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

BackgroundAgent-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). Methodology/Principal FindingsWe 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. Conclusions/SignificanceOur approach represents a novel, convenient and versatile method to model Plasmodium falciparum infection.

背景 基于智能体的恶性疟原虫（Plasmodium falciparum）感染建模相较于传统的Ross-Macdonald方法论（Ross-Macdonald methodology）具有显著优势，其可对受真实传播（接种模式）影响的异质性群落开展模拟。
方法/主要结果 我们开发了一款全新的基于智能体模型，该模型覆盖了宿主体内的核心生理过程：疟原虫的增殖及其受先天免疫与适应性免疫的调控。本模型同时整合了恶性疟原虫抗原变异的简化版本。我们利用疟疾治疗（malaria-therapy, MT）研究的数据对模型进行校准，并提出了一种全新的校准流程，可同时考量观测到的疟原虫密度模式中的确定性分量与伪随机分量。基于122例MT患者的疟原虫密度数据，我们生成了大量经校准的参数集。所得数据集被用于构建并模拟类MT宿主的异质性基于智能体（agent-based, AB）群落。我们开展了多项数值实验，使AB群落暴露于既往实地研究中报道的真实接种模式，并将模型输出与实地观测到的疟疾流行率进行对比。结果整体吻合，证实了该基于智能体的方法在真实群落疟疾传播建模中的应用潜力。
结论与意义 我们的方法为恶性疟原虫感染建模提供了一种新颖、便捷且通用性强的技术手段。