Environmental change and human encroachment into wildlife habitats are key drivers in the emergence and transmission of zoonotic diseases (Lambin et al., 2010; Patz et al., 2004). Individual movements into different habitats influence exposure to disease vectors and animal reservoirs, determining risk and propagation of vector-borne diseases (Stoddard et al., 2013; Stoddard et al., 2009; Pindolia et al., 2012). Increased contact between these populations is theorised to drive increases of the zoonotic malaria Plasmodium knowlesi in Malaysian Borneo, now the main cause of human malaria within this region. P. knowlesi is carried by long- and pig-tailed macaques (Macaca fascicularis and M. nemestrina) and transmitted by the Anopheles leucospryphus mosquito group, both populations highly sensitive to land cover and land use change (Moyes et al., 2016). Although higher spatial overlap between people, macaques and mosquito vectors likely drives transmission, the impact of human movement and land use in determining individual infection risks is poorly understood (Imai et al., 2014).

The emergence of the zoonotic malaria Plasmodium knowlesi has been positively associated with both forest cover and historical deforestation (Fornace et al., 2016b; Shearer et al., 2016). However, out of necessity, statistical approaches to assess environmental risk factors for P. knowlesi and other infectious diseases typically evaluate relationships between disease metrics and local land cover surrounding houses or villages. While an individual may spend most of their time within the vicinity of their residence, this area does not necessarily represent where they are most likely to be exposed to a disease. This is supported by varying associations between P. knowlesi occurrence and landscape variables at different distances from households, ranging from 100 m to 5 km, likely partially due to human movement into different surrounding habitats (Fornace et al., 2016b; Brock et al., 2019). Although land cover variables describing physical terrestrial surfaces are frequently incorporated into disease models, land use is rarely quantified. Land use is commonly defined as ‘the arrangements, activities, and inputs that people undertake in certain land cover types’ (IPCC, 2000). Places with similar types of land cover may be used very differently, with the activities and frequencies with which people visit these places determining the spatial distribution of disease (Lambin et al., 2010).

Mathematical modelling studies have revealed the importance of spatial variation in contact rates due to the movement of individuals through heterogeneous environments with varying transmission intensity (Acevedo et al., 2015). A multi-species transmission model of P. knowlesi highlighted the role of mixing patterns between populations in different ecological settings in determining the basic reproductive rate and subsequent modelling studies illustrate the sensitivity of this disease system to population densities of both people and wildlife hosts (Imai et al., 2014; Yakob et al., 2018). However, although mechanistic models have been extended to explore the potential importance of these heterogeneities in disease dynamics, there are inherent constraints on model complexity and most models make simplistic assumptions about the habitat uses of different populations.

Empirical data on human population movement is increasingly available, allowing assessment of the impact of mobility on infectious disease dispersion and risks (Pindolia et al., 2012). On larger spatial scales, mobile phone data have revealed the role of human migration in the transmission of infectious diseases such as malaria, dengue and rubella (Chang et al., 2019; Wesolowski et al., 2015a; Wesolowski et al., 2015b).Although this data can provide insights into long range movements, spatial resolution of this data is limited, particularly in areas with poor or no mobile coverage, such as forested areas (Wesolowski et al., 2016). Alternatively, the advent of low-cost GPS tracking devices allows quantification of fine-scale movements, demonstrating marked heterogeneity in individual movement and risk behaviours (Stoddard et al., 2013; Vazquez-Prokopec et al., 2013). Combining these data with detailed data on land cover and vector dynamics can provide new insights into how landscapes affect P. knowlesi transmission.

