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 space use into exposure estimates illustrates the importance of intensified interactions between pathogens, insect vectors and people around habitat edges.
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.
Landsat 8 Operational Land ImagerLandsat 8.
Daily TRMM and other satellites precipitation product (3B42 V6 derived)Tropical Rainfall Measurement Mission.
- Kimberly M Fornace
- Neal Alexander
- Tommy R Abidin
- Paddy M Brock
- Tock H Chua
- Indra Vythilingam
- Heather M Ferguson
- Benny O Manin
- Meng L Wong
- Sui H Ng
- Jon Cox
- Chris Drakeley
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: This study was approved by the Medical Research Sub-Committee of the Malaysian Ministry of Health (NMRR-12-537-12568) and the Research Ethics Committee of the London School of Hygiene and Tropical Medicine (6531). Written informed consent was obtained from all participants or parents or guardians and assent obtained from children under 18.
- Ben Cooper, Mahidol Oxford Tropical Medicine Research Unit, Thailand
© 2019, Fornace et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
The establishment and spread of antimalarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.
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