Author
Listed:
- Kang Liu
- Meng Zhang
- Guikai Xi
- Aiping Deng
- Tie Song
- Qinglan Li
- Min Kang
- Ling Yin
Abstract
Background: As a mosquito-borne infectious disease, dengue fever (DF) has spread through tropical and subtropical regions worldwide in recent decades. Dengue forecasting is essential for enhancing the effectiveness of preventive measures. Current studies have been primarily conducted at national, sub-national, and city levels, while an intra-urban dengue forecasting at a fine spatial resolution still remains a challenging feat. As viruses spread rapidly because of a highly dynamic population flow, integrating spatial interactions of human movements between regions would be potentially beneficial for intra-urban dengue forecasting. Methodology: In this study, a new framework for enhancing intra-urban dengue forecasting was developed by integrating the spatial interactions between urban regions. First, a graph-embedding technique called Node2Vec was employed to learn the embeddings (in the form of an N-dimensional real-valued vector) of the regions from their population flow network. As strongly interacting regions would have more similar embeddings, the embeddings can serve as “interaction features.” Then, the interaction features were combined with those commonly used features (e.g., temperature, rainfall, and population) to enhance the supervised learning–based dengue forecasting models at a fine-grained intra-urban scale. Results: The performance of forecasting models (i.e., SVM, LASSO, and ANN) integrated with and without interaction features was tested and compared on township-level dengue forecasting in Guangzhou, the most threatened sub-tropical city in China. Results showed that models using both common and interaction features can achieve better performance than that using common features alone. Conclusions: The proposed approach for incorporating spatial interactions of human movements using graph-embedding technique is effective, which can help enhance fine-grained intra-urban dengue forecasting. Author summary: Dengue fever, a mosquito-borne infectious disease, has become a serious public health problem in many tropical and subtropical regions worldwide, such as Southeast Asian countries and the Guangdong Province in China. In the absence of an effective vaccine at present, disease surveillance and mosquito control remain the primary means of controlling the spread of the disease. At an intra-urban setting, it is important to predict the spatial distribution of future patients, which can help government agencies to establish precise and targeted prevention measures beforehand. Considering the fast virus spread within a city because of a highly dynamic population flow, we proposed a novel approach to enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions. First, using a graph-embedding model called Node2Vec, the embeddings of the regions were learned from their population interaction network so that strongly interacted regions would have more similar embeddings. Secondly, serving as interaction features, the embeddings were combined with the commonly used features as inputs of the forecasting models. The experimental results indicated that the performance of the models can be improved by incorporating the interaction features, confirming the effectiveness of our proposed strategy in enhancing fine-grained intra-urban dengue forecasting.
Suggested Citation
Kang Liu & Meng Zhang & Guikai Xi & Aiping Deng & Tie Song & Qinglan Li & Min Kang & Ling Yin, 2020.
"Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions,"
PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(12), pages 1-22, December.
Handle:
RePEc:plo:pntd00:0008924
DOI: 10.1371/journal.pntd.0008924
Download full text from publisher
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.
- Chi-Chieh Huang & Tuen Yee Tiffany Tam & Yinq-Rong Chern & Shih-Chun Candice Lung & Nai-Tzu Chen & Chih-Da Wu, 2018.
"Spatial Clustering of Dengue Fever Incidence and Its Association with Surrounding Greenness,"
IJERPH, MDPI, vol. 15(9), pages 1-12, August.
- Orratai Nontapet & Jiraporn Jaroenpool & Sarunya Maneerattanasa & Supaporn Thongchan & Chumpron Ponprasert & Patthanasak Khammaneechan & Cua Ngoc Le & Nirachon Chutipattana & Charuai Suwanbamrung, 2022.
"Effects of the Developing and Using a Model to Predict Dengue Risk Villages Based on Subdistrict Administrative Organization in Southern Thailand,"
IJERPH, MDPI, vol. 19(19), pages 1-23, September.
- Ming Sun & Xueyu Jiao, 2023.
"Quantitative Identification Study of Epidemic Risk in the Spatial Environment of Harbin City,"
Sustainability, MDPI, vol. 15(9), pages 1-22, May.
- Mazni Baharom & Norfazilah Ahmad & Rozita Hod & Fadly Syah Arsad & Fredolin Tangang, 2021.
"The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review,"
IJERPH, MDPI, vol. 18(21), pages 1-22, October.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pntd00:0008924. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosntds (email available below). General contact details of provider: https://journals.plos.org/plosntds/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.