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

Abstract

Land-use changes, such as deforestation and agriculture, can influence mosquito vector populations and malaria transmission. These land-use changes have been linked to increased incidence in human cases of the zoonotic malaria Plasmodium knowlesi in Sabah, Malaysian Borneo. This study investigates whether these associations are partially driven by fine-scale land-use changes creating more favourable aquatic breeding habitats for P. knowlesi anopheline vectors. Using aerial remote sensing data, we developed a sampling frame representative of all land use types within a major focus of P. knowlesi transmission. From 2015 to 2016 monthly longitudinal surveys of larval habitats were collected in randomly selected areas stratified by land use type. Additional remote sensing data on environmental variables, land cover and landscape configuration were assembled for the study site. Risk factor analyses were performed over multiple spatial scales to determine associations between environmental and spatial variables and anopheline larval presence. Habitat fragmentation (300 m), aspect (350 m), distance to rubber plantations (100 m) and Culex larval presence were identified as risk factors for Anopheles breeding. Additionally, models were fit to determine the presence of potential larval habitats within the areas surveyed and used to generate a time-series of monthly predictive maps. These results indicate that land-use change and topography influence the suitability of larval habitats, and may partially explain the link between P. knowlesi incidence and deforestation. The predictive maps, and identification of the spatial scales at which risk factors are most influential may aid spatio-temporally targeted vector control interventions.

Landscape drives zoonotic malaria prevalence in non-human primates

“…Ecological processes determining P. knowlesi infection are influenced by dynamic factors over multiple spatial scales 43 . We utilised a data-driven methodology to select variables at distances that capture maximum impact on P. knowlesi prevalence 32,42 , with surface elevation and human population density most influential at localised scales (5km) and forest cover and forest fragmentation showing strongest influence within 20km radii. Contrary to previous studies on risk factors for human incidence of P. knowlesi 31,32 , geographical elevation and human population density were not found to be associated with P. knowlesi in NHPs.…”

Non-Human Primate Malaria Infections: A Review on the Epidemiology in Malaysia

“…The risk of acquiring simian malaria infection is high among the communities living near forest fringes or those who work inside the forest due to the proximity to reservoir hosts and mosquito vectors. High-risk groups include indigenous people, farmers, cultivators, and forest workers [135,136]. Human infection by zoonotic malaria occurs in areas where the vectors and the natural host species co-exist [137].…”

Mapping potential malaria vector larval habitats for larval source management: Introduction to multi-model ensembling approaches

“…Overall, we surveyed about 50,000 aquatic habitats, and Anopheles larval infestation status was available in about 40,000 habitats (Table 1). We have noticed that very few studies have predicted the potential of larval positive habitats [19]. Since all aquatic habitats had water at the time they were surveyed, to identify predictors of aquatic habitats we artificially generated about 13,500 pseudo-habitats in randomly selected locations with known residential houses [25,36] (Table 1).…”

“…Many climatic-and environmental-based models have been used to predict the potential distribution of malaria vector larval habitats, for example, ecological/environmental niche models [15,16], logistic regression [17][18][19], and machine learning methods such as artificial . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”