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

Abstract

The public health threat posed by zoonotic Plasmodium knowlesi appears to be growing: it is increasingly reported across South East Asia, and is the leading cause of malaria in Malaysian Borneo. Plasmodium knowlesi threatens progress towards malaria elimination as aspects of its transmission, such as spillover from wildlife reservoirs and reliance on outdoor-biting vectors, may limit the effectiveness of conventional methods of malaria control. The development of new quantitative approaches that address the ecological complexity of P. knowlesi, particularly through a focus on its primary reservoir hosts, will be required to control it. Here, we review what is known about P. knowlesi transmission, identify key knowledge gaps in the context of current approaches to transmission modelling, and discuss the integration of these approaches with clinical parasitology and geostatistical analysis. We highlight the need to incorporate the influences of fine-scale spatial variation, rapid changes to the landscape, and reservoir population and transmission dynamics. The proposed integrated approach would address the unique challenges posed by malaria as a zoonosis, aid the identification of transmission hotspots, provide insight into the mechanistic links between incidence and land use change and support the design of appropriate interventions.

Keywords: Plasmodium knowlesi; R 0; infectious disease transmission; macaque; malaria; mathematical model; mosquito; reproduction number; vector; zoonosis.

Fig. 1.

(A) The average number of secondary human infections caused by a single macaque case (x-axis) and by a single human case (y-axis), and system R₀ (colours), for each scenario; (B) the same information plotted only for scenarios that generated prevalences deemed plausible (humans: 0·5–5%; macaques: 50–90%), scenarios in which R^HH was >1 are circled; (C) the medians and interquartile ranges of the ratios of humans to vectors, humans to macaques and macaques to vectors for all scenarios, plausible scenarios, and plausible scenarios in which R^HH >1; (D) the median and interquartile ranges of the four transmission coefficients: C_vh (vector–human), C_vm (vector–macaque), C_hv (human–vector) C_mv (macaque–vector); and the vector-biting preference for humans vs macaques (p).

Fig. 2.

(A) Three example neighbourhood sizes drawn around a case household, showing % forest cover in 2012, and (B) the deviance explained by four example forest variables at 13 neighbourhood sizes in univariate generalized additive models of infection status.