Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Malaria transmission modelling: A network perspective

Language

English

Abstract

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. © 2012 Liu et al.; licensee BioMed Central Ltd.

Publication Date

2012

Source Publication Title

Infectious Diseases of Poverty

Volume

1

Issue

11

Start Page

1

End Page

8

Publisher

BioMed Central

DOI

10.1186/2049-9957-1-11

Link to Publisher's Edition

http://dx.doi.org/10.1186/2049-9957-1-11

ISSN (print)

20499957

ISSN (electronic)

20499957

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