Previous studies of P. knowlesi have relied on questionnaire surveys, identifying self-reported travel to nearby plantations and forest areas as a risk factor for P. knowlesi and other malaria infections (e.g. Grigg et al., 2017; Singh et al., 2004; Yasuoka and Levins, 2007). However, the resultant spatial range and frequency of these movements remain unknown and the definition of different habitat types is entirely subjective. Further, little is known about differences in local movement patterns in different demographic groups. While infections in male adults have been linked to forest and plantation work, it is unknown whether infections reported in women and young children are likely to arise from exposure to similar environments (Barber et al., 2012). The main mosquito vector in this area, An. balabacensis, is primarily exophagic and has been identified in farm, forest and village areas near houses (Wong et al., 2015; Manin et al., 2016). Macaque populations are reported in close proximity to human settlements and molecular and modelling studies suggest transmission remains primarily zoonotic in this area (Imai et al., 2014; Lee et al., 2011; Chua et al., 2017). A case control study detected higher abundances of An. balabacensis near P. knowlesi case housesholds, suggesting the possibility of peri-domestic transmission (Manin et al., 2016). Understanding the importance of these habitats is essential to effectively target intervention strategies and predict impacts of future environmental changes.

Key questions remain about where individuals are likely to be exposed to P. knowlesi and how landscape determines risk. Functional ecology approaches allow the distribution of different populations to be modelled based on biological resources and relate transmission to landscape and environmental factors (Hartemink et al., 2015). Within wildlife ecology, numerous methods have been developed to estimate utilisation distributions (UDs), the probability of an individual or species being within a specific location during the sampling period (Papworth et al., 2012). Although these methods traditionally rely on kernel density smoothing, kernel density estimates may not actually reflect time individuals spend in a specific location if there is substantial missing data or irregular time intervals. Alternatively, biased random bridges (BRBs) improve on these methods by estimating the utilisation distribution as a time-ordered series of points, taking advantage of the autocorrelated nature of GPS tracks to bias movement predictions towards subsequent locations in a time series (Benhamou, 2011). This allows for interpolation of missing values and adjustment for spatial error to estimate utilisation distributions representing both the intensity (mean residence time per visit) and frequency of individual visits to specific locations. By integrating these estimates of individual space use with detailed spatial and environmental data in a Bayesian framework, fine-scale patterns of human land use can be predicted and overlaid with spatiotemporal models of mosquito distribution. This allows exploration of how landscape composition, as well as configuration and connectivity between habitats, impacts human exposure to P. knowlesi and other vector-borne and zoonotic diseases.

Focusing on one aspect of land use, human movement and time spent within different land cover types, we explored the role of heterogeneity in local space use on disease exposure. Rolling cross-sectional GPS tracking surveys were conducted in two study areas with on-going P. knowlesi transmission in Northern Sabah, Malaysia (Matunggong and Limbuak; Fornace et al., 2018). We aimed to characterise local movement patterns and identify individuals and locations associated with increased P. knowlesi exposure risks by: 1. analysing individual movement patterns and developing predictive maps of human space use relative to spatial and environmental factors, 2. modelling biting rates of the main vector An. balabacensis, and 3. assessing exposure risks for P. knowlesi based on predicted mosquito and human densities (Figure 1) Integrating these three approaches allowed a uniquely spatially explicit examination of disease risk.

Figure 1

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Between February 2014 and May 2016, 285 consenting people participated in the GPS tracking study with 243 included in the final analysis including 109 in Limbuak and 134 in Matunggong (Table 1). The most commonly reported occupation was farm or plantation work (n = 73), primarily conducted within the immediate vicinity of the house. A total of 3,424,913 GPS points were collected, representing 6,319,885 person-minutes of sampling time. Median sampling duration was 16.27 days (IQR 13.72–19.97), with points recorded for a median of 59.1% (IQR: 46.9–71.1%) of the sampling duration. Maximum distances travelled ranged from no travel outside the house to 116 km, with a median distance travelled of 1.8 km. Utilisation distributions (UDs), the probability of an individual being in a location in space within a given time (Figure 3), varied by gender and occupation. Individuals at the more rural Limbuak site covered larger distances (Table 2), with the largest distances covered by individuals reporting primary occupations of fishing (n = 5) and office work (n = 9). Although substantial differences were reported in all movements (24 hr sampling) between seasons, no seasonal differences were observed in human movements during peak Anopheles biting times (6pm-6am).

