Zoonotic diseases are infectious diseases that are transmitted from animals to humans. For example, the malaria-causing parasite Plasmodium knowlesi can be transmitted from monkeys to humans through mosquitos that have previously fed on infected monkeys. In Malaysia, progress towards eliminating malaria is being undermined by the rise of human incidences of ‘monkey malaria’, which has been declared a public health threat by The World Health Organisation.

In humans, cases of monkey malaria are higher in areas of recent deforestation. Changes in habitat may affect how monkeys, insects and humans interact, making it easier for diseases like malaria to pass between them. Deforestation could also change the behaviour of wildlife, which could lead to an increase in infection rates. For example, reduced living space increases contact between monkeys, or it may prevent behaviours that help animals to avoid parasites.

Johnson et al. wanted to investigate how the prevalence of malaria in monkeys varies across Southeast Asia to see whether an increase of Plasmodium knowlesi in primates is linked to changes in the landscape. They merged the results of 23 existing studies, including data from 148 sites and 6322 monkeys to see how environmental factors like deforestation influenced the amount of disease in different places.

Many previous studies have assumed that disease prevalence is high across all macaques, monkey species that are considered pests, and in all places. But Johnson et al. found that disease rates vary widely across different regions. Overall disease rates in monkeys are lower than expected (only 12%), but in regions with less forest or more ‘fragmented’ forest areas, malaria rates are higher. Areas with a high disease rate in monkeys tend to further coincide with infection hotspots for humans. This suggests that deforestation may be driving malaria infection in monkeys, which could be part of the reason for increased human infection rates.

Johnsons et al.’s study has provided an important step towards better understanding the link between deforestation and the levels of monkey malaria in humans living nearby. Their study provides important insights into how we might find ways of managing the landscape better to reduce health risks from wildlife infection.

Zoonotic infectious diseases arise from the spillover of pathogens into human populations, typically from a reservoir in wildlife hosts. Anthropogenic land use and land cover change have now been widely linked to infectious disease outbreaks (Brock et al., 2019; Davidson et al., 2019a; Loh et al., 2016). Such practices, including deforestation, logging, clearing for cash-crop plantations or conversion of intact forest into arable land, are accelerating across tropical forests of Southeast Asia (Fornace et al., 2021; Imai et al., 2018; Fornace et al., 2021; Imai et al., 2018). Mechanisms that underly the association between habitat disturbance and spillover risk from wildlife hosts are complex and occur over multiple spatial scales (Brock et al., 2019). In Brazil, re-emergence of Yellow Fever Virus in both NHPs and humans has been linked to areas with highly fragmented forest (Ilacqua et al., 2021). In part, an increase in ‘edge’ habitat in fragmented or mosaic landscapes can facilitate spatial overlap and altered contact patterns between wildlife, vectors, and humans (Lehman et al., 2006). Such ecological interfaces are also thought to contribute to parasite spillover in other vector-borne diseases including Zika (Li et al., 2021a), Babesiosis and Lyme disease (Simon et al., 2014), Trypanosoma cruzi (Vaz et al., 2007) and zoonotic malaria (Brock et al., 2019; Grigg et al., 2017). At the same time, habitat fragmentation can have detrimental impact on wildlife population viability, with reduced host species occupancy and reduced disease burden in highly disturbed habitats (Hanski and Ovaskainen, 2000). Disentangling this interplay is essential to inform ecological strategies for surveillance and mitigation of diseases in regions undergoing landscape change (Fornace et al., 2021).

Zoonotic P. knowlesi is a public health threat of increasing importance across Southeast Asia, following the identification of a prominent infection foci in Borneo in 2004 (Singh et al., 2004). P. knowlesi is a zoonosis, with a sylvatic cycle circulating in non-human primates (NHPs). Human cases currently occur only from spillover events (Ruiz Cuenca et al., 2022; Fornace et al., 2022; Fornace et al., 2023; Lee et al., 2011). Human transmission requires bites from infective mosquitos, primarily anopheline mosquitos of the Leucosphyrus Complex (Anopheles balabacensis, An. latens, An. introlactus) and Dirus Complex (An. dirus, An. cracens) (Moyes et al., 2016; Vythilingam et al., 2006; Wong et al., 2015). Natural hosts for P. knowlesi are typically Long-tailed macaques (Macaca fascicularis) and Southern Pig-tailed macaques (M. nemestrina) (Moyes et al., 2016), both occurring widely across Southeast Asia. Currently, distribution of P. knowlesi cases is thought to be restricted to the predicted ranges of known vector and host species (Davidson et al., 2019b), although recent studies have also identified other NHPs found to be harbouring P. knowlesi. This includes Stump-tailed macaques (M. arctoides), which are now considered to be another natural reservoir (Fungfuang et al., 2020).

Progress towards malaria elimination in Malaysia has been stymied by a recent rise in human incidence of P. knowlesi malaria. Even after accounting for increases in surveillance and diagnostic improvements it is now recognised as the most common cause of clinical malaria in Malaysia (Cooper et al., 2020). Indeed, Malaysia was the first country not to qualify for malaria elimination due to ongoing presence of zoonotic malaria and the WHO updated the guidelines to reflect zoonotic malaria as a public health threat (World Health Organization, 2021). Emergence of Plasmodium knowlesi infections has been linked to changes in land cover and land use (Fornace et al., 2021). While sporadic cases have been reported across Southeast Asia, including in Indonesia (Setiadi et al., 2016), the Philippines (Fornace et al., 2018), Vietnam (Maeno et al., 2015), Brunei (Koh et al., 2019), and Myanmar (Ghinai et al., 2017), the majority of P. knowlesi cases are found in East Malaysia (Borneo) with hotspots in the states of Sabah and Sarawak (Jeyaprakasam et al., 2020), areas that have seen extensive deforestation and landscape modification. In Sabah, human prevalence of P. knowlesi infection has recently been shown to be specifically associated with recent loss of intact forest, agricultural activities, and fragmentation across multiple localised spatial scales (Brock et al., 2019; Fornace et al., 2019b; Fornace et al., 2016).

Prevalence of the pathogen in reservoir hosts is one of three crucial factors determining the force of infection in zoonotic spillover events (Murray and Daszak, 2013). Despite this, very little is known of the impact of rapid landscape change on the distribution of P. knowlesi in NHPs. Literature on the impacts of fragmentation on primates tends to focus on primate density and abundance (Link et al., 2010; Zunino et al., 2007). What is known is that effects of land cover changes on primate-pathogen dynamics are highly variable and context-specific. Although the vector species responsible for sylvatic transmission remain unknown, the Anopheles leucospryphus group, the only vector group implicated in P. knowlesi transmission, is widely associated with secondary, disturbed forest (Brant, 2011; Hawkes et al., 2019; Wong et al., 2015). Macaques have been known to preferentially rely on fringe habitat, a behaviour that may be exaggerated in response to habitat fragmentation and facilitate exposure to vectors (Lehman et al., 2006; Stark et al., 2019). Changes to land composition can also create the biosocial conditions for higher rates of parasitism in primates. Under conditions of limited resources and reduction in viable habitat, conspecific primate density may increase as troops compete for available space. In turn, this can favour transmission via intra-species contact or allow the exchange of pathogens between troops dwelling in interior forest versus edge habitat (Faust et al., 2018; Stark et al., 2019). Habitat use may also become more intensive, preventing parasite avoidance behaviours (Nunn and Dokey, 2006). Land cover change is also known to favour more adaptable, synanthropic species such as M. fascicularis (McFarlane et al., 2012). Considering the spillover risk posed by wildlife reservoirs of P. knowlesi, clarifying any relationships between environmental factors and parasitaemia in key host species may contribute to a more comprehensive understanding of P. knowlesi transmission patterns.

Earth Observation (EO) data provides novel opportunities to investigate epidemiological patterns of diseases which are linked to environmental drivers (Kalluri et al., 2007). In relation to P. knowlesi, utility of fine-scale remote-sensing data has been demonstrated: examples include satellite-derived data used to examine household-level exposure risk in relation to proximate land configuration (Fornace et al., 2019b), UAV-imagery used to link real-time deforestation to macaque host behavioural change (Stark et al., 2019), and remote-sensing data used to interrogate risk factors for vector breeding sites (Byrne et al., 2021). Although macroecological studies that utilise geospatial data are often confounded by issues of matching temporal and spatial scales, as well as by the quality and accuracy of available georeferencing, measures can be taken to account for this when examining the role of environmental factors in modulating disease outcomes. Furthermore, ecological processes occur and interact over a range of distances, or ‘spatial scales’ (Brock et al., 2019; Fornace et al., 2016; Loh et al., 2016). This applies to determinants of vector-borne disease ecology, from larval breeding microclimate to wildlife host foraging behaviour. As multiple influential variables are rarely captured by a single scale (Cohen et al., 2016), data-driven methods can be applied to examine risk factors over multiple scales and identify covariates at their most influential extent (Byrne et al., 2021).

