Modeling Multi-Host Multi-Pathogen Infectious Diseases

许多病原体--人类和动物都是一样的--协同作用。两种或两种以上病原体对同一宿主的影响可能不容易根据每种病原体单独作用的已知影响来预测。实际上，许多疾病系统是由多种宿主和多种病原体组成的。病原体和宿主种群在时间上的行为和变化是科学家和公众感兴趣的，当考虑种群保护和管理或传染病遏制时。然而，这样的系统的数学模型变得高维，并包含许多参数，需要从现有的数据来确定。他们的数学分析也不简单。作为一个研究系统，将考虑两种水蚤宿主（水蚤）和四种病原体。这两个主机也作为竞争对手的食物资源，并受到捕食。因此，它们构成了一个合适的生态流行病学系统，用于开发新的模型和技术。在建模和数学分析方面取得的进展将适用于其他疾病系统，例如蜜蜂和蝴蝶等授粉者的疾病系统。这项工作还将为研究生和本科生的跨学科培训创造机会。研究人员将与当地的高中和儿童博物馆合作，传播他们的研究结果，并吸引和培训年轻一代，重点是代表性不足的少数民族。通过数据理论耦合，将生成生物现实模型。所考虑的宿主和病原体的多样性将允许调查它们之间的广泛相互作用。由此产生的数学模型将是高度非线性的。它们将被用来形成可检验的假设，并揭示形成流行病的机制。除了基于微分方程和离散动力系统的模型外，还将开发粗粒度近似。此外，信息理论技术将被用来衡量观测和模拟的时间序列的复杂性，并揭示任何现有的模式，以及混乱。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many pathogens - human and animal alike - act in tandem. The effects of two or more pathogens on the same host might not be easy to predict based on the known effect of each pathogen acting alone. Actually, many disease systems are composed of multiple hosts and multiple pathogens. The behavior and changes of pathogen and host populations in time are of interest to scientists and the public, when population conservation and management or contagion containment are considered. However, mathematical models of such systems become high-dimensional and contain many parameters that need to be determined from existing data. Their mathematical analysis is also not straightforward. As a study system, two Daphnia hosts (waterflea) and four of their pathogens will be considered. The two hosts also act as competitors for food resources and are subject to predation. Hence, they consist a suitable eco-epidemiological system for the development of new models and techniques. Progress made in terms of modeling and mathematical analysis will be applicable to other disease systems, such as those of pollinators, like bees and butterflies. This work will also create opportunities for the interdisciplinary training of students, both graduate and undergraduate. The investigators will partner with local high-schools and a children's museum to disseminate their findings and engage and train younger generations, with emphasis on underrepresented minorities.Through data-theory coupling, biologically realistic models will be generated. The diversity of hosts and pathogens under consideration will allow the investigation of a wide range of interactions among them. The resulting mathematical models will be highly nonlinear. They will be used to form testable hypotheses and to reveal the mechanisms that shape the epidemics. Besides models based on differential equations and discrete dynamical systems, coarse grained approximations will also be developed. Moreover, information theoretical techniques will be used as measures of the complexity of observed and simulated time-series and to reveal any existing patters, as well as chaos. This integrated approach will be used to reveal insights into the drivers of epidemics and facilitate their prediction.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.