Figure 3

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For both study areas, we developed models of community space use during peak mosquito biting hours (6pm – 6am), in the form of resource utilisation functions, predictions of time- and space-specific UDs on the basis of spatial and environmental variables (Papworth et al., 2012). Between 6pm – 6am, human space use (UDs) was mostly predictable and negatively correlated with distance from the individual’s house, other houses, roads and slope. The AUC for presence/absence models was 0.936 for Matunggong and 0.938 for Limbuak and RMSE for the overall model was 0.0073 and 0.0043 for Matunggong and Limbuak, respectively. While individuals were more likely to use areas further away from forests in the Matunggong site, human space use was positively correlated with proximity to forests in the Limbuak site (Table 3). Despite marked differences between different demographic groups and seasons observed during 24 hr movements, these factors did not improve the predictive power of the model for movements between 6pm and 6am.

Between August 2013 and December 2015, 4814 An. balabacensis were caught from 328 sampling nights in 155 unique locations. The median biting rate was 2.1 bites per night per person, ranging from 0 to 28 bites per person per night (Figure 4). Despite monthly variation, including temporal autocorrelation did not improve model fit (Table 4). Although no associations were identified between land classification and vector density in this site, models identified positive relationships with enhanced vegetation indices (EVI) and negative associations with distance to forest and human population density (Table 5). Of 1524 mosquitoes tested for Plasmodium sporozoites, the median sporozoite rate was 0.24% (95% CI: 0.09–0.58%).

Figure 4

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For individuals included in the GPS tracking study in Matunggong, where both human movement and entomology data were available, we calculated exposure risks as a derived quantity from utilisation distributions and mosquito biting rate models. Exposure varied markedly between individuals, with an overall 150-fold difference in predicted mean probabilities of infected bites per night (range: 0.00005–0.0078) (Table 6). No clear differences were observed between genders, age groups or occupations of individuals sampled and there was no association between risk and distance travelled.

Using the resource utilisation function with demographic and spatial data for all individuals in Matunggong, we predicted community-wide space use and estimated exposure to infected mosquitoes (Figure 5). The predicted number of person nights per grid cell for the entire community ranged from 0 to 12.79 (median: 0.01, IQR: 0.0004–0.99), with the mean probability of a community member exposed to an infected bite per grid cell of 0.00082 (IQR: 0.00001, 0.00050). Although over 43% of the study site is forest and relatively high biting rates were predicted in forests during the study period (mean: 1.94, range: 0.04–12.59), this habitat was rarely used by people in the evenings, with less than 8% of predicted person-nights in forests. Models only based on mosquito biting rates and not including human space use predicted 42% of infectious bites occurred in forested areas and only 8.6% of bites occurring within 100 m of houses (Figure 5C). In contrast, when space use patterns are included, over 91% of predicted infected bites were predicted within 500 m of houses (Figure 5D). Highest exposure risks were consistently found near forest edges and in close proximity to households, despite spatial and temporal heterogeneity and model uncertainty (Figure 4).

Figure 5

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This study highlights the importance of human space use in different land cover types in determining exposure to zoonotic and vector-borne diseases such as P. knowlesi. Although P. knowlesi has previously been associated with forest exposure (e.g. Grigg et al., 2017) and higher biting rates have been reported in forest interiors (Wong et al., 2015), this novel approach incorporating both mosquito and human space use data provides a new perspective on peri-domestic transmission, with more than 90% of infectious bites predicted in areas surrounding households at forest edges. This study additionally demonstrates the utility of ecological methods to understand human movement and identify geographical areas associated with higher contact with disease vectors.

