Mechanistic within-host models of the asexual; Plasmodium falciparum; infection: a review and analytical assessment

Camponovo, F. and Lee, T. E. and Russell, J. R. and Burgert, L. and Gerardin, J. and Penny, M. A.. (2021) Mechanistic within-host models of the asexual; Plasmodium falciparum; infection: a review and analytical assessment. Malar J, 20. p. 309.

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BACKGROUND: Malaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the host's immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models. METHODS: Mechanistic within-host models of parasite dynamics were identified through a review of published literature. For a subset of these, model code was reproduced and descriptive statistics compared between the models using fitted data. Through simulation and model analysis, key features of the models were compared, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability. RESULTS: The assessed within-host malaria models generally replicate infection dynamics in malaria-naive individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitaemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models. CONCLUSIONS: This study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, it is proposed that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterization and large stochasticity which inaccurately represent unknown disease mechanisms.
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:Camponovo, Flavia and Lee, Tamsin and Burgert, Lydia and Penny, Melissa
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
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Last Modified:19 Dec 2022 12:48
Deposited On:19 Dec 2022 12:48

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