or symptomatic infections in Zanzibar or the mainland (Figure 1A), suggesting that these parasite populations remain similar when comparing clinical status. However, this genotyping approach does not address potential variation in the many hypervariable gene families that encode genes known to be associated with pathogenesis (e.g., var, rifin, and stevor genes) and does not address differences in expression of genes associated with pathogenesis that may reflect differences in the populations. Investigation with other methods, such as long-read genome sequencing and transcriptional profiling, would be needed to address these differences.

While mutations for partial artemisinin resistance were not observed in K13, other antimalarial-resistant mutations of concern were observed. Validated drug resistance mutations linked to sulfadoxine/pyrimethamine resistance (Pfdhfr-N51I, Pfdhfr-C59R, Pfdhfr-S108N, Pfdhps-A437G, Pfdhps-K540E) were found at high prevalence (Table 2). Prevalence of polymorphisms associated with amodiaquine resistance (Pfcrt-K76T, Pfmdr1-N86Y, Pfmdr1-Y184F, Pfmdr1-D1246Y) was seen at similar proportions as previous reports (Msellem et al., 2020). The wild-type Pfmdr1-N86 was dominant in both mainland and archipelago populations, concerning reduced lumefantrine susceptibility. Although polymorphisms associated with artemisinin resistance did not appear in this population, continued surveillance is warranted given the emergence of these mutations in East Africa and reports of rare resistance mutations on the coast consistent with the spread of emerging Pfk13 mutations (Moser et al., 2021).

Overall, parasites between Zanzibar and coastal mainland Tanzania remain highly related, but population microstructure on the island reflects ongoing low-level transmission in Zanzibar, partially driven by asymptomatic infections that potentially constitute a long-term reservoir. This is likely the result of the continued pressure on the population through the implementation of effective control measures. In this study, parasite genomics allows us to parse differences in parasite populations and reveals substructure in an area of low-transmission intensity. A recent study identified ‘hotspot’ shehias, defined as areas with comparatively higher malaria transmission than other shehias, near the port of Zanzibar town and in northern Pemba (Bisanzio et al., 2023). These regions overlapped with shehias in this study with high levels of IBD, especially in northern Pemba (Figure 4). These areas of substructure represent parasites that differentiated in relative isolation and are thus important locales to target intervention to interrupt local transmission (Bousema et al., 2012). While a field cluster-randomized control trial in Kenya targeting these hotspots did not confer much reduction of malaria outside of the hotspot (Bousema et al., 2016), if areas are isolated pockets, which genetic differentiation can help determine, targeted interventions in these areas are likely needed, potentially through both mass drug administration and vector control (Morris et al., 2018; Okell et al., 2011). Such strategies and measures preventing imported malaria could accelerate progress toward zero malaria in Zanzibar.

Parasite sequence data is available through SRA (BioProject PRJNA926345). Code used for analysis is available at: https://github.com/sconnelly007/TAN_MIP (copy archived at Connelly, 2024).

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Author details

1. Sean V Connelly

   MD-PhD Program, University of North Carolina at Chapel Hill, Chapel Hill, United States

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   sean_connelly@med.unc.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7330-7340

2. Nicholas F Brazeau

   MD-PhD Program, University of North Carolina at Chapel Hill, Chapel Hill, United States

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Mwinyi Msellem

   Research Division, Ministry of Health, Zanzibar, United Republic of Tanzania

   Contribution

   Resources, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Billy E Ngasala

   1. Department of Parasitology and Medical Entomology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of Tanzania
   2. Global Health and Migration Unit, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden

   Contribution

   Conceptualization, Resources, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

5. Ozkan Aydemir

   Department of Medicine, University of Massachusetts Chan Medical School, Worcester, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3458-6527

6. Varun Goel

   Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

7. Karamoko Niaré

   Department of Pathology and Laboratory Medicine, Brown University, Providence, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

8. David J Giesbrecht

   Department of Pathology and Laboratory Medicine, Brown University, Providence, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Zachary R Popkin-Hall

   Institute for Global Health and Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Chris Hennelly

    Institute for Global Health and Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Zackary Park

    Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Ann M Moormann

    Department of Medicine, University of Massachusetts Chan Medical School, Worcester, United States

    Contribution

    Conceptualization, Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

13. John M Ong'echa

    Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya

    Contribution

    Conceptualization, Resources, Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3928-6774

14. Robert Verity

    MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom

    Contribution

    Conceptualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

15. Safia Mohammed

    Zanzibar Malaria Elimination Program (ZAMEP), Zanzibar, United Republic of Tanzania

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

16. Shija J Shija

    Zanzibar Malaria Elimination Program (ZAMEP), Zanzibar, United Republic of Tanzania

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

17. Lwidiko E Mhamilawa

    1. Department of Parasitology and Medical Entomology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of Tanzania
    2. Global Health and Migration Unit, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden

    Contribution

    Conceptualization, Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

18. Ulrika Morris

    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden

    Contribution

    Conceptualization, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4734-5754

19. Andreas Mårtensson

    Global Health and Migration Unit, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Investigation, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

20. Jessica T Lin

    Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4516-723X

21. Anders Björkman

    1. Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
    2. Department of Global Public Health, Karolinska Institute, Stockholm, Sweden

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

22. Jonathan J Juliano

    1. Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States
    2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, United States
    3. Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    Additional information

    co-senior authors

    "This ORCID iD identifies the author of this article:" 0000-0002-0591-0850

23. Jeffrey A Bailey

    Department of Pathology and Laboratory Medicine, Brown University, Providence, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    Additional information

    co-senior authors

    "This ORCID iD identifies the author of this article:" 0000-0002-6899-8204

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