Within these communities, local movement patterns during peak vector times were largely predictable and could be explained by spatial and environmental factors. However, despite this finding, there was substantial variation in predicted exposure between individuals as a result of heterogeneity in habitats used. No significant differences in exposure were predicted between men and women, with individuals with high exposure risks identified across occupational and age groups. Although this finding differs from clinical reports, a comprehensive survey within this community identified equal proportions of men and women exposed to P. knowlesi as evidenced by specific antibody responses and data on asymptomatic infections suggests higher numbers of non-clinical infections in women (Fornace et al., 2018; Fornace et al., 2016a). While infrequent events or long-range movements (such as hunting trips) may contribute to these differences in clinical cases and may not have been captured within this two-week sampling period within the study site, this analysis highlights the importance of routine movements into local environments in shaping exposure risks.

This improved understanding of how local human land use is related to exposure risk has important implications for surveillance and control programmes. Malaria control programmes often rely on interventions within the house, such as insecticide treated bednets and indoor residual spraying; however, movements outside during peak biting times illustrate the importance of also targeting outdoor transmission. The identification of areas where exposure is likely to occur can further be used to refine interventions; for example, although insecticide treated hammocks have been proposed for deep forest environments, larval source management may be more appropriate to target environments in close proximity to houses. Although initial P. knowlesi cases were primarily identified in adult men living and working in forests (Singh et al., 2004), this study illustrates the potential importance of peri-domestic habitats in transmission and provides quantitative insight on mixing between people and infected mosquitoes in forest fringe areas. As Malaysia moves towards malaria elimination, surveillance systems are incorporating novel focal investigation methods, including monitoring changes in local land use and populations at risk (Bahagian Kawalan Penyakit, Kementerian Kesihatan Malaysia, 2016). In additional to routine vector surveillance, this study highlights the need to incorporate measures of human space when defining risk zones.

Even with the large and highly detailed movement dataset analysed, this study was limited by the availability of mosquito data; as human landing catch data were assembled from other studies, there was not uniform spatial and temporal coverage of the study site increasing uncertainty. The limited mosquito data availability precluded development of mosquito biting rate models for Limbuak and other outlying islands. An additional limitation to estimating mosquito biting rates was the difficulty obtaining spatially and temporally resolute remote sensing data for predictors due to high cloud cover (Weiss et al., 2015). As few positive mosquitoes were identified, uniform estimates of sporozoite rates based on available data were used across the Matunggong site; if further data were available, these models could be refined to incorporate estimates of human and macaque density, mosquito biting preferences in different habitats and infection levels in all hosts (Yakob et al., 2010). Additionally, as this study was designed to quantitatively estimate time spent in different landscapes, further studies could explore other aspects of land use, such as the purposes of travel, activities undertaken or practices used to modify or management land cover.

Despite these limitations, this is the first large-scale study to utilise GPS tracking data and ecological methods to create fine-scale maps of exposure risk. This study highlights the importance of incorporating heterogenous patterns of human space use into disease models, as the majority of human exposure may occur in areas with lower vector biting rates but greater probabilities of human use. Further, results quantitatively illustrate the importance of forest edges and local habitat in P. knowlesi transmission and can inform understanding of other zoonotic and vector-borne diseases.

Data on human subjects is not available due to ethical restrictions around sharing identifiable information. All other data is publicly available with relevant links or publications included. Code to reproduce this analysis is available on GitHub or as supplementary information.

1. 1. NASA LP DAAC

   (2015) NASA Landsat Science

   Landsat 8 Operational Land Imager.

   https://landsat.gsfc.nasa.gov/landsat-8/
2. 1. NASA LP DAAC

   (2015) ASTER

   Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model.

   https://doi.org/10.5067/ASTER/ASTGTM.003
3. 1. DAAC NL

   (2015) MODIS

   MODIS/ Terra Vegetation Indices 16-Day L3 Global 250m Grid SIN V006.

   https://doi.org/10.5067/MODIS/MOD13Q1.006
4. 1. NASA TRMM

   (2015) Tropical Rainfall Measurement Mission

   Daily TRMM and other satellites precipitation product (3B42 V6 derived).