We hypothesise that prevalence of P. knowlesi in primate host species is spatially heterogeneous and that higher prevalence is partially driven by forest loss and fragmentation, contributing to the strong associations described between land use, land cover and human P. knowlesi risk. This study is the first to systematically assess P. knowlesi prevalence in NHPs at a regional scale, and across a wide range of habitats. In conceptual frameworks and transmission models, it is often assumed that P. knowlesi infections in NHPs are chronic (low level, persistent infection) and ubiquitous (uniformly distributed across populations; Brock et al., 2016; Jeyaprakasam et al., 2020). No studies have systematically assessed the extent and quality of all available data on P. knowlesi in NHPs. Independent studies investigating P. knowlesi in primates are typically constrained by small sample sizes and confined geographic areas, limiting inference that can be made about relationships between infection dynamics and landscape characteristics. Systematic tools developed for epidemiological studies of disease prevalence in human populations are rarely applied to the study of wildlife disease prevalence; however, such tools can be used to capture the scale and contrast required in macroecological studies to quantify disease burdens regionally. Furthermore, while recent research has shown the impact of deforestation on the distribution of macaques in the context of P. knowlesi (Moyes et al., 2016; Stark et al., 2019), associations between landscape and variation in the prevalence of simian Plasmodium spp. in primates have not been explored. We aimed to (1) assemble a georeferenced dataset of P. knowlesi in NHPs; (2) evaluate variation in NHP P. knowlesi prevalence by geographic region; and (3) assess environmental and spatial risk factors for P. knowlesi prevalence in NHPs across Southeast Asia.

A systematic literature review was conducted in Medline, Embase, and Web of Science to identify articles reporting prevalence of naturally acquired Plasmodium knowlesi in NHPs. Twenty-three research articles were identified (Akter et al., 2015; Amir et al., 2020; Chang et al., 2011; Fungfuang et al., 2020; Gamalo et al., 2019; Ho et al., 2010; Zamzuri et al., 2020; Jeslyn et al., 2011; Kaewchot et al., 2022; Lee et al., 2011; Li et al., 2021b; Muehlenbein et al., 2015; Nada-Raja et al., 2022; Putaporntip et al., 2010; Saleh Huddin et al., 2019; Salwati et al., 2017; Seethamchai et al., 2008; Shahar, 2019; Gilhooly et al., 2016; personal correspondence, 2013 and 2015 ; Vythilingam et al., 2008; Yusuf et al., 2022; Zhang et al., 2016), containing 148 unique primate survey records to form the dataset for analyses (see Appendix 2 for details of JBI Critical Assessment) (Munn et al., 2015). Year of sampling ranges from 2004 to 2019. No primatological studies were identified from Vietnam, Brunei, or Timor-Leste. Full characteristics of the articles and individual study methodologies are reported in Appendix 1—table 2. Spatial resolution of the survey sites varied from GPS point coordinates to country-level administrative boundaries (Appendix 5—table 1). Geographic distribution of sampling is illustrated in Figure 1.

Figure 1

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Overall, records report on a total of 6322 primates, with the largest proportion sampled from Peninsular Malaysia (48.5%, n=3069/6322). Primate surveys were primarily conducted on Long-tailed macaques (Macaca fascicularis) (90.5%, n=5720/6322) followed by Pig-tailed macaques (M. nemestrina; n=532/6322; Amir et al., 2020; Lee et al., 2011; Muehlenbein et al., 2015; Putaporntip et al., 2010; Appendix 1—table 3). Reported prevalence of Plasmodium knowlesi in NHPs ranged from 0% to 100%. Only 87 of the surveys (58.8%, n=87/148) reported a positive diagnosis, with the remaining 61 sites finding no molecular evidence of P. knowlesi infection (41.2%) in any primates tested. A full breakdown of P. knowlesi infection rates according to reported primate characteristics can be found in Appendix 1.

Meta-analysis of P. knowlesi prevalence

To quantify regional heterogeneity in simian cases of P. knowlesi, a one-stage meta-analysis of prevalence (number positive out of the number sampled) was conducted on primate malaria survey data. Overall pooled estimate for P. knowlesi prevalence was 11.99% (CI95% 9.35–15.26). Overall heterogeneity was assessed using the I² statistic. Substantial between-study heterogeneity (I² ≥75%) was found across all prevalence records (I²=80.5%; CI95% 77.3–83.1). In the sub-group analysis by region, pooled prevalence estimates are consistently low for Thailand (2.0%, CI95% 1.1–3.5%), moderate in Peninsular Malaysia (14.3%, CI95% 11.1–18.2) and elevated in Singapore (23.3%, CI95% 11.0–42.8) and Malaysian Borneo (41.1%, CI95% 20.8–64.9) (Figure 2). Sub-group heterogeneity was assessed using prediction intervals, derived from τ ² statistic used to describe between-study variability. Prediction intervals indicate high heterogeneity of estimates within regions, consistent with expectations of high variability of prevalence across individual study sites. Detailed forest plots for individual prevalence estimates can be found in Appendix 3—figure 2.

Figure 2

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Risk factor analysis

Covariate data and P. knowlesi prevalence data were used to fit additional models to explore the relationships between localised landscape configuration and NHP malaria prevalence. Environmental covariates were extracted from satellite-derived remote sensing datasets (Table 1) at either true sampling sites (GPS coordinates) or 10 random pseudo-sampling sites to account for geographic uncertainty in prevalence data. Host species was grouped as ‘Macaca fascicularis’ or ‘Other’ due to sample counts of <10 for certain primate species. Only 57.4% (n=85/148 records) of data included year of sampling, deemed to be insufficient to assess temporal patterns in prevalence. Tree canopy cover ranged from negligible to near total cover (100%) within buffer radii (Appendix 4—table 2). Details of covariate data processing is illustrated in Appendix 4.

Table 1

Covariate	Spatial res.	Temporal res.	Source
Human density (p/km2)	1 km	2012	WorldPop, 2018
Elevation (m)	1 km	2003	SRTM 90 m Digital Elevation v4.1 Jarvis et al., 2008
Tree cover (1/0)*	30 m	Annual	Hansen’s Global Forest Watch Hansen et al., 2013

1. *

   Derivatives: Proportion canopy cover (%), Perimeter: area ratio (PARA >0)

Following a two-stage approach for selection of explanatory variables, tree cover and fragmentation (measured by perimeter: area ratio, PARA) were retained at 5 km as linear terms, human population density was retained at both 5 km and 20 km and primate species was retained as a categorical variable. Spearman’s rank tests for residual correlation between final variables at selected scales indicates a strong negative correlation between tree cover and fragmentation index (PARA; ρ = –0.75; Appendix 6—figure 2).

Adjusting for all other covariates in the model, we identified strong evidence of an effect between increasing tree canopy cover and higher prevalence of P. knowlesi in NHPs within a 5 km radius (aOR = 1.38, CI95% 1.19–1.60; p<0.0001). Evidence was also found for an association between likelihood of P. knowlesi and higher degrees of habitat fragmentation (PARA) within 5 km (aOR = 1.17, CI95% 1.02–1.34, p<0.0281). Evidence suggests that human population density within a 5 km radius is associated with risk of P. knowlesi in NHP (aOR = 1.36, CI95% 1.16–1.58, p=0.0001) whilst human density within 20 km has an inverse effect on likelihood of P. knowlesi (aOR = 0.56, CI95% 0.46–0.67, p<0.0001). M. fascicularis is also associated with higher prevalence relative to all other non-human primate species (aOR = 2.50, CI95% 1.31–4.85; p=0.0051). Additional complexity did not improve optimal model fit and effect modification was not pursued. In sensitivity analyses removing data points with excessive spatial uncertainty or restricting data points only to areas with high probability of macaque occurrence, evidence was consistently found that tree canopy cover (5 km) and host species exhibit a strong positive association with prevalence of P. knowlesi in NHP (Appendix 6). Final adjusted OR for the full multivariable model can be visualised in Figure 3.

