Healthc Inform Res.  2019 Jul;25(3):182-192. 10.4258/hir.2019.25.3.182.

Nature of Complex Network of Dengue Epidemic as a Scale-Free Network

• Malik HA
• Abid F
• Mahmood N
• Wahiddin M
• Malik A

• KMID: 2457470
• DOI: http://doi.org/10.4258/hir.2019.25.3.182

Abstract

OBJECTIVES
Dengue epidemic is a dynamic and complex phenomenon that has gained considerable attention due to its injurious effects. The focus of this study is to statically analyze the nature of the dengue epidemic network in terms of whether it follows the features of a scale-free network or a random network.
METHODS
A multifarious network of Aedes aegypti is addressed keeping the viewpoint of a complex system and modelled as a network. The dengue network has been transformed into a one-mode network from a two-mode network by utilizing projection methods. Furthermore, three network features have been analyzed, the power-law, clustering coefficient, and network visualization. In addition, five methods have been applied to calculate the global clustering coefficient.
RESULTS
It has been observed that dengue epidemic follows a power-law, with the value of its exponent γ = −2.1. The value of the clustering coefficient is high for dengue cases, as weight of links. The minimum method showed the highest value among the methods used to calculate the coefficient. Network visualization showed the main areas. Moreover, the dengue situation did not remain the same throughout the observed period.
CONCLUSIONS
The results showed that the network topology exhibits the features of a scale-free network instead of a random network. Focal hubs are highlighted and the critical period is found. Outcomes are important for the researchers, health officials, and policy makers who deal with arbovirus epidemic diseases. Zika virus and Chikungunya virus can also be modelled and analyzed in this manner.

Keyword

Dengue Virus; Arboviruse; Epidemics; Big Data; Network Meta-Analysis

MeSH Terms

Administrative Personnel
Aedes
Arboviruses
Chikungunya virus
Critical Period (Psychology)
Dengue Virus
Dengue*
Humans
Methods
Zika Virus

Figure

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  Figure 1 Dengue cases in Gombak, Selangor (2013–2014).

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  Figure 2 Dengue cases in Hulu Langat, Selangor (2013–2014).

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  Figure 3 Dengue cases in Petaling, Selangor (2013–2014).

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  Figure 4 Probability distribution of node strength distribution on log scale in Selangor (2013–2014). The x-axis shows the strength of links and the y-axis shows the probability distribution of links strength.

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  Figure 5 Links density using weighted Newman method of projection.

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  Figure 6 Week-wis e comparison among six districts (October 20, 2013 to October 18, 2014).

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  Figure 7 Different methods of global clustering coefficients (comparison) in two-mode network. Bi: binary, AM: arithmetic mean, GM: geometric mean, Max: maximum, Min: minimum.

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  Figure 8 Graphical view of dengue network in Gombak.

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  Figure 9 Real map of Gombak, Malaysia. The map showed dengue affected noedes (GL is Gombak, location no. 1 to 58), image captured from Google maps and modified.

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