   https://doi.org/10.5067/TRMM/TMPA/DAY/7

1. 1. Acevedo MA
   2. Prosper O
   3. Lopiano K
   4. Ruktanonchai N
   5. Caughlin TT
   6. Martcheva M
   7. Osenberg CW
   8. Smith DL

   (2015) Spatial heterogeneity, host movement and mosquito-borne disease transmission

   PLOS ONE 10:e0127552.

   https://doi.org/10.1371/journal.pone.0127552

   • PubMed
   • Google Scholar
2. Report

   1. Bahagian Kawalan Penyakit, Kementerian Kesihatan Malaysia

   (2016)

    Garis Panduan Pencegahan Malaria Re-Introduction Di Malaysia

   Putra Jaya: Kementerian Kesihatan Malaysia.

   • Google Scholar
3. 1. Barber BE
   2. William T
   3. Dhararaj P
   4. Anderios F
   5. Grigg MJ
   6. Yeo TW
   7. Anstey NM

   (2012) Epidemiology of Plasmodium knowlesi malaria in north-east sabah, Malaysia: family clusters and wide age distribution

   Malaria Journal 11:401.

   https://doi.org/10.1186/1475-2875-11-401

   • PubMed
   • Google Scholar
4. 1. Barbet-Massin M
   2. Jiguet F
   3. Albert CH
   4. Thuiller W

   (2012) Selecting pseudo-absences for species distribution models: how, where and how many?

   Methods in Ecology and Evolution 3:327–338.

   https://doi.org/10.1111/j.2041-210X.2011.00172.x

   • Google Scholar
5. 1. Benhamou S

   (2011) Dynamic approach to space and habitat use based on biased random bridges

   PLOS ONE 6:e14592.

   https://doi.org/10.1371/journal.pone.0014592

   • PubMed
   • Google Scholar
6. Book

   1. Blangiardo M
   2. Spatial CM

   (2015) Spatio-Temporal Bayesian Models with R-INLA

   Chichester, United Kingdom: John Wiley & Sons, Ltd.

   https://doi.org/10.1002/9781118950203

   • Google Scholar
7. 1. Brock PM
   2. Fornace KM
   3. Grigg MJ
   4. Anstey NM
   5. William T
   6. Cox J

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

   Proceedings of the Royal Society B 2019:286.

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

   • Google Scholar
8. 1. Chang HH
   2. Wesolowski A
   3. Sinha I
   4. Jacob CG
   5. Mahmud A
   6. Uddin D
   7. Zaman SI
   8. Hossain MA
   9. Faiz MA
   10. Ghose A
   11. Sayeed AA
   12. Rahman MR
   13. Islam A
   14. Karim MJ
   15. Rezwan MK
   16. Shamsuzzaman AKM
   17. Jhora ST
   18. Aktaruzzaman MM
   19. Drury E
   20. Gonçalves S
   21. Kekre M
   22. Dhorda M
   23. Vongpromek R
   24. Miotto O
   25. Engø-Monsen K
   26. Kwiatkowski D
   27. Maude RJ
   28. Buckee C

   (2019) Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

   eLife 8:e43481.

   https://doi.org/10.7554/eLife.43481

   • PubMed
   • Google Scholar
9. 1. Chua TH
   2. Manin BO
   3. Daim S
   4. Vythilingam I
   5. Drakeley C

   (2017) Phylogenetic analysis of simian Plasmodium spp. infecting Anopheles balabacensis baisas in sabah, Malaysia

   PLOS Neglected Tropical Diseases 11:e0005991.

   https://doi.org/10.1371/journal.pntd.0005991

   • PubMed
   • Google Scholar
10. Data

    1. Didan K

    (authors) (2015) MOD13Q1 MODIS/Terra vegetation indices 16-Day L3 global 250m SIN grid V006

    NASA EOSDIS Land Processes DAAC.