Figure 3

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Land use and land cover change is widely linked to spillover of zoonotic pathogens from sylvatic reservoirs into human populations, and pathogen prevalence in wildlife host species is key in driving the force of infection in spillover events. Our initial analyses found that for Plasmodium knowlesi, there is substantial spatial heterogeneity and prevalence in non-human primates varies markedly between regions of Southeast Asia (Zhang et al., 2016). Consistent with our hypothesis that parasite density in primate hosts would be higher in areas experiencing habitat disturbance, we identified strong links between P. knowlesi in NHPs and measures of contemporaneous tree cover and habitat fragmentation. To our knowledge, this is the first systematic study to find evidence of landscape influencing the distribution of P. knowlesi prevalence in NHPs. Results offer evidence that P. knowlesi infection rates in NHPs are linked to changes in landscape across broad spatial scales, and that prevalence of P. knowlesi in reservoir species may be driving spillover risk across Southeast Asia. These findings could provide insight to improving surveillance of P. knowlesi and to the development of ecologically targeted interventions.

While previous studies have estimated that P. knowlesi infection would be chronic in all macaques, or as high as 50–90% for modelling P. knowlesi transmission in Malaysia (Brock et al., 2016), this data strongly suggests that this is not the case. Overall prevalence of P. knowlesi infection in all NHPs is markedly lower than usual estimates, emphasising the importance of accounting for absence data in estimations of prevalence. Considerable heterogeneity was identified between and within regional estimates for P. knowlesi across Southeast Asia, which likely reflects genuine differences according to distinct climates and habitats (Shearer et al., 2016). Malaysian Borneo was found to have an estimated prevalence over five-fold higher than West Malaysia. Crucially, such extreme prevalence estimates for NHPs in Borneo align with the known hotspot for human incidence of P. knowlesi (Cooper et al., 2020). By comparison, for Peninsular Malaysia, estimated prevalence is far lower than anticipated. Cases of human P. knowlesi do occur in West Malaysia, although transmission has been found to exhibit spatial clustering (Phang et al., 2020) which may correspond to pockets of high risk within the wider context of low prevalence of P. knowlesi in macaque populations. Regional trends in P. knowlesi also mask differences in infection rates between sample locations, driven by more localised factors. Multiple studies reported finding P. knowlesi infections in wild macaques to be low or absent in peri-domestic or urbanised areas, attributed to the absence of vector species typically found in forest fringes (Brant et al., 2016; Chua et al., 2019; Manin et al., 2016). This pattern is seen in reports from Peninsular Malaysia (Saleh Huddin et al., 2019; Vythilingam et al., 2008), Singapore (Jeslyn et al., 2011; Li et al., 2021b), and Thailand (Fungfuang et al., 2020; Putaporntip et al., 2010). The high heterogeneity of reports here suggests that the picture is even more complex. P. knowlesi infections may even vary between troops within a single study site, as was seen in the Philippines (Gamalo et al., 2019). Fine-scale interactions are unlikely to be captured by the scale of this study.

Ecological processes determining P. knowlesi infection are influenced by dynamic variables over multiple spatial scales (Cohen et al., 2016). We utilised a data-driven methodology to select variables at distances that capture maximum impact on P. knowlesi prevalence (Byrne et al., 2021; Fornace et al., 2019b), with tree cover and fragmentation influential at localised scales and human population density also exerting influence within wider radii. Contrary to previous studies on risk factors for human incidence of P. knowlesi (Fornace et al., 2019b; Fornace et al., 2016), elevation was not found to be associated with P. knowlesi in NHPs at any scale. Vector and host species composition vary substantially across tropical ecotones, and it is likely that the study extent encompasses a range of putative vectors across different landscapes, such as those of the Minimus Complex in northern regions (Parker et al., 2015) or the recently incriminated An.-collessi and An.-roperi from the Umbrosus Group (De Ang et al., 2021). Given that the vector species driving sylvatic transmission remain elusive, it is conceivable that the elevation range covers multiple vector and host species niches and explains the lack of observed relationship between elevation and P. knowlesi in NHPs. Human population density was found to be significant at multiple distances, with contrasting effects on parasite prevalence in NHP. Previous studies have found a negative association between human density and vector density and biting rates in forested landscapes (Fornace et al., 2019a). Across wide spatial scales, increased vector density in less populated, more forested areas could generate higher parasite prevalence in NHPs. At the same time Long-tailed macaques, a species shown here to have higher prevalence rates, are notorious as nuisance animals and many of the available samples were collected opportunistically in urban areas, which might underly the observed positive association between localised high human density and higher prevalence in NHP. Whilst more data would be needed to understand this interaction, this further demonstrates the importance of using approaches to identify disease dynamics across multiple spatial scales (Brock et al., 2019).

A key finding is the link between high prevalence of P. knowlesi in primate host species with high degrees of habitat fragmentation. Habitat fragmentation is a key aspect of landscape modification, where large contiguous areas of habitat (for example, forests) are broken into a mosaic of smaller patches. This disturbs the ecological structure by increasing the density of fringes or ‘edges’, dynamic habitat often at the boundaries between natural ecosystems and human-modified landscapes (Borremans et al., 2019). Other studies have linked habitat fragmentation to increased generalist parasite density in primates. In Uganda, a higher prevalence and infection risk of protozoal parasites was observed in wild populations of red colobus primates (Procolobus rufomitratus) inhabiting fragmented forests compared to those in undisturbed habitat (Gillespie and Chapman, 2008). For P. knowlesi, creation of edge habitat is thought to favour vectors of the Leucosphyrus Complex (Davidson et al., 2019a; Hawkes et al., 2019). Anopheles spp. presence can be predicted by indices of fragmentation in Sabah, Borneo, with land cover changes creating more suitable micro-climate for larval habitats (Byrne et al., 2021), and an increased abundance of An. balabacensis found in forest fringes (Hawkes et al., 2019; Wong et al., 2015). Increasing landscape complexity results in increased density of edge habitat, with conceivably higher density of vectors in forest fringes. Therefore, preferential use of fringe habitat and high exposure to vectors in forest fringes may contribute to higher conspecific transmission of P. knowlesi between primates in increasingly fragmented habitats. This finding also lends clarity to landscape fragmentation as a risk factor for human exposure to P. knowlesi in Malaysian Borneo (Brock et al., 2019; Fornace et al., 2019b), with changes in relative host density, vector density and wildlife parasite prevalence in nascent forest fringes potentially enhancing the spillover of this disease system into human populations in fragmented habitats.

Conversely, we saw a strong association between high parasite prevalence and high tree canopy coverage. Given that a strong inverse relationship with fragmentation was observed, with high tree density correlating to low fragmentation indices and vice versa, this speaks to a trade-off between dense canopy cover and high habitat complexity and suggests an ‘ideal’ amount of habitat fragmentation that facilitates prevalence in primate hosts. For animals with larger home ranges, individual-based disease models combined with movement ecology approaches have shown that the most highly fragmented areas are less favourable for maintaining parasite transmission (White et al., 2018). In Sabah, individual macaques were shown to increase ranging behaviour in response to deforestation (Stark et al., 2019). Forest edge density also peaks at intermediate levels of land conversion (Borremans et al., 2019). With smaller habitat patches in maximally fragmented landscapes potentially insufficient to support macaque troops, this interplay between disease ecology and metapopulation theory may explain why both tree density and habitat fragmentation appear to pose a greater risk for simian P. knowlesi. Likewise, this may relate to the finding that in Borneo, larger forest patches (lower fragmentation indices) were associated with P. knowlesi spillover in Borneo (Fornace et al., 2019b). Overall, this finding offers an insight to mechanisms that underpin the increased force of infection of P. knowlesi that is associated with landscape change.

There are limitations to consider in the available data and interpretation of these findings. ‘Small-study effects’ were observed in the dataset, suggestive of a bias toward positive effect estimates (Stewart et al., 2012). This may be a result of data disaggregation and small studies creating artefactually higher estimates or may reflect true bias in data collection toward areas known to be endemic for P. knowlesi and convenience sampling of macaques. Assumptions have also been made that sample site equates to habitat, which may not reflect actual habitat use, and even accurate georeferenced data points are unlikely to entirely reflect surrounding habitat within the macaque home range. Variability in study designs and data reporting also impacted geospatial accuracy. Steps were taken to account for spatial bias by extracting covariates at randomly generated pseudo-sampling points. Whilst uncertainty cannot be eliminated, we demonstrate a robust methodology to accommodate for geographical uncertainty in ecological studies. Future investigations should prioritise systematic, georeferenced sampling across a range of landscape scenarios.

Results show important regional ecological trends, but broad geographic patterns may not be generalisable at individual levels, or to all putative host species in all geographic contexts (Zhang et al., 2016). Follow-up studies should be conducted at higher spatial and temporal resolution to characterise the effect of local landscape configuration on wildlife P. knowlesi prevalence. Effects of fragmentation are likely to be dependent on land conversion type, species composition and surrounding matrix habitat (Fornace et al., 2019b). Use of perimeter: area ratio (PARA) as a fragmentation index was justified given high canopy coverage in study sites (Wang et al., 2014), although Edge Density (ED) or normalised Landscape Shape Index (nLSI) might be more appropriate in future analyses to account for variation in forest abundance. Specific land configurations have previously been linked to P. knowlesi exposure in Borneo (Fornace et al., 2019b), notably in areas where palm oil plantation is a dominant industry. Given this, broad forest classifications used here may mask important differences in P. knowlesi prevalence between land classes. As it was not possible to include contemporary land cover classifications in this analysis, future studies would also benefit from looking at specific habitat type (e.g., primary forest, agroforest, plantation).

