Mathematical Modeling of Malaria Infection with Innate and Adaptive Immunity in Individuals and Agent-Based Communities

Department of Mathematics, Case Western Reserve University, Cleveland, Ohio, United States of America.
PLoS ONE (Impact Factor: 3.23). 03/2012; 7(3):e34040. DOI: 10.1371/journal.pone.0034040
Source: PubMed


Agent-based modeling of Plasmodium falciparum infection offers an attractive alternative to the conventional Ross-Macdonald methodology, as it allows simulation of heterogeneous communities subjected to realistic transmission (inoculation patterns).
We developed a new, agent based model that accounts for the essential in-host processes: parasite replication and its regulation by innate and adaptive immunity. The model also incorporates a simplified version of antigenic variation by Plasmodium falciparum. We calibrated the model using data from malaria-therapy (MT) studies, and developed a novel calibration procedure that accounts for a deterministic and a pseudo-random component in the observed parasite density patterns. Using the parasite density patterns of 122 MT patients, we generated a large number of calibrated parameters. The resulting data set served as a basis for constructing and simulating heterogeneous agent-based (AB) communities of MT-like hosts. We conducted several numerical experiments subjecting AB communities to realistic inoculation patterns reported from previous field studies, and compared the model output to the observed malaria prevalence in the field. There was overall consistency, supporting the potential of this agent-based methodology to represent transmission in realistic communities.
Our approach represents a novel, convenient and versatile method to model Plasmodium falciparum infection.

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Available from: Charles H King, Oct 08, 2015
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    • "Very recently Gurarie et al. (see ref. [18]) implemented a discrete time computer model for the case of malaria. This modeling approach, termed agent-based, consists in a set of coupled difference equations that describe the transition between successive iterations of the parasite population (i.e. "
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    ABSTRACT: We present a novel model that describes the within-host evolutionary dynamics of parasites undergoing antigenic variation. The approach uses a multi-type branching process with two types of entities defined according to their relationship with the immune system: clans of resistant parasitic cells (i.e. groups of cells sharing the same antigen not yet recognized by the immune system) that may become sensitive, and individual sensitive cells that can acquire a new resistance thus giving rise to the emergence of a new clan. The simplicity of the model allows analytical treatment to determine the subcritical and supercritical regimes in the space of parameters. By incorporating a density-dependent mechanism the model is able to capture additional relevant features observed in experimental data, such as the characteristic parasitemia waves. In summary our approach provides a new general framework to address the dynamics of antigenic variation which can be easily adapted to cope with broader and more complex situations. Copyright © 2015. Published by Elsevier Ltd.
    Journal of Theoretical Biology 09/2015; 380:489-498. DOI:10.1016/j.jtbi.2015.06.025 · 2.12 Impact Factor
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    • "In very low transmission settings such as Cambodia, asymptomatic infections remain the major reservoir of malaria parasites contributing to maintain disease transmission [9-11]. As a consequence, the detection and treatment of the asymptomatic carriers is a crucial step in progress towards malaria elimination [12]. "
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    • "The other burden, which complicates the understanding of time-trend of malaria cases, is the difficulty to measure the exposure to infectious mosquito bite. Models have been developed in various aspects such as, to understand the acquisition of immunity to malaria (Filipe et al. 2007; Ghani et al. 2009; Gurarie et al. 2012), impact of ACT and other interventions on malaria prevalence (Okell et al. 2008; Griffin et al. 2010), impact of domestic animals or genetically-modified mosquitoes on the transmission of malaria (Rafikov et al. 2009; Nah et al. 2010; Diaz et al. 2011), effects of weather and climate change on malaria transmission (Hoshen and Morse 2004; Chatterjee and Sarkar 2009; Parham and Michael 2010; Ermert et al. 2011; Author's personal copy Roy et al. 2011). All these models (simple or complex) are capable of addressing specific questions of interest. "
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