    https://doi.org/10.5067/MODIS/MOD13Q1.006
11. 1. Fornace KM
    2. Nuin NA
    3. Betson M
    4. Grigg MJ
    5. William T
    6. Anstey NM
    7. Yeo TW
    8. Cox J
    9. Ying LT
    10. Drakeley CJ

    (2016a) Asymptomatic and submicroscopic carriage of Plasmodium knowlesi malaria in household and community members of clinical cases in sabah, Malaysia

    Journal of Infectious Diseases 213:784–787.

    https://doi.org/10.1093/infdis/jiv475

    • PubMed
    • Google Scholar
12. 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

    (2016b) Association between landscape factors and spatial patterns of Plasmodium knowlesi infections in sabah, Malaysia

    Emerging Infectious Diseases 22:201–209.

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

    • PubMed
    • Google Scholar
13. 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
14. 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
15. 1. Hartemink N
    2. Vanwambeke SO
    3. Purse BV
    4. Gilbert M
    5. Van Dyck H

    (2015) Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks

    Biological Reviews 90:1151–1162.

    https://doi.org/10.1111/brv.12149

    • PubMed
    • Google Scholar
16. Book

    1. Held L
    2. Schrodle B
    3. Posterior RH

    (2010) Cross-validatory Predictive Checks: A Comparison of MCMC and INLA

    In: Kneib T, Tutz G, editors. Statistical Modelling and Regression Structures. Physica-Verlag HD. pp. 91–110.

    https://doi.org/10.1007/978-3-7908-2413-1_6

    • Google Scholar
17. 1. Hooten MB
    2. Hanks EM
    3. Johnson DS
    4. Alldredge MW

    (2013) Reconciling resource utilization and resource selection functions

    Journal of Animal Ecology 82:1146–1154.

    https://doi.org/10.1111/1365-2656.12080

    • PubMed
    • Google Scholar
18. 1. Imai N
    2. White MT
    3. Ghani AC
    4. Drakeley CJ

    (2014) Transmission and control of Plasmodium knowlesi: a mathematical modelling study

    PLOS Neglected Tropical Diseases 8:e2978.

    https://doi.org/10.1371/journal.pntd.0002978

    • PubMed
    • Google Scholar
19. Book

    1. IPCC

    (2000)

    Land Use, Land-Use Change, and Forestry

    Watson RT, Noble IR, Bolin B, Ravindranath NH, Verado DJ, Dokken DJ, editors. UK: Cambridge University Press.

    • Google Scholar
20. 1. Johnston R
    2. Brady HE

    (2002) The rolling cross-section design

    Electoral Studies 21:283–295.

    https://doi.org/10.1016/S0261-3794(01)00022-1

    • Google Scholar
21. 1. Lambin EF
    2. Tran A
    3. Vanwambeke SO
    4. Linard C
    5. Soti V

    (2010) Pathogenic landscapes: interactions between land, people, disease vectors, and their animal hosts

    International Journal of Health Geographics 9:54.

    https://doi.org/10.1186/1476-072X-9-54

    • PubMed
    • Google Scholar
22. 1. Lee KS
    2. Divis PC
    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 Pathogens 7:e1002015.

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

    • PubMed
    • Google Scholar
23. 1. Lindgren F
    2. Rue H
    3. Lindström J

    (2011) An explicit link between gaussian fields and gaussian markov random fields: the stochastic partial differential equation approach

    Journal of the Royal Statistical Society: Series B 73:423–498.

    https://doi.org/10.1111/j.1467-9868.2011.00777.x

    • Google Scholar
24. 1. Lindgren F
    2. Rue H

    (2015) Bayesian spatial modelling with R-INLA

    Journal of Statistical Software 63:1–25.

    https://doi.org/10.18637/jss.v063.i19

    • Google Scholar
25. 1. Manin BO
    2. Ferguson HM
    3. Vythilingam I
    4. Fornace K
    5. William T
    6. Torr SJ
    7. Drakeley C
    8. 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
26. 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 IR
    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
27. 1. Papworth SK
    2. Bunnefeld N
    3. Slocombe K
    4. Milner-Gulland EJ