All data of P. knowlesi prevalence in non-human primates were sourced from systematic literature review of publicly available research articles. Published primate datasets are available in Ruiz Cuenca et al., 2022 and Shahar, 2019. Full reference list for all publicly available primate infection data including details of individual articles and data types are available in Appendix 1. Earth Observation (EO) data were derived from open-source environmental datasets. References, links and details of scale and resolution and manipulation are provided in manuscript (Table 1 in Results and details in Methods) with further information provided in the Appendix 4. Example code for manipulation of environmental data and data analysis are available on an associated GitHub repository (copy archived at Johnson, 2024).

1. 1. Akter R
   2. Vythilingam I
   3. Khaw LT
   4. Qvist R
   5. Lim Y
   6. Sitam FT
   7. Venugopalan B
   8. Sekaran SD
   9. Vythilingam I
   10. Khaw LT
   11. Lim Y
   12. Sitam FT
   13. Venugopalan B
   14. Sekaran SD
   15. Qvist R
   16. Lim Y
   17. Sitam FT
   18. Venugopalan B
   19. Sekaran SD
   20. Vythilingam I
   21. Khaw LT
   22. Lim Y
   23. Sitam FT
   24. Venugopalan B
   25. Sekaran SD

   (2015) Simian malaria in wild macaques: first report from Hulu Selangor district, Selangor, Malaysia

   Malaria Journal 14:386.

   https://doi.org/10.1186/s12936-015-0856-3

   • PubMed
   • Google Scholar
2. 1. Amir A
   2. Shahari S
   3. Liew JWK
   4. de Silva JR
   5. Khan MB
   6. Lai MY
   7. Snounou G
   8. Abdullah ML
   9. Gani M
   10. Rovie-Ryan JJ
   11. Lau Y-L

   (2020) Natural Plasmodium infection in wild macaques of three states in peninsular Malaysia

   Acta Tropica 211:105596.

   https://doi.org/10.1016/j.actatropica.2020.105596

   • Google Scholar
3. 1. Antinori S
   2. Galimberti L
   3. Milazzo L
   4. Corbellino M

   (2013) Plasmodium knowlesi: the emerging zoonotic malaria parasite

   Acta Tropica 125:191–201.

   https://doi.org/10.1016/j.actatropica.2012.10.008

   • PubMed
   • Google Scholar
4. 1. Borremans B
   2. Faust C
   3. Manlove KR
   4. Sokolow SH
   5. Lloyd-Smith JO

   (2019) Cross-species pathogen spillover across ecosystem boundaries: mechanisms and theory

   Philosophical Transactions of the Royal Society B 374:20180344.

   https://doi.org/10.1098/rstb.2018.0344

   • Google Scholar
5. Book

   1. Brant HL

   (2011)

   Changes in Abundance, Diversity and Community Composition of Mosquitoes Based on Different Land Use in Sabah, Malaysia

   Imperial College London.

   • Google Scholar
6. 1. Brant HL
   2. Ewers RM
   3. Vythilingam I
   4. Drakeley C
   5. Benedick S
   6. Mumford JD

   (2016) Vertical stratification of adult mosquitoes (Diptera: Culicidae) within a tropical rainforest in Sabah, Malaysia

   Malaria Journal 15:370.

   https://doi.org/10.1186/s12936-016-1416-1

   • PubMed
   • Google Scholar
7. 1. Brock PM
   2. Fornace KM
   3. Parmiter M
   4. Cox J
   5. Drakeley CJ
   6. Ferguson HM
   7. Kao RR

   (2016) Plasmodium knowlesi transmission: integrating quantitative approaches from epidemiology and ecology to understand malaria as a zoonosis

   Parasitology 143:389–400.

   https://doi.org/10.1017/S0031182015001821

   • PubMed
   • Google Scholar
8. 1. Brock PM
   2. Fornace KM
   3. Grigg MJ
   4. Anstey NM
   5. William T
   6. Cox J
   7. Drakeley CJ
   8. Ferguson HM
   9. Kao RR
   10. Fornace KM
   11. Cox J
   12. Drakeley CJ
   13. Grigg MJ
   14. Anstey NM
   15. William T
   16. Kao RR

   (2019) Predictive analysis across spatial scales links zoonotic malaria to deforestation

   Proceedings. Biological Sciences 286:20182351.

   https://doi.org/10.1098/rspb.2018.2351

   • PubMed
   • Google Scholar
9. Software

   1. Buchhorn M
   2. Smets B
   3. Bertels L

   (2020) Copernicus global land service: land cover 100m: collection 3: epoch 2019: globe, version Version V3.0.1

   Zenodo.

   https://zenodo.org/records/3939050
10. 1. Byrne I
    2. Aure W
    3. Manin BO
    4. Vythilingam I
    5. Ferguson HM
    6. Drakeley CJ
    7. Chua TH
    8. Fornace KM

    (2021) Environmental and spatial risk factors for the larval habitats of Plasmodium knowlesi vectors in Sabah, Malaysian Borneo

    Scientific Reports 11:11810.

    https://doi.org/10.1038/s41598-021-90893-1

    • PubMed
    • Google Scholar
11. 1. Chang Q
    2. Sun X
    3. Wang J
    4. Yin J
    5. Song J
    6. Peng S
    7. Lu H
    8. Zhou H
    9. Jiang N
    10. Chen Q

    (2011) Identification of Hepatocystis species in a macaque monkey in northern Myanmar

    Research and Reports in Tropical Medicine 2:141–146.

    https://doi.org/10.2147/RRTM.S27182

    • PubMed
    • Google Scholar
12. 1. Cheng Y
    2. Tjaden NB
    3. Jaeschke A
    4. Thomas SM
    5. Beierkuhnlein C

    (2021) Using centroids of spatial units in ecological niche modelling: Effects on model performance in the context of environmental data grain size

    Global Ecology and Biogeography 30:611–621.

    https://doi.org/10.1111/geb.13240

    • Google Scholar
13. 1. Chua TH
    2. Manin BO
    3. Vythilingam I
    4. Fornace K
    5. Drakeley CJ

    (2019) Effect of different habitat types on abundance and biting times of Anopheles balabacensis Baisas (Diptera: Culicidae) in Kudat district of Sabah, Malaysia

    Parasites & Vectors 12:364.

    https://doi.org/10.1186/s13071-019-3627-0

    • PubMed
    • Google Scholar
14. 1. Cohen JM
    2. Civitello DJ
    3. Brace AJ
    4. Feichtinger EM
    5. Ortega CN
    6. Richardson JC
    7. Sauer EL
    8. Liu X
    9. Rohr JR

    (2016) Spatial scale modulates the strength of ecological processes driving disease distributions

    PNAS 113:.

    https://doi.org/10.1073/pnas.1521657113

    • Google Scholar
15. 1. Cooper DJ
    2. Rajahram GS
    3. William T
    4. Jelip J
    5. Mohammad R
    6. Benedict J
    7. Alaza DA
    8. Malacova E
    9. Yeo TW
    10. Grigg MJ
    11. Anstey NM
    12. Barber BE

    (2020) Plasmodium knowlesi Malaria in Sabah, Malaysia, 2015-2017: Ongoing Increase in Incidence Despite Near-elimination of the Human-only Plasmodium Species

    Clinical Infectious Diseases 70:361–367.

    https://doi.org/10.1093/cid/ciz237

    • PubMed
    • Google Scholar
16. 1. Davidson G
    2. Chua TH
    3. Cook A
    4. Speldewinde P
    5. Weinstein P

    (2019a) The Role of Ecological Linkage Mechanisms in Plasmodium knowlesi Transmission and Spread

    EcoHealth 16:594–610.

    https://doi.org/10.1007/s10393-019-01395-6

    • PubMed
    • Google Scholar
17. 1. Davidson G
    2. Chua TH
    3. Cook A
    4. Speldewinde P
    5. Weinstein P

    (2019b) Defining the ecological and evolutionary drivers of Plasmodium knowlesi transmission within a multi-scale framework

    Malaria Journal 18:66.

    https://doi.org/10.1186/s12936-019-2693-2

    • PubMed
    • Google Scholar
18. 1. De Ang JX
    2. Yaman K
    3. Kadir KA
    4. Matusop A
    5. Singh B

