Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Infect Dis Poverty. 2012 Nov 1;1(1):11. doi: 10.1186/2049-9957-1-11.
Malaria transmission can be affected by multiple or even hidden factors, making it difficult to timely and accurately predict the impact of elimination and eradication programs that have been undertaken and the potential resurgence and spread that may continue to emerge. One approach at the moment is to develop and deploy surveillance systems in an attempt to identify them as timely as possible and thus to enable policy makers to modify and implement strategies for further preventing the transmission. Most of the surveillance data will be of temporal and spatial nature. From an interdisciplinary point of view, it would be interesting to ask the following important as well as challenging question: Based on the available surveillance data in temporal and spatial forms, how can we build a more effective surveillance mechanism for monitoring and early detecting the relative prevalence and transmission patterns of malaria? What we can note from the existing clustering-based surveillance software systems is that they do not infer the underlying transmission networks of malaria. However, such networks can be quite informative and insightful as they characterize how malaria transmits from one place to another. They can also in turn allow public health policy makers and researchers to uncover the hidden and interacting factors such as environment, genetics and ecology and to discover/predict malaria transmission patterns/trends. The network perspective further extends the present approaches to modelling malaria transmission based on a set of chosen factors. In this article, we survey the related work on transmission network inference, discuss how such an approach can be utilized in developing an effective computational means for inferring malaria transmission networks based on partial surveillance data, and what methodological steps and issues may be involved in its formulation and validation.
疟疾传播可能受到多种甚至隐藏因素的影响，这使得及时、准确地预测已开展的消除和根除规划的影响以及可能持续出现的潜在复发和传播变得困难。目前的一种方法是开发和部署监测系统，试图尽可能及时地发现这些问题，从而使决策者能够修改和实施进一步防止传播的战略。大多数监测数据将具有时间和空间性质。从跨学科的角度来看，提出以下重要而具有挑战性的问题将是很有趣的：基于现有的时间和空间形式的监测数据，我们如何构建一个更有效的监测机制，以监测和早期发现疟疾的相对流行率和传播模式？从现有的基于聚类的监测软件系统中我们可以注意到，它们不会推断疟疾的潜在传播网络。然而，这些网络可以提供非常有启发性的信息，因为它们描述了疟疾如何从一个地方传播到另一个地方。它们还可以反过来帮助公共卫生政策制定者和研究人员发现隐藏的相互作用因素，如环境、遗传和生态，并发现/预测疟疾的传播模式/趋势。网络视角进一步扩展了目前基于一组选定因素来模拟疟疾传播的方法。在本文中，我们调查了有关传播网络推断的相关工作，讨论了如何在基于部分监测数据推断疟疾传播网络方面利用这种方法，以及在其制定和验证过程中可能涉及哪些方法步骤和问题。