    (2012) Movement ecology of human resource users: using net squared displacement, biased random bridges and resource utilization functions to quantify hunter and gatherer behaviour

    Methods in Ecology and Evolution 3:584–594.

    https://doi.org/10.1111/j.2041-210X.2012.00189.x

    • Google Scholar
28. 1. Patz JA
    2. Daszak P
    3. Tabor GM
    4. Aguirre AA
    5. Pearl M
    6. Epstein J
    7. Wolfe ND
    8. Kilpatrick AM
    9. Foufopoulos J
    10. Molyneux D
    11. Bradley DJ
    12. Working Group on Land Use Change and Disease Emergence

    (2004) Unhealthy landscapes: policy recommendations on land use change and infectious disease emergence

    Environmental Health Perspectives 112:1092–1098.

    https://doi.org/10.1289/ehp.6877

    • PubMed
    • Google Scholar
29. 1. Pindolia DK
    2. Garcia AJ
    3. Wesolowski A
    4. Smith DL
    5. Buckee CO
    6. Noor AM
    7. Snow RW
    8. Tatem AJ

    (2012) Human movement data for malaria control and elimination strategic planning

    Malaria Journal 11:205.

    https://doi.org/10.1186/1475-2875-11-205

    • PubMed
    • Google Scholar
30. 1. Rue H
    2. Martino S
    3. Chopin N

    (2009) Approximate bayesian inference for latent gaussian models by using integrated nested Laplace approximations

    Journal of the Royal Statistical Society 2:319–392.

    https://doi.org/10.1111/j.1467-9868.2008.00700.x

    • Google Scholar
31. 1. Sadykova D
    2. Scott BE
    3. De Dominicis M
    4. Wakelin SL
    5. Sadykov A
    6. Wolf J

    (2017) Bayesian joint models with INLA exploring marine mobile predator-prey and competitor species habitat overlap

    Ecology and Evolution 7:5212–5226.

    https://doi.org/10.1002/ece3.3081

    • PubMed
    • Google Scholar
32. 1. Sh N
    2. Homathevi R
    3. Chua TH

    (2016)

    Mosquitoes of kudat: species composition and their medical importance (Diptera: culicidae)

    Serangga 21:149–162.

    • Google Scholar
33. 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 IR
    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
34. 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
35. 1. Stoddard ST
    2. Morrison AC
    3. Vazquez-Prokopec GM
    4. Paz Soldan V
    5. Kochel TJ
    6. Kitron U
    7. Elder JP
    8. Scott TW

    (2009) The role of human movement in the transmission of vector-borne pathogens

    PLOS Neglected Tropical Diseases 3:e481.

    https://doi.org/10.1371/journal.pntd.0000481

    • PubMed
    • Google Scholar
36. 1. Stoddard ST
    2. Forshey BM
    3. Morrison AC
    4. Paz-Soldan VA
    5. Vazquez-Prokopec GM
    6. Astete H
    7. Reiner RC
    8. Vilcarromero S
    9. Elder JP
    10. Halsey ES
    11. Kochel TJ
    12. Kitron U
    13. Scott TW

    (2013) House-to-house human movement drives dengue virus transmission

    PNAS 110:994–999.

    https://doi.org/10.1073/pnas.1213349110

    • PubMed
    • Google Scholar
37. 1. Vazquez-Prokopec GM
    2. Bisanzio D
    3. Stoddard ST
    4. Paz-Soldan V
    5. Morrison AC
    6. Elder JP
    7. Ramirez-Paredes J
    8. Halsey ES
    9. Kochel TJ
    10. Scott TW
    11. Kitron U

    (2013) Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment

    PLOS ONE 8:e58802.

    https://doi.org/10.1371/journal.pone.0058802

    • PubMed
    • Google Scholar
38. 1. Weiss DJ
    2. Mappin B
    3. Dalrymple U
    4. Bhatt S
    5. Cameron E
    6. Hay SI
    7. Gething PW