    (2021) New vectors that are early feeders for Plasmodium knowlesi and other simian malaria parasites in Sarawak, Malaysian Borneo

    Scientific Reports 11:7739.

    https://doi.org/10.1038/s41598-021-86107-3

    • PubMed
    • Google Scholar
19. 1. Faust CL
    2. McCallum HI
    3. Bloomfield LSP
    4. Gottdenker NL
    5. Gillespie TR
    6. Torney CJ
    7. Dobson AP
    8. Plowright RK

    (2018) Pathogen spillover during land conversion

    Ecology Letters 21:471–483.

    https://doi.org/10.1111/ele.12904

    • PubMed
    • Google Scholar
20. 1. Fornace KM
    2. Abidin TR
    3. Alexander N
    4. Brock P
    5. Grigg MJ
    6. Murphy A
    7. William T
    8. Menon J
    9. Drakeley CJ
    10. Cox J

    (2016) Association between Landscape Factors and Spatial Patterns of Plasmodium knowlesi Infections in Sabah, Malaysia

    Emerging Infectious Diseases 22:201–208.

    https://doi.org/10.3201/eid2202.150656

    • PubMed
    • Google Scholar
21. 1. Fornace KM
    2. Herman LS
    3. Abidin TR
    4. Chua TH
    5. Daim S
    6. Lorenzo PJ
    7. Grignard L
    8. Nuin NA
    9. Ying LT
    10. Grigg MJ
    11. William T
    12. Espino F
    13. Cox J
    14. Tetteh KKA
    15. Drakeley CJ

    (2018) Exposure and infection to Plasmodium knowlesi in case study communities in Northern Sabah, Malaysia and Palawan, The Philippines

    PLOS Neglected Tropical Diseases 12:e0006432.

    https://doi.org/10.1371/journal.pntd.0006432

    • PubMed
    • Google Scholar
22. 1. Fornace KM
    2. Alexander N
    3. Abidin TR
    4. Brock PM
    5. Chua TH
    6. Vythilingam I
    7. Ferguson HM
    8. Manin BO
    9. Wong ML
    10. Ng SH
    11. Cox J
    12. Drakeley C

    (2019a) Local human movement patterns and land use impact exposure to zoonotic malaria in Malaysian Borneo

    eLife 8:e47602.

    https://doi.org/10.7554/eLife.47602

    • PubMed
    • Google Scholar
23. 1. Fornace KM
    2. Brock PM
    3. Abidin TR
    4. Grignard L
    5. Herman LS
    6. Chua TH
    7. Daim S
    8. William T
    9. Patterson C
    10. Hall T
    11. Grigg MJ
    12. Anstey NM
    13. Tetteh KKA
    14. Cox J
    15. Drakeley CJ

    (2019b) Environmental risk factors and exposure to the zoonotic malaria parasite Plasmodium knowlesi across northern Sabah, Malaysia: a population-based cross-sectional survey

    The Lancet. Planetary Health 3:e179–e186.

    https://doi.org/10.1016/S2542-5196(19)30045-2

    • PubMed
    • Google Scholar
24. 1. Fornace KM
    2. Diaz AV
    3. Lines J
    4. Drakeley CJ

    (2021) Achieving global malaria eradication in changing landscapes

    Malaria Journal 20:69.

    https://doi.org/10.1186/s12936-021-03599-0

    • Google Scholar
25. Preprint

    1. Fornace K
    2. Topazian H
    3. Routledge I
    4. Asyraf S
    5. Jelip J
    6. Lindblade K
    7. Jeffree MS
    8. Cuenca PR
    9. Bhatt S
    10. Ahmed K
    11. Ghani AC
    12. Drakeley C

    (2022) No Evidence of Sustained Nonzoonotic Plasmodium knowlesi Transmission: Estimating Species-Specific Spatiotemporally Varying Reproduction Numbers for Malaria in Malaysia

    Nature Portfolio.

    https://doi.org/10.21203/rs.3.rs-2278329/v1

    • Google Scholar
26. 1. Fornace KM
    2. Topazian HM
    3. Routledge I
    4. Asyraf S
    5. Jelip J
    6. Lindblade KA
    7. Jeffree MS
    8. Ruiz Cuenca P
    9. Bhatt S
    10. Ahmed K
    11. Ghani AC
    12. Drakeley C

    (2023) No evidence of sustained nonzoonotic Plasmodium knowlesi transmission in Malaysia from modelling malaria case data

    Nature Communications 14:2945.

    https://doi.org/10.1038/s41467-023-38476-8

    • PubMed
    • Google Scholar
27. 1. Fungfuang W
    2. Udom C
    3. Tongthainan D
    4. Kadir KA
    5. Singh B

    (2020) Malaria parasites in macaques in Thailand: stump-tailed macaques (Macaca arctoides) are new natural hosts for Plasmodium knowlesi, Plasmodium inui, Plasmodium coatneyi and Plasmodium fieldi

    Malaria Journal 19:350.

    https://doi.org/10.1186/s12936-020-03424-0

    • PubMed
    • Google Scholar
28. 1. Gamalo LE
    2. Dimalibot J
    3. Kadir KA
    4. Singh B
    5. Paller VG

    (2019) Plasmodium knowlesi and other malaria parasites in long-tailed macaques from the Philippines

    Malaria Journal 18:147.

    https://doi.org/10.1186/s12936-019-2780-4

    • PubMed
    • Google Scholar
29. 1. Ghinai I
    2. Cook J
    3. Hla TTW
    4. Htet HMT
    5. Hall T
    6. Lubis IND
    7. Ghinai R
    8. Hesketh T
    9. Naung Y
    10. Lwin MM
    11. Latt TS
    12. Heymann DL
    13. Sutherland CJ
    14. Drakeley C
    15. Field N

    (2017) Malaria epidemiology in central Myanmar: identification of a multi-species asymptomatic reservoir of infection

    Malaria Journal 16:16.

    https://doi.org/10.1186/s12936-016-1651-5

    • PubMed
    • Google Scholar
30. 1. Gilhooly L
    2. McIntyre A
    3. Grigg M
    4. Fornace K
    5. Cox J
    6. Drakeley C
    7. Lynn MS
    8. Stark D

    (2016)

    The Monkeybar Project: population density of long-tailed macaques (Macaca fascicularis) in two different forest types in Kudat District, Sabah, Malaysia

    American Journal of Physical Anthropology 159:155.

    • Google Scholar
31. 1. Gillespie TR
    2. Chapman CA

    (2008) Forest fragmentation, the decline of an endangered primate, and changes in host-parasite interactions relative to an unfragmented forest

    American Journal of Primatology 70:222–230.

    https://doi.org/10.1002/ajp.20475

    • PubMed
    • Google Scholar
32. 1. Grigg MJ
    2. Cox J
    3. William T
    4. Jelip J
    5. Fornace KM
    6. Brock PM
    7. von Seidlein L
    8. Barber BE
    9. Anstey NM
    10. Yeo TW
    11. Drakeley CJ

    (2017) Individual-level factors associated with the risk of acquiring human Plasmodium knowlesi malaria in Malaysia: a case-control study

    The Lancet. Planetary Health 1:e97–e104.

    https://doi.org/10.1016/S2542-5196(17)30031-1

    • PubMed
    • Google Scholar
33. 1. Hansen MC
    2. Potapov PV
    3. Moore R
    4. Hancher M
    5. Turubanova SA
    6. Tyukavina A
    7. Thau D
    8. Stehman SV
    9. Goetz SJ
    10. Loveland TR
    11. Kommareddy A
    12. Egorov A
    13. Chini L
    14. Justice CO
    15. Townshend JRG

    (2013) High-resolution global maps of 21st-century forest cover change

    Science 342:850–853.

    https://doi.org/10.1126/science.1244693

    • PubMed
    • Google Scholar
34. 1. Hanski I
    2. Ovaskainen O

    (2000) The metapopulation capacity of a fragmented landscape

    Nature 404:755–758.

    https://doi.org/10.1038/35008063

    • PubMed
    • Google Scholar
35. 1. Hawkes FM
    2. Manin BO
    3. Cooper A
    4. Daim S
    5. R H
    6. Jelip J
    7. Husin T
    8. Chua TH

    (2019) Vector compositions change across forested to deforested ecotones in emerging areas of zoonotic malaria transmission in Malaysia

    Scientific Reports 9:13312.

    https://doi.org/10.1038/s41598-019-49842-2

    • PubMed
    • Google Scholar
36. Conference

    1. Ho GC
    2. Lee CL
    3. Sharma RSK

    (2010)

    Association of Institutions for Tropical Veterinary Medicine (AITVM)

    14th International Conference of the Association of Institutions for Tropical Veterinary Medicine.

