期刊论文详细信息
BMC Public Health
Spatio-temporal correlation networks of dengue in the state of Bahia
José Garcia V Miranda2  Marcelo A Moret3  Vera C Vale1  Hugo Saba1 
[1]Universidade do Estado da Bahia, Salvador, Bahia, Brasil
[2]Physics Institute - Universidade Federal da Bahia, Salvador, Brasil
[3]Senai/Cimatec, Salvador, Bahia, Brasil
关键词: Bahia;    Randomization;    Transport;    Correlation;    Dengue;   
Others  :  1126116
DOI  :  10.1186/1471-2458-14-1085
 received in 2014-08-06, accepted in 2014-10-10,  发布年份 2014
PDF
【 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.

【 授权许可】

   
2014 Saba et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150218070212443.pdf 805KB PDF download
Figure 5. 48KB Image download
Figure 4. 46KB Image download
Figure 3. 72KB Image download
Figure 2. 68KB Image download
Figure 1. 72KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】
  • [1]Braga IA, Valle D: Aedes aegypti: histórico do controle no Brasil [Aedes aegypti: history of control in Brazil]. Epidemiol Serv Saúde Brasília 2007., 16
  • [2]Guo X, Xu Y, Bian G, Pike AD, Xie Y, Xi Z: Response of the mosquito protein interaction network to dengue infection. BMC Genomics 2010, 11:380. BioMed Central Full Text
  • [3]Kappagoda S, Ioannidis J: Prevention and control of neglected tropical diseases: overview of randomized trials, systematic reviews and meta-analyses. Bull World Health Organ 2014, 92(5):356-366C.
  • [4]Huy R, Buchy P, Conan A, Ngan C, Ong S, Ali R, Duong V, Yit S, Ung S, Te V, Chroeung N, Pheaktra NC, Uok V, Vong S: National dengue surveillance in Cambodia, 1980−2008: insights on epidemiological and virological trends and impact of vector control interventions. Bull World Health Organ 2010, 88:650-657.
  • [5]Nogueira R, Schatzmayr H, Santos A, Cunha F, Coelho R, Souza J, Guimarães L, Araújo F, Simone E, Baran T, Teixeira M, Miagostovich G, Pereira M: Emerging Infectious Diseases. 11th edition. Atlanta: EUA; 2005:1376-1381. n. 9
  • [6]Gubler DJ, Meltzer M: Impact of dengue/dengue haemorrhagic fever on the developing world. Adv Virus Res 1999, 53:35-70.
  • [7]Roriz-Cruz M, Sprinz E, Rosset I, Goldani L, Texeira MA: Dengue and primary care: a tale of two cities. Bull World Health Organ 2010, 88:244-245.
  • [8]Eguiluz VM, Chialvo D, Cecchi GA, Baliki M, Apkarian AV: Scale-free brain functional networks. Phys Rev Lett 2005, 94:018102.
  • [9]Abe S, Suzuki N: Scale-free network of earthquakes. Europhysics Letters 2004, 65:581-586. n.4
  • [10]Santana CN, Fontes AS, dos S Cidreira MA, Almeida RB, González AP, Andrade RFS, Miranda JGV: Graph theory defining non-local dependency of rainfall in Northeast Brazil. Ecol Complex 2009, 6:272-277.
  • [11]Mutlu AY, Bernat E, Aviyente S: A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification. Comput Math Methods Med 2012, 2012:451516.
  • [12]Silva BBM, Miranda JGV, Corso G, Copelli M, Vasconcelos N, Ribeiro S, Andrade RFS: Statistical characterization of an ensemble of functional neural networks. Eur Phys J E Condensed Matter Complex Systems 2012, 85:358.
  • [13]Quaak I, Brouns MR, van de Bor M: The dynamics of autism spectrum disorders: how neurotoxic compounds and neurotransmitters interact. revista internacional da investigação ambiental e de saúde pública 2013, 10:3384-3408. n. 8
  • [14]Nakapan S, Tripathi NK, Tipdecho T, Souris M: Spatial diffusion of influenza outbreak-related climatic factors in Chiang Mai Province, Thailand. revista internacional da investigação ambiental e de saúde pública 2012, 9(11):3824-3842.
  • [15]Lin C-H, Wen T-H: Using geographically weighted regression (GWR) to explore the different spatial varying relationships of immature mosquitos and human densities with the incidence of dengue. revista internacional da investigação ambiental e de saúde pública 2011, 8(7):2798-2815.
  • [16]Lozano-Fuentes S, Elizondo-Quiroga D, Farfan-Ale JA, Loroño-Pino MA, Garcia-Rejon J, Gomez-Carro S, Lira-Zumbardo V, Najera-Vazquez R, Fernandez-Salas I, Calderon-Martinez J, Dominguez-Galera M, Mis-Avila P, Morris N, Coleman M, Moore CG, Beaty BJ, Eisen L: Use of Google Earth to strengthen public health capacity and facilitate management of vector-borne diseases in resource-poor environments. Bull World Health Organ 2008, 86(9):718-725.
  • [17]Casteigts A, Flocchini P, Quattrociocchi W, Santoro N: Time-Varying Graphs and Dynamic Networks. 2010, 20. Arxiv preprint arXiv10120009
  • [18]Flocchini P, Mans B, Santoro N: Exploration of periodically varying graphs. Proc. 20th Intl. Symposium on Algorithms and Computation (ISAAC) 2009, 534543.
  • [19]Tang J, Scellato S, Musolesi M, Mascolo C, Latora V: Small-world behavior in time-varying graphs. Phys Rev E Stat Nonlinear Soft Matter Phys 2010, 81(5 Pt 2):055101.
  • [20]Manly BFJ: Randomization, Bootstrap and Monte Carlo Methods in Biology. Flórida: Chapman & Hall; 2006:460.
  • [21]Viola DN: Detecção e modelagem de padrão eários e de contagem. Piracicaba: USP: Escola Superior de Agricultura “Luiz de Queiroz”; 2007. [Tese de Doutorado em Estatística e Experimentação Agronômica] [Detection and modeling of spatial patterns in binary and counting data. PhD thesis in Agronomic Statistics and Experimentation. USP: “Luiz de Queiroz” School of Agriculture]
  • [22]Saba H, Miranda JGV, Moret MA: Self-organized critical phenomenon as a q-exponential decay: avalanche epidemiology of dengue. Physica A Stat Mech Appl 2014, 413:205-211.
  文献评价指标  
  下载次数:61次 浏览次数:22次