A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data

Tao, Haiyan; Wang, Keli; Li, Zhuo; Li, Xuliang; Li, Qiuping; Liu, Yuan; Xu, Yue

doi:10.1080/13658816.2019.1684497

Cited by 9 publications

(11 citation statements)

References 42 publications

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“…Other than symptoms and environmental factors, many research works have indicated the impact of socio-geographical factors [48,50] as well as human movement [23,32,44] on the transmission of DeV and its outbreak. One such work by Tao et al [46] had presented a model that depicts the role of the infected humans' movement in contributing DeV infection outbreak and suggested that the restricted movement in infected areas can lower the diffusion risk of DeV infection.…”

Section: Dengue-based Healthcarementioning

confidence: 99%

An intelligent healthcare system for predicting and preventing dengue virus infection

Sood

Mahajan

et al. 2021

Computing

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Section: Dengue-based Healthcarementioning

confidence: 99%

An intelligent healthcare system for predicting and preventing dengue virus infection

Sood

Mahajan

et al. 2021

Computing

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“…For this reason, accurately forecasting the spatial distribution of dengue cases within a city is important for government agencies to establish early and targeted prevention and PLOS NEGLECTED TROPICAL DISEASES control. While on the other hand, even though researchers have realized the importance of human mobility in virus transmission [26][27][28][29], the real population movement data have not been well utilized in dengue forecasting.…”

Section: Discussionmentioning

confidence: 99%

“…With the exclusion of past cases and meteorological variables, the population [14,16,23], Internet search index [12,10,19], street view images [25], and dengue-related phone calls from telephone triage services [20] have also been proven as useful predictors for dengue forecasting. In addition, the influence of human mobility [26][27][28][29], land use [30], road network [3,31], population structure [32], and urban village [4] on dengue transmission has also been investigated.…”

Section: Introductionmentioning

confidence: 99%

“…This study aimed to establish a fine-grained intra-urban dengue forecasting framework for identifying target areas with greater risk in the near future. Considering that highly dynamic population flows greatly facilitate the spread of virus within cities [ 26 ], integrating spatial interactions between urban regions would be useful for dengue forecasting. To achieve this goal, this study ingeniously introduced the graph-embedding technique to capture spatial interactions of human movements between urban regions.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions

Liu

Zhang

et al. 2020

PLoS Negl Trop Dis

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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.

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“…Although there were nine distinct statistical models, all of them used one of three methods to account for spatial connectivity: inclusion of spatial covariates as fixed effects, localized regression models or the inclusion of a spatially structured random effect or random field. incidence in connected regions [21][22][23][24][25][26][27][28][29][30], the number of people moving between regions [20,[31][32][33][34][35], the distance between regions [31,[35][36][37], coordinates of the centroid of a region [38][39][40], the number of time spent commuting between regions [41] and spatial eigenvectors created using spatial filtering [42][43][44]. Spatial filtering creates spatial covariates by decomposing Moran's I (a measure of spatial correlation) into an eigenvector per region/observation [45].…”

Section: Statistical Modelsmentioning

confidence: 99%

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Lee

Jarvis

Edmunds

et al. 2021

J. R. Soc. Interface.

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Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

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A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data

Cited by 9 publications

References 42 publications

An intelligent healthcare system for predicting and preventing dengue virus infection

An intelligent healthcare system for predicting and preventing dengue virus infection

Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

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