    • Google Scholar
37. 1. Ilacqua RC
    2. Medeiros-Sousa AR
    3. Ramos DG
    4. Obara MT
    5. Ceretti-Junior W
    6. Mucci LF
    7. Marrelli MT
    8. Laporta GZ

    (2021) Reemergence of yellow fever in Brazil: The role of distinct landscape fragmentation thresholds

    Journal of Environmental and Public Health 2021:8230789.

    https://doi.org/10.1155/2021/8230789

    • PubMed
    • Google Scholar
38. 1. Imai N
    2. Furukawa T
    3. Tsujino R
    4. Kitamura S
    5. Yumoto T

    (2018) Factors affecting forest area change in Southeast Asia during 1980-2010

    PLOS ONE 13:e0197391.

    https://doi.org/10.1371/journal.pone.0197391

    • PubMed
    • Google Scholar
39. Software

    1. Jarvis A
    2. Reuter HI
    3. A. Nelson EG

    (2008) Hole-filled SRTM for the globe: version 4: data grid, version Version 4

    SRTM Data.

    http://srtm.csi.cgiar.org
40. 1. Jeslyn WPS
    2. Huat TC
    3. Vernon L
    4. Irene LMZ
    5. Sung LK
    6. Jarrod LP
    7. Singh B
    8. Ching NL
    9. Huat TC
    10. Singh B
    11. Vernon L
    12. Jarrod LP
    13. Irene LMZ
    14. Sung LK
    15. Jarrod LP
    16. Singh B
    17. Ching NL
    18. Huat TC
    19. Singh B
    20. Vernon L
    21. Jarrod LP

    (2011) Molecular epidemiological investigation of Plasmodium knowlesi in humans and macaques in Singapore

    Vector Borne and Zoonotic Diseases 11:131–135.

    https://doi.org/10.1089/vbz.2010.0024

    • PubMed
    • Google Scholar
41. 1. Jeyaprakasam NK
    2. Liew JWK
    3. Low VL
    4. Wan-Sulaiman WY
    5. Vythilingam I

    (2020) Plasmodium knowlesi infecting humans in Southeast Asia: What’s next?

    PLOS Neglected Tropical Diseases 14:e0008900.

    https://doi.org/10.1371/journal.pntd.0008900

    • PubMed
    • Google Scholar
42. Software

    1. Johnson E

    (2024) KnowlesiFrag, version swh:1:rev:6eecb0460cd61c26a79413bf91654557eff1380c

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:740d27b06ea123bd194f817f1ed0a561bf173aa5;origin=https://github.com/EmiliJohnson/KnowlesiFrag;visit=swh:1:snp:b85e05b75cb6399e68d2b1667abcd1ca61cdb99e;anchor=swh:1:rev:6eecb0460cd61c26a79413bf91654557eff1380c
43. 1. Kaewchot S
    2. Tangsudjai S
    3. Sariya L
    4. Mongkolphan C
    5. Saechin A
    6. Sariwongchan R
    7. Panpeth N
    8. Thongsahuan S
    9. Suksai P

    (2022) Zoonotic pathogens survey in free-living long-tailed macaques in Thailand

    International Journal of Veterinary Science and Medicine 10:11–18.

    https://doi.org/10.1080/23144599.2022.2040176

    • PubMed
    • Google Scholar
44. 1. Kalluri S
    2. Gilruth P
    3. Rogers D
    4. Szczur M

    (2007) Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: A review

    PLOS Pathogens 3:e116.

    https://doi.org/10.1371/journal.ppat.0030116

    • Google Scholar
45. 1. Koh GJN
    2. Ismail PK
    3. Koh D

    (2019) Occupationally Acquired Plasmodium knowlesi Malaria in Brunei Darussalam

    Safety and Health at Work 10:122–124.

    https://doi.org/10.1016/j.shaw.2018.09.002

    • Google Scholar
46. 1. Lee KS
    2. Divis PCS
    3. Zakaria SK
    4. Matusop A
    5. Julin RA
    6. Conway DJ
    7. Cox-Singh J
    8. Singh B

    (2011) Plasmodium knowlesi: Reservoir Hosts and Tracking the Emergence in Humans and Macaques

    PLOS Pathog 7:e1002015.

    https://doi.org/10.1371/journal.ppat.1002015

    • Google Scholar
47. 1. Lehman SM
    2. Rajaonson A
    3. Day S

    (2006) Edge effects and their influence on lemur density and distribution in Southeast Madagascar

    American Journal of Physical Anthropology 129:232–241.

    https://doi.org/10.1002/ajpa.20241

    • PubMed
    • Google Scholar
48. 1. Li J
    2. Jia K
    3. Liu Y
    4. Yuan B
    5. Xia M
    6. Zhao W

    (2021a) Spatiotemporal distribution of zika virus and its spatially heterogeneous relationship with the environment

    International Journal of Environmental Research and Public Health 18:290.

    https://doi.org/10.3390/ijerph18010290

    • Google Scholar
49. 1. Li MI
    2. Mailepessov D
    3. Vythilingam I
    4. Lee V
    5. Lam P
    6. Ng LC
    7. Tan CH

    (2021b) Prevalence of simian malaria parasites in macaques of Singapore

    PLOS Neglected Tropical Diseases 15:e0009110.

    https://doi.org/10.1371/journal.pntd.0009110

    • PubMed
    • Google Scholar
50. 1. Liberati A
    2. Altman DG
    3. Tetzlaff J
    4. Mulrow C
    5. Gøtzsche PC
    6. Ioannidis JPA
    7. Clarke M
    8. Devereaux PJ
    9. Kleijnen J
    10. Moher D

    (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration

    PLOS Medicine 6:e1000100.

    https://doi.org/10.1371/journal.pmed.1000100

    • PubMed
    • Google Scholar
51. 1. Lin L
    2. Chu H

    (2018) Quantifying publication bias in meta-analysis

    Biometrics 74:785–794.

    https://doi.org/10.1111/biom.12817

    • PubMed
    • Google Scholar
52. 1. Link A
    2. de Luna A
    3. Alfonso F
    4. Giraldo-Beltran P
    5. Ramirez F

    (2010) Initial effects of fragmentation on the density of three neotropical primate species in two lowland forests of Colombia

    Endangered Species Research 13:41–50.

    https://doi.org/10.3354/esr00312

    • Google Scholar
53. 1. Loh EH
    2. Murray KA
    3. Nava A
    4. Daszak P

    (2016)

    Evaluating the links between biodiversity, land-use change, and infectious disease emergence

    Tropical Conservation pp. 79–88.

    • Google Scholar
54. 1. Maeno Y
    2. Quang NT
    3. Culleton R
    4. Kawai S
    5. Masuda G
    6. Nakazawa S
    7. Marchand RP

    (2015) Humans frequently exposed to a range of non-human primate malaria parasite species through the bites of Anopheles dirus mosquitoes in South-central Vietnam

    Parasites & Vectors 8:376.

    https://doi.org/10.1186/s13071-015-0995-y

    • PubMed
    • Google Scholar
55. 1. Manin BO
    2. Ferguson HM
    3. Vythilingam I
    4. Fornace K
    5. William T
    6. Torr SJ
    7. Drakeley C
    8. Chua TH
    9. Ferguson HM
    10. Vythilingam I
    11. Fornace K
    12. Drakeley C
    13. William T
    14. Torr SJ
    15. Drakeley C
    16. Chua TH
    17. Ferguson HM
    18. Vythilingam I
    19. Fornace K
    20. Drakeley C
    21. William T
    22. Torr SJ
    23. Drakeley C
    24. Chua TH

    (2016) Investigating the contribution of peri-domestic transmission to risk of zoonotic malaria infection in humans

    PLOS Neglected Tropical Diseases 10:e0005064.

    https://doi.org/10.1371/journal.pntd.0005064

    • PubMed
    • Google Scholar
56. 1. McFarlane R
    2. Sleigh A
    3. McMichael T

    (2012) Synanthropy of wild mammals as a determinant of emerging infectious diseases in the Asian-Australasian region

    EcoHealth 9:24–35.

    https://doi.org/10.1007/s10393-012-0763-9

    • PubMed
    • Google Scholar
57. Software

    1. McGarigal K
    2. Cushman SA
    3. Ene E

    (2021) FRAGSTATS V4: spatial pattern analysis program for categorical maps

    Fragstats.

    https://www.fragstats.org
58. 1. Moyes CL
    2. Shearer FM
    3. Huang Z
    4. Wiebe A
    5. Gibson HS
    6. Nijman V
    7. Mohd-Azlan J
    8. Brodie JF
    9. Malaivijitnond S
    10. Linkie M
    11. Samejima H
    12. O’Brien TG
    13. Trainor CR
    14. Hamada Y
    15. Giordano AJ
    16. Kinnaird MF
    17. Elyazar IRF
    18. Sinka ME
    19. Vythilingam I
    20. Bangs MJ
    21. Pigott DM
    22. Weiss DJ
    23. Golding N
    24. Hay SI

    (2016) Predicting the geographical distributions of the macaque hosts and mosquito vectors of Plasmodium knowlesi malaria in forested and non-forested areas

    Parasites & Vectors 9:242.

    https://doi.org/10.1186/s13071-016-1527-0

    • PubMed
    • Google Scholar
59. 1. Muehlenbein MP
    2. Pacheco MA
    3. Taylor JE
    4. Prall SP
    5. Ambu L
    6. Nathan S
    7. Alsisto S
    8. Ramirez D
    9. Escalante AA

    (2015) Accelerated diversification of nonhuman primate malarias in Southeast Asia: Adaptive radiation or geographic speciation?

