Monday Eze et al., International Journal of Emerging Trends in Engineering Research, 8(7), July 2020, 2934 - 2939 2934 ABSTRACT The necessity for contact tracing in the fight against infectious diseases including pandemics like COVID 19 cannot be overemphasized. One of the obvious challenges is how to device practical strategies for computational evolution and construction of a homogenous network for contact tracing. A second challenge is how to evolve practical tools and algorithms for the visualization of contact network that models transmission. This research evolves a new algorithm which first, builds a new specialized data structure known as PiVector, and then a corresponding contact network visualization system. The resulting network could be used for contact tracing of the transmission of infectious diseases. This work practically demonstrates a programmatic and data-driven approach to network evolution, construction and visualization. This work was implemented using Python Programing Language and other related data mining technologies. Key words: Homogenous, Contact Network, PiVector Construction, Visualization, Python. 1. INTRODUCTION Network Theory has found its application in several research fields as well as diverse areas of human endeavours [1]. Some of the application areas are social networks, biological networks, semantic networks used in computational linguistics, cognitive networks in neurosciences, transportation networks, and telecommunication networks, among others. Literature has shown that the evolutionary foundation of all these networks is the mathematical graph [2]. A graph is defined as a collection of vertices, which are connected to each other through edges. In other words, each edge of graph joins two vertices [3]. A network is an instantiation of a graph, such that the nodes and edges have specific identities. For instance, a social network comprises of nodes as human beings, while the edges are the links or relationships between those human beings [4]. Again, a road network comprises of nodes as cities, while the edges are the actual roads linking the cities [5]. Research has underlined the necessity for efficient algorithms and programmatic techniques for generating complex networks [6]. This current work demonstrates how to achieve this goal, in a procedural manner, beginning from the workflow stage, to the implementation of the proposed algorithm. Infectious diseases spread through human contacts [7], which could be modelled as a network. In order to control the spread of such diseases, it is very necessary to successfully locate the infected persons, and all the yet-to-be-infected contacts, so they could be isolated or treated as the case may be. This activity is known as contact tracing [8]. The necessity for contact tracing and its automation in the fight against pandemics like COVID 19 [9] cannot be overemphasized. Epidemiologists [10] cannot afford to depend on manual contact tracing. Thus the need for this research, which is geared at developing a new computational algorithm [11] for network evolution and visualization. Tackling the challenges of devising practical strategies for computational evolution and construction of a homogenous network for contact tracing are the major deliverables of this work. This research evolves a new specialized data structure known as PiVector, then uses it as input to construct the network for use in contact tracing of infectious diseases transmission. 2. EVOLUTIONARY CONCEPTS This section presents some evolutionary concepts [12] which constitute the foundation of this research. In order to effectively generate a computational network, it is necessary to propose a requisite data structure [13]. As shown in Figure 1, the spread of a pandemic such as COVID-19 begins with the establishment of a contact with an infected person (H1), and then a successful transmission from the infected case to another human being (H2). A New Algorithm for Contact Trace Network Evolution and Visualization Monday Eze 1 , Chigozirim Ajaegbu 2 , Olusola Maitanmi 3 , Doris Nnakwuzie 4 1 Department of Computer Science, Babcock University, Nigeria. ezem@babcock.edu.ng 2 Department of Computer Science, Babcock University, Nigeria. ajaegbuc@babcock.edu.ng 3 Department of Software Engineering, Babcock University, Nigeria. maitanmio@babcock.edu.ng 4 Dept. of Computer Science, Alex Ekwueme Federal University, Nigeria. okafordoris49@yahoo.com ISSN 2347 - 3983 Volume 8. No. 7, July 2020 International Journal of Emerging Trends in Engineering Research Available Online at http://www.warse.org/IJETER/static/pdf/file/ijeter09872020.pdf https://doi.org/10.30534/ijeter/2020/09872020