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  1. Stark DJ, Fornace KM, Brock PM, Abidin TR, Gilhooly L, Jalius C, et al.
    Ecohealth, 2019 12;16(4):638-646.
    PMID: 30927165 DOI: 10.1007/s10393-019-01403-9
    Land-use changes can impact infectious disease transmission by increasing spatial overlap between people and wildlife disease reservoirs. In Malaysian Borneo, increases in human infections by the zoonotic malaria Plasmodium knowlesi are hypothesised to be due to increasing contact between people and macaques due to deforestation. To explore how macaque responses to environmental change impact disease risks, we analysed movement of a GPS-collared long-tailed macaque in a knowlesi-endemic area in Sabah, Malaysia, during a deforestation event. Land-cover maps were derived from satellite-based and aerial remote sensing data and models of macaque occurrence were developed to evaluate how macaque habitat use was influenced by land-use change. During deforestation, changes were observed in macaque troop home range size, movement speeds and use of different habitat types. Results of models were consistent with the hypothesis that macaque ranging behaviour is disturbed by deforestation events but begins to equilibrate after seeking and occupying a new habitat, potentially impacting human disease risks. Further research is required to explore how these changes in macaque movement affect knowlesi epidemiology on a wider spatial scale.
  2. Brock PM, Fornace KM, Grigg MJ, Anstey NM, William T, Cox J, et al.
    Proc Biol Sci, 2019 Jan 16;286(1894):20182351.
    PMID: 30963872 DOI: 10.1098/rspb.2018.2351
    The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.
  3. Grigg MJ, Cox J, William T, Jelip J, Fornace KM, Brock PM, et al.
    Lancet Planet Health, 2017 Jun 09;1(3):e97-e104.
    PMID: 28758162 DOI: 10.1016/S2542-5196(17)30031-1
    BACKGROUND: The emergence of human malaria due to the monkey parasite Plasmodium knowlesi threatens elimination efforts in southeast Asia. Changes in land use are thought to be driving the rise in reported P knowlesi cases, but the role of individual-level factors is unclear. To address this knowledge gap we assessed human and environmental factors associated with zoonotic knowlesi malaria risk.

    METHODS: We did this population-based case-control study over a 2 year period in the state of Sabah in Malaysia. We enrolled cases with microscopy-positive, PCR-confirmed malaria who presented to two primary referral hospitals serving the adjacent districts of Kudat and Kota Marudu. We randomly selected three malaria-negative community controls per case, who were matched by village within 2 weeks of case detection. We obtained questionnaire data on demographics, behaviour, and residential malaria risk factors, and we also assessed glucose-6-phosphate dehydrogenase (G6PD) enzyme activity. We used conditional logistic regression models to evaluate exposure risk between P knowlesi cases and controls, and between P knowlesi and human-only Plasmodium spp malaria cases.

    FINDINGS: From Dec 5, 2012, to Jan 30, 2015, we screened 414 patients and subsequently enrolled 229 cases with P knowlesi malaria mono-infection and 91 cases with other Plasmodium spp infection. We enrolled 953 matched controls, including 683 matched to P knowlesi cases and 270 matched to non-P knowlesi cases. Age 15 years or older (adjusted odds ratio [aOR] 4·16, 95% CI 2·09-8·29, p<0·0001), male gender (4·20, 2·54-6·97, p<0·0001), plantation work (3·50, CI, 1·34-9·15, p=0·011), sleeping outside (3·61, 1·48-8·85, p=0·0049), travel (2·48, 1·45-4·23, p=0·0010), being aware of the presence of monkeys in the past 4 weeks (3·35, 1·91-5·88, p<0·0001), and having open eaves or gaps in walls (2·18, 1·33-3·59, p=0·0021) were independently associated with increased risk of symptomatic P knowlesi infection. Farming occupation (aOR 1·89, 95% CI 1·07-3·35, p=0·028), clearing vegetation (1·89, 1·11-3·22, p=0·020), and having long grass around the house (2·08, 1·25-3·46, p=0·0048) increased risk for P knowlesi infection but not other Plasmodium spp infection. G6PD deficiency seemed to be protective against P knowlesi (aOR 0·20, 95% CI 0·04-0·96, p=0·045), as did residual insecticide spraying of household walls (0·52, 0·31-0·87, p=0·014), with the presence of young sparse forest (0·35, 0·20-0·63, p=00040) and rice paddy around the house (0·16, 0·03-0·78, 0·023) also associated with decreased risk.