    (2015) Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach

    Malaria Journal 14:68.

    https://doi.org/10.1186/s12936-015-0574-x

    • PubMed
    • Google Scholar
39. 1. Wesolowski A
    2. Qureshi T
    3. Boni MF
    4. Sundsøy PR
    5. Johansson MA
    6. Rasheed SB
    7. Engø-Monsen K
    8. Buckee CO

    (2015a) Impact of human mobility on the emergence of dengue epidemics in Pakistan

    PNAS 112:11887–11892.

    https://doi.org/10.1073/pnas.1504964112

    • Google Scholar
40. 1. Wesolowski A
    2. Metcalf CJ
    3. Eagle N
    4. Kombich J
    5. Grenfell BT
    6. Bjørnstad ON
    7. Lessler J
    8. Tatem AJ
    9. Buckee CO

    (2015b) Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

    PNAS 112:11114–11119.

    https://doi.org/10.1073/pnas.1423542112

    • PubMed
    • Google Scholar
41. 1. Wesolowski A
    2. Buckee CO
    3. Engø-Monsen K
    4. Metcalf CJE

    (2016) Connecting mobility to infectious diseases: the promise and limits of mobile phone data

    Journal of Infectious Diseases 214:S414–S420.

    https://doi.org/10.1093/infdis/jiw273

    • PubMed
    • Google Scholar
42. 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

    (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

    • PubMed
    • Google Scholar
43. 1. Yakob L
    2. Bonsall MB
    3. Yan G

    (2010) Modelling knowlesi malaria transmission in humans: vector preference and host competence

    Malaria Journal 9:329.

    https://doi.org/10.1186/1475-2875-9-329

    • PubMed
    • Google Scholar
44. 1. Yakob L
    2. Lloyd AL
    3. Kao RR
    4. Ferguson HM
    5. Brock PM
    6. Drakeley C
    7. Bonsall MB

    (2018) Plasmodium knowlesi invasion following spread by infected mosquitoes, macaques and humans

    Parasitology 145:101–110.

    https://doi.org/10.1017/S0031182016002456

    • PubMed
    • Google Scholar
45. 1. Yasuoka J
    2. Levins R

    (2007) Impact of deforestation and agricultural development on anopheline ecology and malaria epidemiology

    The American Journal of Tropical Medicine and Hygiene 76:450–460.

    https://doi.org/10.4269/ajtmh.2007.76.450

    • PubMed
    • Google Scholar

Author details

1. Kimberly M Fornace

   1. Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
   2. Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   Kimberly.Fornace@lshtm.ac.uk

   Competing interests

   No competing interests declared

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

2. Neal Alexander

   Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Tommy R Abidin

   Department of Pathobiology and Medical Diagnostics, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

4. Paddy M Brock

   Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

5. Tock H Chua

   Department of Pathobiology and Medical Diagnostics, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

   Contribution

   Data curation, Writing—review and editing, Interpretation of mosquito data

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8984-8723

6. Indra Vythilingam

   Parasitology Department, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

   Contribution

   Data curation, Writing—review and editing, Interpretation and analysis of mosquito data

   Competing interests

   No competing interests declared

7. Heather M Ferguson

   Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

8. Benny O Manin

   Department of Pathobiology and Medical Diagnostics, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

   Contribution

   Data curation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0726-6146

9. Meng L Wong

   Parasitology Department, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

   Contribution

   Data curation, Investigation, Collection and analysis of mosquito data

   Competing interests

   No competing interests declared

10. Sui H Ng

    Department of Pathobiology and Medical Diagnostics, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

    Contribution

    Data curation, Investigation, Collection and analysis of mosquito data

    Competing interests

    No competing interests declared

11. Jon Cox

    Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

12. Chris Drakeley

    Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Writing—review and editing

    Competing interests

    No competing interests declared

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

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