Mikler Armin R, Venkatachalam Sangeeta, Ramisetty-Mikler Suhasini
1Computational Epidemiology Research Laboratory, Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203 USA.
2Dallas Regional Campus, University of Texas School of Public Health, Dallas, TX 75390 USA.
Stoch Environ Res Risk Assess. 2007;21(5):533. doi: 10.1007/s00477-007-0137-y. Epub 2007 Apr 17.
Emerging infectious diseases continue to place a strain on the welfare of the population by decreasing the population's general health and increasing the burden on public health infrastructure. This paper addresses these issues through the development of a computational framework for modeling and simulating infectious disease outbreaks in a specific geographic region facilitating the quantification of public health policy decisions. Effectively modeling and simulating past epidemics to project current or future disease outbreaks will lead to improved control and intervention policies and disaster preparedness. In this paper, we introduce a computational framework that brings together spatio-temporal geography and population demographics with specific disease pathology in a novel simulation paradigm termed, global stochastic field simulation (GSFS). The primary aim of this simulation paradigm is to facilitate intelligent what-if-analysis in the event of health crisis, such as an influenza pandemic. The dynamics of any epidemic are intrinsically related to a region's spatio-temporal characteristics and demographic composition and as such, must be considered when developing infectious disease control and intervention strategies. Similarly, comparison of past and current epidemics must include demographic changes into any effective public health policy for control and intervention strategies. GSFS is a hybrid approach to modeling, implicitly combining agent-based modeling with the cellular automata paradigm. Specifically, GSFS is a computational framework that will facilitate the effective identification of risk groups in the population and determine adequate points of control, leading to more effective surveillance and control of infectious diseases epidemics. The analysis of past disease outbreaks in a given population and the projection of current or future epidemics constitutes a significant challenge to Public Health. The corresponding design of computational models and the simulation that facilitates epidemiologists' understanding of the manifestation of diseases represents a challenge to computer and mathematical sciences.
新发传染病持续给民众福祉带来压力，它降低了民众的总体健康水平，增加了公共卫生基础设施的负担。本文通过开发一个计算框架来解决这些问题，该框架用于对特定地理区域内的传染病爆发进行建模和模拟，有助于对公共卫生政策决策进行量化。有效地对过去的疫情进行建模和模拟，以预测当前或未来的疾病爆发，将带来更好的控制和干预政策以及备灾措施。在本文中，我们引入了一个计算框架，该框架在一种名为全球随机场模拟（GSFS）的新颖模拟范式中，将时空地理和人口统计学与特定疾病病理学结合在一起。这种模拟范式的主要目的是在发生健康危机（如流感大流行）时促进智能的假设分析。任何疫情的动态都与一个地区的时空特征和人口构成有着内在联系，因此，在制定传染病控制和干预策略时必须予以考虑。同样，在制定任何有效的控制和干预策略的公共卫生政策时，对过去和当前疫情的比较必须纳入人口变化因素。GSFS是一种混合建模方法，它将基于主体的建模与细胞自动机范式隐式结合。具体而言，GSFS是一个计算框架，它将有助于有效识别群体中的风险人群，并确定适当的控制节点，从而更有效地监测和控制传染病疫情。分析特定人群过去的疾病爆发情况以及预测当前或未来的疫情，对公共卫生构成了重大挑战。相应的计算模型设计以及有助于流行病学家理解疾病表现的模拟，对计算机科学和数学科学来说也是一项挑战。