    INTERPRETATION: Adult men working in agricultural areas were at highest risk of knowlesi malaria, although peri-domestic transmission also occurrs. Human behavioural factors associated with P knowlesi transmission could be targeted in future public health interventions.

    FUNDING: United Kingdom Medical Research Council, Natural Environment Research Council, Economic and Social Research Council, and Biotechnology and Biosciences Research Council.

  4. Fornace KM, Alexander N, Abidin TR, Brock PM, Chua TH, Vythilingam I, et al.
    Elife, 2019 10 22;8.
    PMID: 31638575 DOI: 10.7554/eLife.47602
    Human movement into insect vector and wildlife reservoir habitats determines zoonotic disease risks; however, few data are available to quantify the impact of land use on pathogen transmission. Here, we utilise GPS tracking devices and novel applications of ecological methods to develop fine-scale models of human space use relative to land cover to assess exposure to the zoonotic malaria Plasmodium knowlesi in Malaysian Borneo. Combining data with spatially explicit models of mosquito biting rates, we demonstrate the role of individual heterogeneities in local space use in disease exposure. At a community level, our data indicate that areas close to both secondary forest and houses have the highest probability of human P. knowlesi exposure, providing quantitative evidence for the importance of ecotones. Despite higher biting rates in forests, incorporating human movement and space use into exposure estimates illustrates the importance of intensified interactions between pathogens, insect vectors and people around habitat edges.
  5. Fornace KM, Brock PM, Abidin TR, Grignard L, Herman LS, Chua TH, et al.
    Lancet Planet Health, 2019 04;3(4):e179-e186.
    PMID: 31029229 DOI: 10.1016/S2542-5196(19)30045-2
    BACKGROUND: Land use changes disrupt ecosystems, altering the transmission of vector-borne diseases. These changes have been associated with increasing incidence of zoonotic malaria caused by Plasmodium knowlesi; however, the population-level distributions of infection and exposure remain unknown. We aimed to measure prevalence of serological exposure to P knowlesi and assess associated risk factors.

    METHODS: We did an environmentally stratified, population-based, cross-sectional survey across households in the Kudat, Kota Marudu, Pitas, and Ranau districts in northern Sabah, Malaysia, encompassing a range of ecologies. Using blood samples, the transmission intensity of P knowlesi and other malaria species was measured by specific antibody prevalence and infection detected using molecular methods. Proportions and configurations of land types were extracted from maps derived from satellite images; a data-mining approach was used to select variables. A Bayesian hierarchical model for P knowlesi seropositivity was developed, incorporating questionnaire data about individual and household-level risk factors with selected landscape factors.

    FINDINGS: Between Sept 17, 2015, and Dec 12, 2015, 10 100 individuals with a median age of 25 years (range 3 months to 105 years) were sampled from 2849 households in 180 villages. 5·1% (95% CI 4·8-5·4) were seropositive for P knowlesi, and marked historical decreases were observed in the transmission of Plasmodium falciparum and Plasmodium vivax. Nine Plasmodium spp infections were detected. Age, male sex, contact with macaques, forest use, and raised house construction were positively associated with P knowlesi exposure, whereas residing at higher geographical elevations and use of insecticide were protective. Agricultural and forest variables, such as proportions and fragmentation of land cover types, predicted exposure at different spatial scales from households.

    INTERPRETATION: Although few infections were detected, P knowlesi exposure was observed in all demographic groups and was associated with occupational factors. Results suggest that agricultural expansion and forest fragmentation affect P knowlesi exposure, supporting linkages between land use change and P knowlesi transmission.

    FUNDING: UK Medical Research Council, Natural Environment Research Council, Economic and Social Research Council, and Biotechnology and Biosciences Research Council.

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