    Molecular Biology and Evolution 32:422–439.

    https://doi.org/10.1093/molbev/msu310

    • Google Scholar
60. 1. Munn Z
    2. Moola S
    3. Lisy K
    4. Riitano D
    5. Tufanaru C

    (2015) Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data

    International Journal of Evidence-Based Healthcare 13:147–153.

    https://doi.org/10.1097/XEB.0000000000000054

    • PubMed
    • Google Scholar
61. 1. Murray KA
    2. Daszak P

    (2013) Human ecology in pathogenic landscapes: two hypotheses on how land use change drives viral emergence

    Current Opinion in Virology 3:79–83.

    https://doi.org/10.1016/j.coviro.2013.01.006

    • PubMed
    • Google Scholar
62. 1. Nada-Raja T
    2. Kadir KA
    3. Divis PCS
    4. Mohamad DSA
    5. Matusop A
    6. Singh B

    (2022) Macaca fascicularis and Macaca nemestrina infected with zoonotic malaria parasites are widely distributed in Sarawak, Malaysian Borneo

    Scientific Reports 12:10476.

    https://doi.org/10.1038/s41598-022-14560-9

    • PubMed
    • Google Scholar
63. 1. Nunn CL
    2. Dokey ATW

    (2006) Ranging patterns and parasitism in primates

    Biology Letters 2:351–354.

    https://doi.org/10.1098/rsbl.2006.0485

    • PubMed
    • Google Scholar
64. 1. Parker DM
    2. Carrara VI
    3. Pukrittayakamee S
    4. McGready R
    5. Nosten FH

    (2015) Malaria ecology along the Thailand-Myanmar border

    Malaria Journal 14:388.

    https://doi.org/10.1186/s12936-015-0921-y

    • PubMed
    • Google Scholar
65. 1. Phang WK
    2. Hamid MHA
    3. Jelip J
    4. Mudin RN
    5. Chuang TW
    6. Lau YL
    7. Fong MY

    (2020) Spatial and Temporal Analysis of Plasmodium knowlesi Infection in Peninsular Malaysia, 2011 to 2018

    International Journal of Environmental Research and Public Health 17:9271.

    https://doi.org/10.3390/ijerph17249271

    • PubMed
    • Google Scholar
66. 1. Potapov P
    2. Yaroshenko A
    3. Turubanova S
    4. Dubinin M
    5. Laestadius L
    6. Thies C
    7. Aksenov D
    8. Egorov A
    9. Yesipova Y
    10. Glushkov I
    11. Karpachevskiy M
    12. Kostikova A
    13. Manisha A
    14. Tsybikova E
    15. Zhuravleva I

    (2008) Mapping the world’s intact forest landscapes by remote sensing

    Ecology and Society 13:.

    https://doi.org/10.5751/ES-02670-130251

    • Google Scholar
67. 1. Putaporntip C
    2. Jongwutiwes S
    3. Thongaree S
    4. Seethamchai S
    5. Grynberg P
    6. Hughes AL

    (2010) Ecology of malaria parasites infecting Southeast Asian macaques: evidence from cytochrome b sequences

    Molecular Ecology 19:3466–3476.

    https://doi.org/10.1111/j.1365-294X.2010.04756.x

    • PubMed
    • Google Scholar
68. Book

    1. Rogerson PA

    (2001) Statistical Methods for Geography

    SAGE.

    https://doi.org/10.4135/9781849209953

    • Google Scholar
69. 1. Ruiz Cuenca P
    2. Key S
    3. Lindblade KA
    4. Vythilingam I
    5. Drakeley C
    6. Fornace K

    (2022) Is there evidence of sustained human-mosquito-human transmission of the zoonotic malaria Plasmodium knowlesi? A systematic literature review

    Malaria Journal 21:1–19.

    https://doi.org/10.1186/s12936-022-04110-z

    • Google Scholar
70. 1. Saleh Huddin A
    2. Md Yusuf NA
    3. Razak M
    4. Ogu Salim N
    5. Hisam S

    (2019) Genetic diversity of Plasmodium knowlesi among human and long-tailed macaque populations in Peninsular Malaysia: The utility of microsatellite markers

    Infection, Genetics and Evolution 75:103952.

    https://doi.org/10.1016/j.meegid.2019.103952

    • PubMed
    • Google Scholar
71. 1. Salwati E
    2. Handayani S
    3. Marleta Dewi R
    4. Kesehatan KR
    5. Litbang BP

    (2017) Kasus Baru Plasmodium knowlesi pada Manusia di Jambi

    Jurnal Biotek Medisiana Indonesia 6:39–51.

    https://doi.org/10.22435/JBMI.V6I1.1684

    • Google Scholar
72. 1. Schwarzer G
    2. Chemaitelly H
    3. Abu-Raddad LJ
    4. Rücker G

    (2019) Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions

    Research Synthesis Methods 10:476–483.

    https://doi.org/10.1002/jrsm.1348

    • PubMed
    • Google Scholar
73. 1. Seethamchai S
    2. Putaporntip C
    3. Jongwutiwes S
    4. Cui L
    5. Malaivijitnond S

    (2008) Malaria and Hepatocystis Species in Wild Macaques, Southern Thailand

    The American Journal of Tropical Medicine and Hygiene 78:646–653.

    https://doi.org/10.4269/ajtmh.2008.78.646

    • Google Scholar
74. 1. Setiadi W
    2. Sudoyo H
    3. Trimarsanto H
    4. Sihite BA
    5. Saragih RJ
    6. Juliawaty R
    7. Wangsamuda S
    8. Asih PBS
    9. Syafruddin D

    (2016) A zoonotic human infection with simian malaria, Plasmodium knowlesi, in Central Kalimantan, Indonesia

    Malaria Journal 15:218.

    https://doi.org/10.1186/s12936-016-1272-z

    • Google Scholar
75. Thesis

    1. Shahar ZS

    (2019)

    Molecular prevalence, spatial distribution and epidemiological risk factors of Plasmodium knowlesi infection among Macaca fascicularis raffles in Peninsular Malaysia

    Universiti Putra Malaysia.

    • Google Scholar
76. 1. Shearer FM
    2. Huang Z
    3. Weiss DJ
    4. Wiebe A
    5. Gibson HS
    6. Battle KE
    7. Pigott DM
    8. Brady OJ
    9. Putaporntip C
    10. Jongwutiwes S
    11. Lau YL
    12. Manske M
    13. Amato R
    14. Elyazar IRF
    15. Vythilingam I
    16. Bhatt S
    17. Gething PW
    18. Singh B
    19. Golding N
    20. Hay SI
    21. Moyes CL

    (2016) Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria

    PLOS Neglected Tropical Diseases 10:e0004915.

    https://doi.org/10.1371/journal.pntd.0004915

    • PubMed
    • Google Scholar
77. 1. Simon JA
    2. Marrotte RR
    3. Desrosiers N
    4. Fiset J
    5. Gaitan J
    6. Gonzalez A
    7. Koffi JK
    8. Lapointe FJ
    9. Leighton PA
    10. Lindsay LR
    11. Logan T
    12. Milord F
    13. Ogden NH
    14. Rogic A
    15. Roy-Dufresne E
    16. Suter D
    17. Tessier N
    18. Millien V

    (2014) Climate change and habitat fragmentation drive the occurrence of Borrelia burgdorferi, the agent of Lyme disease, at the northeastern limit of its distribution

    Evolutionary Applications 7:750–764.

    https://doi.org/10.1111/eva.12165

    • PubMed
    • Google Scholar
78. 1. Singh B
    2. Sung LK
    3. Matusop A
    4. Radhakrishnan A
    5. Shamsul SSG
    6. Cox-Singh J
    7. Thomas A
    8. Conway DJ

    (2004) A large focus of naturally acquired Plasmodium knowlesi infections in human beings

