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Patient variability in the blood-stage dynamics of Plasmodium falciparum captured by clustering historical data

Masserey, T. and Penny, M. A. and Lee, T. E.. (2022) Patient variability in the blood-stage dynamics of Plasmodium falciparum captured by clustering historical data. Malar J, 21. p. 300.

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Official URL: https://edoc.unibas.ch/90644/

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Abstract

BACKGROUND: Mathematical models provide an understanding of the dynamics of a Plasmodium falciparum blood-stage infection (within-host models), and can predict the impact of control strategies that affect the blood-stage of malaria. However, the dynamics of P. falciparum blood-stage infections are highly variable between individuals. Within-host models use different techniques to capture this inter-individual variation. This struggle may be unnecessary because patients can be clustered according to similar key within-host dynamics. This study aimed to identify clusters of patients with similar parasitaemia profiles so that future mathematical models can include an improved understanding of within-host variation. METHODS: Patients' parasitaemia data were analyzed to identify (i) clusters of patients (from 35 patients) that have a similar overall parasitaemia profile and (ii) clusters of patients (from 100 patients) that have a similar first wave of parasitaemia. For each cluster analysis, patients were clustered based on key features which previous models used to summarize parasitaemia dynamics. The clustering analyses were performed using a finite mixture model. The centroid values of the clusters were used to parameterize two established within-host models to generate parasitaemia profiles. These profiles (that used the novel centroid parameterization) were compared with profiles that used individual-specific parameterization (as in the original models), as well as profiles that ignored individual variation (using overall means for parameterization). RESULTS: To capture the variation of within-host dynamics, when studying the overall parasitaemia profile, two clusters efficiently grouped patients based on their infection length and the height of the first parasitaemia peak. When studying the first wave of parasitaemia, five clusters efficiently grouped patients based on the height of the peak and the speed of the clearance following the peak of parasitaemia. The clusters were based on features that summarize the strength of patient innate and adaptive immune responses. Parameterizing previous within host-models based on cluster centroid values accurately predict individual patient parasitaemia profiles. CONCLUSION: This study confirms that patients have personalized immune responses, which explains the variation of parasitaemia dynamics. Clustering can guide the optimal inclusion of within-host variation in future studies, and inform the design and parameterization of population-based models.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Disease Modelling > Disease Modelling and Intervention Dynamics (Penny)
UniBasel Contributors:Masserey, Thiery and Penny, Melissa and Lee, Tamsin
Item Type:Article, refereed
Article Subtype:Research Article
ISSN:1475-2875 (Electronic)1475-2875 (Linking)
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
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Last Modified:27 Dec 2022 12:36
Deposited On:27 Dec 2022 12:36

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