### I. Introduction

*Aedes aegypti*’. Dengue is an arbovirus that spreads infections through mosquitoes to humans and infected humans to mosquitoes (other than

*Aedes*species), constituting a complex network. The network of arbovirus epidemic has become a complex phenomenon. In [7], the robustness of the dengue complex network under targeted versus random attack was observed and it was found that targeted attack gives better outcomes in scale-free networks compared to random networks.

*k*represents the degree of a node, the probability of node degree distribution is represented by P(

*k*), and γ (gamma) is a scaling exponent, which is a statistical parameter that is called a connectivity distribution exponent. In reality, γ does not depend on a specific scale of network that is why; it is called a scale-free parameter. Also, the value of γ has been confirmed in many research studies to approximately range from 2 to 3. R-project has been utilized to find γ and other graphical visualizations in this research.

### 1. Background

*Aedes aegypti*, which is the primary vector of dengue virus [14]. DENV-1, DENV-2, DENV-3, and DENV-4 are four serotypes that have been found in this disease [14,15,16,17,18].

#### 1) Dengue in Malaysia

#### 2) Modeling of dengue epidemic network

*Aedes aegypti*network from the viewpoint of a scale-free network and random network. In general, there are various types of networks and different ways to destroy those networks. It is very important that before attacking any network, the topology of the network should be understood. Similarly, to break down the dengue network, it must be clarified whether it should be treated as a scale-free network or a random network.

### II. Methods

### 1. Network Analysis

### III. Results

### 1. Power-Law Behavior

*Aedes aegypti*, whereas in other localities this mosquito attacked once or twice in a year. This showed the dengue has effected within this particular area. Similarly, there are a few areas that were highly affected by the dengue virus. In very few places the dengue appearance is high whereas the majority of localities have a small number of dengue cases. There is consequently a need to place greater focus on these few areas to control this disease. On the other side, identification of focal nodes in a complex networks is an important issue for researchers and scientists. Specifically, if the central node is identified, it will potentially make it possible to control the flow of other nodes. Moreover, via that node, other nodes can be captured very quickly. Hence, targeting the areas in which dengue virus is appearing repeatedly and affecting large populations may be a more helpful means to find and control the central node.

^{2}is the coefficient of determination. This statistical measure shows how good the regression line estimates the real data points. R

^{2}provides information on the goodness of fit of a model. Here, R

^{2}= 0.9213, 0.923, and 0.9102 specify that the regression line perfectly fits the data, respectively.

*SS*represents the total sum of squares and

_{tot}*SS*is the residual sum of squares.

_{res}_{in}= 2.1 and γ

_{out}= 2.7, where γ

_{in}and γ

_{out}are in-degree and out-degree, respectively. In 2001, Liljeros et al. [29] modelled and investigated human sexual connections as a network. Researchers found this societal occurrence to be scale-free and showed that it follows the power-law form (where γ

_{f}= 2.54 for females and γ

_{m}= 2.31 for males). Newman formed a scientific association network as a two-mode network, where he modelled nodes as scientists and their collaborated papers. Two scientists are linked if they worked on a joint article as primary nodes. He observed the degree distribution of this network in the case of a high energy physics databank, which follows a power-law with the exponent γ = 1.2 [1].

*Aedes aegypti*was at its peak. It can be observed from the graph that Petaling is the most affected area followed by Hulu Langat. The human populations of Gombak, Sepang, Hulu Selangor, and Klang have also been victims of

*Aedes aegypti*. The dataset showed the peak activity was from December 2013 till the end of February 2014. These twelve weeks were the most critical in these two highly affected districts. These districts had high infection rates in these 9 weeks compared to the other 3 weeks in the year. For the remaining four districts, the time series suggests activity without a clear, sustained epidemic burst between October 20, 2013 and October 18, 2014. Sepang, for instance, appears to have higher activity from October 2013 to the end of December 2013, without any significant activity in 2014. Gombak represented an isolated peak in the 25th week of 2014 and Hulu Selangor showed slightly elevated activity by the end of 2014 (41st week onwards). It is observed that out of 12 months, these 3 months showed the highest rate of dengue infections. It can be concluded that, apart from the importance of focal nodes, time duration is also important, as 3 months showed the highest rate, and also showed power-law resemblance. This feature also indicates a scale-free network.

### 2. Clustering Coefficient

^{*}w represents the values of 4-paths and τ

^{*}Δw shows the value of these 4-paths that are closed by being part of at least one 6-cycle (i.e., a loop of six links with five nodes).

### 3. Network Visualization from Localities Perspective

### IV. Discussion

*Aedes Aegypti*. Furthermore, the GMM technique would be less costly and more effective when applied to a scale-free network compared to a random network. The methods and results of this research are also important for researchers and scientists who deal with arbovirus epidemics, such as the

*Zika*and

*Chikungunya*viruses.