Dengue propagation prediction using human mobility

M. A. Abeyrathna, D. A. Abeygunawrdane, R. A. A. V. Wijesundara, V. B. Mudalige, M. Bandara, Shehan Perera, Danaja Maldeniya, Kaushalya Madhawa, Sriganesh Locknathan
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引用次数: 7

Abstract

This paper discusses about predicting Dengue out-breaks in Sri Lanka using heterogeneous data sets: Mobile Network Big data and epidemiological data. Up to now, Dengue epidemiological prediction was largely done using the past Dengue cases and weather data. However, very recently it was discovered that infection can propagate through humans, where an infected human travels to a vulnerable area and mosquitoes of that area will bite him, and contract and spread the virus. Hence this research explores the potential of incorporating human mobility, derived through mobile network data in predicting Dengue propagation. This paper presents the various data sources, how the data fusion was conducted and how the fused data was fed into the model and the results obtained and a discussion thereof, including the potential of extending the research.
利用人的流动性预测登革热的传播
本文讨论了使用异构数据集预测斯里兰卡登革热疫情:移动网络大数据和流行病学数据。到目前为止,登革热流行病学预测主要是利用过去的登革热病例和天气数据进行的。然而,最近发现,感染可以通过人类传播,当一个受感染的人到达一个脆弱的地区,该地区的蚊子会叮咬他,感染并传播病毒。因此,本研究探索了将通过移动网络数据得出的人类移动性纳入预测登革热传播的潜力。本文介绍了各种数据源,如何进行数据融合,如何将融合的数据输入到模型中,以及如何获得结果,并对此进行了讨论,包括扩展研究的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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