Spatio-temporal correlation networks of dengue in the state of Bahia

被引:20
作者
Saba, Hugo [1 ]
Vale, Vera C. [1 ]
Moret, Marcelo A. [1 ,2 ]
Miranda, Jose Garcia V. [3 ]
机构
[1] Univ Estado Bahia, Salvador, BA, Brazil
[2] Senai Cimatec, Salvador, BA, Brazil
[3] Univ Fed Bahia, Inst Phys, Salvador, BA, Brazil
关键词
Dengue; Correlation; Transport; Randomization; Bahia; IMPACT;
D O I
10.1186/1471-2458-14-1085
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Dengue is a public health problem that presents complexity in its dissemination. The physical means of spreading and the dynamics of the spread between municipalities need to be analyzed to guide effective public policies to combat this problem. Methods: This study uses timing varying graph methods (TVG) to construct a correlation network between occurrences of reported cases of dengue between cities in the state of Bahia-Brazil. The topological network indices of all cities were correlated with dengue incidence using Spearman correlation. A randomization test was used to estimate the significance value of the correlation. Results: The correlation network presented a complex behavior with a heavy-tail distribution of the network edges weight. The randomization test exhibit a significant correlation (P < 0.0001) between the degree of each municipality in the network and the incidence of dengue in each municipality. Conclusions: The hypothesis of the existence of a correlation between the occurrences of reported cases of dengue between different municipalities in the state of Bahia was validated. The significant correlation between the node degree and incidence, indicates that municipalities with high incidence are also responsible for the spread of the disease in the state. The method proposed suggests a new tool in epidemiological control strategy.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Modelling spatio-temporal data of dengue fever using generalized additive mixed models
    Cabrera, M.
    Taylor, G.
    SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY, 2019, 28 : 1 - 13
  • [32] Spatio-Temporal Correlation Guided Geometric Partitioning for Versatile Video Coding
    Meng, Xuewei
    Jia, Chuanmin
    Zhang, Xinfeng
    Wang, Shanshe
    Ma, Siwei
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 31 : 30 - 42
  • [33] Spatio-Temporal Variation in AOD and Correlation Analysis with PAR and NPP in China from 2001 to 2017
    Li, Xin
    Liang, Hongyu
    Cheng, Weiming
    REMOTE SENSING, 2020, 12 (06)
  • [34] Spatio-temporal dynamics in graphene
    Jago, Roland
    Perea-Causin, Rauel
    Brem, Samuel
    Malic, Ermin
    NANOSCALE, 2019, 11 (20) : 10017 - 10022
  • [35] Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning
    Zhang, Junbo
    Zheng, Yu
    Sun, Junkai
    Qi, Dekang
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 32 (03) : 468 - 478
  • [36] Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia
    Otero, Julian
    Tabares, Alejandra
    Santos-Vega, Mauricio
    VIRUSES-BASEL, 2024, 16 (06):
  • [37] Assessing spatio-temporal trend of vector breeding and dengue fever incidence in association with meteorological conditions
    Malik, Afifa
    Yasar, Abdullah
    Tabinda, Amtul Bari
    Zaheer, Ihsan Elahi
    Malik, Khalida
    Batool, Adeeba
    Mahfooz, Yusra
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2017, 189 (04)
  • [38] Spatio-Temporal Correlation of Interference in MANET Under Spatially Correlated Shadowing Environment
    Kimura, Tatsuaki
    Saito, Hiroshi
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2021, 20 (04) : 1642 - 1655
  • [39] STFGCN: Spatio-Temporal Fusion Graph Convolutional Networks for Subway Traffic Prediction
    Zhang, Xiaoxi
    Tian, Zhanwei
    Shi, Yan
    Guan, Qingwen
    Lu, Yan
    Pan, Yujie
    IEEE ACCESS, 2024, 12 : 194449 - 194461
  • [40] Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs
    Kwak, Semin
    Geroliminis, Nikolas
    Frossard, Pascal
    IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, 2021, 7 (07): : 648 - 659