    The Lancet 363:1017–1024.

    https://doi.org/10.1016/S0140-6736(04)15836-4

    • Google Scholar
79. 1. Stark DJ
    2. Fornace KM
    3. Brock PM
    4. Abidin TR
    5. Gilhooly L
    6. Jalius C
    7. Goossens B
    8. Drakeley CJ
    9. Salgado-Lynn M

    (2019) Long-Tailed Macaque Response to Deforestation in a Plasmodium knowlesi-Endemic Area

    EcoHealth 16:638–646.

    https://doi.org/10.1007/s10393-019-01403-9

    • PubMed
    • Google Scholar
80. 1. Stewart GB
    2. Altman DG
    3. Askie LM
    4. Duley L
    5. Simmonds MC
    6. Stewart LA

    (2012) Statistical analysis of individual participant data meta-analyses: A comparison of methods and recommendations for practice

    PLOS ONE 7:e46042.

    https://doi.org/10.1371/journal.pone.0046042

    • PubMed
    • Google Scholar
81. 1. Vaz VC
    2. D’andrea PS
    3. Jansen AM

    (2007) Effects of habitat fragmentation on wild mammal infection by Trypanosoma cruzi

    Parasitology 134:1785–1793.

    https://doi.org/10.1017/S003118200700323X

    • Google Scholar
82. 1. von Hippel PT

    (2015) The heterogeneity statistic I(2) can be biased in small meta-analyses

    BMC Medical Research Methodology 15:35.

    https://doi.org/10.1186/s12874-015-0024-z

    • PubMed
    • Google Scholar
83. 1. Vythilingam I
    2. Tan CH
    3. Asmad M
    4. Chan ST
    5. Lee KS
    6. Singh B

    (2006) Natural transmission of Plasmodium knowlesi to humans by Anopheles latens in Sarawak, Malaysia

    Transactions of the Royal Society of Tropical Medicine and Hygiene 100:1087–1088.

    https://doi.org/10.1016/j.trstmh.2006.02.006

    • Google Scholar
84. 1. Vythilingam I
    2. Noorazian YM
    3. Huat TC
    4. Jiram AI
    5. Yusri YM
    6. Azahari AH
    7. Norparina I
    8. Noorrain A
    9. Lokmanhakim S
    10. Huat TC
    11. Azahari AH
    12. Noorrain A
    13. Lokmanhakim S
    14. Jiram AI
    15. Yusri YM
    16. Azahari AH
    17. Norparina I
    18. Noorrain A
    19. Lokmanhakim S
    20. Huat TC
    21. Azahari AH
    22. Noorrain A
    23. Lokmanhakim S

    (2008) Plasmodium knowlesi in humans, macaques and mosquitoes in peninsular Malaysia

    Parasites & Vectors 1:26.

    https://doi.org/10.1186/1756-3305-1-26

    • PubMed
    • Google Scholar
85. 1. Wang X
    2. Blanchet FG
    3. Koper N

    (2014) Measuring habitat fragmentation: An evaluation of landscape pattern metrics

    Methods in Ecology and Evolution 5:634–646.

    https://doi.org/10.1111/2041-210X.12198

    • Google Scholar
86. 1. Waxman D
    2. Weinert LA
    3. Welch JJ

    (2014) Inferring host range dynamics from comparative data: the protozoan parasites of new world monkeys

    The American Naturalist 184:65–74.

    https://doi.org/10.1086/676589

    • PubMed
    • Google Scholar
87. 1. White LA
    2. Forester JD
    3. Craft ME

    (2018) Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology

    PNAS 115:7374–7379.

    https://doi.org/10.1073/pnas.1801383115

    • Google Scholar
88. 1. Wilkinson DA
    2. Marshall JC
    3. French NP
    4. Hayman DTS

    (2018) Habitat fragmentation, biodiversity loss and the risk of novel infectious disease emergence

    Journal of the Royal Society, Interface 15:20180403.

    https://doi.org/10.1098/rsif.2018.0403

    • PubMed
    • Google Scholar
89. 1. Wong ML
    2. Chua TH
    3. Leong CS
    4. Khaw LT
    5. Fornace K
    6. Wan-Sulaiman WY
    7. William T
    8. Drakeley C
    9. Ferguson HM
    10. Vythilingam I
    11. Chua TH
    12. Fornace K
    13. Drakeley C
    14. William T
    15. Ferguson HM

    (2015) Seasonal and Spatial Dynamics of the Primary Vector of Plasmodium knowlesi within a Major Transmission Focus in Sabah, Malaysia

    PLOS Neglected Tropical Diseases 9:e0004135.

    https://doi.org/10.1371/journal.pntd.0004135

    • Google Scholar
90. Website

    1. World Health Organization

    (2021) Global Malaria Programme

    Accessed April 12, 2023.

    https://www.who.int/teams/global-malaria-programme/elimination
91. Data

    1. WorldPop

    (authors) (2018) Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076)

    WorldPop.

    https://doi.org/10.5258/SOTON/WP00671
92. 1. Yusuf NM
    2. Zulkefli J
    3. Jiram AI
    4. Vythilingam I
    5. Hisam S
    6. Devi R
    7. Salehhuddin A
    8. Ali NM
    9. Isa M
    10. Alias N
    11. Ogu Salim N
    12. Aziz AA
    13. Sulaiman LH

    (2022) Plasmodium spp. in macaques, Macaca fascicularis, in Malaysia, and their potential role in zoonotic malaria transmission

    Parasite 29:32.

    https://doi.org/10.1051/parasite/2022032

    • PubMed
    • Google Scholar
93. Preprint

    1. Zamzuri M
    2. Ammar IA
    3. Hassan MR
    4. Hod R

    (2020) Distribution and Prevalence of Plasmodium knowlesi Among Macaques In Negeri Sembilan, Malaysia

    Research Square.

    https://doi.org/10.21203/rs.3.rs-75430/v1

    • Google Scholar
94. 1. Zhang X
    2. Kadir KA
    3. Quintanilla-Zariñan LF
    4. Villano J
    5. Houghton P
    6. Du H
    7. Singh B
    8. Smith DG

    (2016) Distribution and prevalence of malaria parasites among long-tailed macaques (Macaca fascicularis) in regional populations across Southeast Asia

    Malaria Journal 15:1–8.

    https://doi.org/10.1186/s12936-016-1494-0

    • Google Scholar
95. 1. Zunino GE
    2. Kowalewski MM
    3. Oklander LI
    4. González V

    (2007) Habitat fragmentation and population size of the black and gold howler monkey (Alouatta caraya) in a semideciduous forest in Northern Argentina

    American Journal of Primatology 69:966–975.

    https://doi.org/10.1002/ajp.20389

    • Google Scholar

Author details

1. Emilia Johnson

   1. School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
   2. Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
   3. Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom

   Contribution

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

   For correspondence

   emilia.johnson@glasgow.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5460-1715

2. Reuben Sunil Kumar Sharma

   Faculty of Veterinary Medicine, Universiti Putra Malaysia, Selangor, Malaysia

   Contribution

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

   Competing interests

   No competing interests declared

3. Pablo Ruiz Cuenca

   1. Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
   2. Lancaster University, Bailrigg, Lancaster, United Kingdom
   3. Liverpool School of Tropical Medicine, Pembroke Place Liverpool, Liverpool, United Kingdom

   Contribution

   Data curation, Supervision, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2180-9509

4. Isabel Byrne

   Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7800-3733

5. Milena Salgado-Lynn

   1. School of Biosciences, Cardiff University, Cardiff, United Kingdom
   2. Wildlife Health, Genetic and Forensic Laboratory, Sabah Wildlife Department, Wisma Muis, Kota Kinabalu, Malaysia
   3. Danau Girang Field Centre, Sabah Wildlife Department, Kinabalu Sabah, Malaysia

   Contribution

   Conceptualization, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1769-6465

6. Zarith Suraya Shahar

   Faculty of Veterinary Medicine, Universiti Putra Malaysia, Selangor, Malaysia

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

7. Lee Col Lin

   Faculty of Veterinary Medicine, Universiti Putra Malaysia, Selangor, Malaysia

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

8. Norhadila Zulkifli

   Faculty of Veterinary Medicine, Universiti Putra Malaysia, Selangor, Malaysia

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

9. Nor Dilaila Mohd Saidi

   Faculty of Veterinary Medicine, Universiti Putra Malaysia, Selangor, Malaysia

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

10. Chris Drakeley

    Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4863-075X

11. Jason Matthiopoulos

    School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom

    Contribution

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

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3639-8172

12. Luca Nelli

    School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Formal analysis, Validation, Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6091-4072

13. Kimberly Fornace

    1. School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
    2. Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
    3. Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
    4. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5484-241X

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