Abstract
In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.
Highlights
According to the World Health Organism (WHO), dengue affects over half of the global population, with an estimated 100–400 million infections each year worldwide [1]
Satellite Earth observation (EO) has been increasingly used in dengue research over the past years, especially for the identification of dengue landscape factors
Various types of landscape factors were considered while the study areas and research objectives have become more complex, and the variety and volume of satellite EO data have been growing over these years
Summary
According to the World Health Organism (WHO), dengue affects over half of the global population, with an estimated 100–400 million infections each year worldwide [1]. It would seem to be more appropriate to implement text classification based on a new bibliographic dataset for selecting relevant records, while the labelled data derived from active learning could be used as training data to train the DL architecture [22]. In this context, focusing on landscape factors affecting dengue transmission and satellite EO data currently used for identifying landscape factors, this study proposes to build a semi-supervised classification framework of literature by integrating the review process and text classification algorithms and provides an overview of dengue landscape factors and satellite EO data. The proposed framework allows for rational and effective selection of literature relevant to our objective from bibliographic databases
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