Temporal Network (Re-)Design Strategies for Early Epidemic Containment

Abstract

This work developed a system that provides a real-time recommendation to minimise the infection rate/propagation of COVID-19 in Saudi Arabian firms. To achieve the main aim of this work, three specific objectives were identified: to study the background information of COVID-19 and its impact in Saudi Arabia; to apply the temporal network design to the dataset to study the infection rate or propagation of COVID-19 in different proposed restrictions such as social distancing, age, among others; and to implement a system to provide recommendations to minimise the infection rate in Saudi Arabian firms. The study made use of a remedial approach that seeks to formulate plans to correct or improve undesirable social, economic, political, and environmental conditions. An analysis of the undesirable conditions, the cause, and the processes associated with the conditions was performed. Plans were formulated to correct the unpalatable condition and implementation methods were then recommended. A temporal network was applied to minimise the infection rate/propagation of COVID-19 in Saudi Arabian firms. Temporal networks are time-varying networks representations that are used for analysing the development, changes, and evolution of a connected system through time. They make use of nodes and edges as the basics of a network. Three-tier architecture was used in designing and implementing the recommendation system. This project mainly used a Python programming language to design the graphical user interface (GUI) and the logic of the system. Python is an open-source programming language that has a wide range of libraries that assist developers in implementing their tasks easily. This design was chosen because the researcher was already familiar with the Python programming language. Risk assessment and control were conducted to assess the possible risks, which included challenges getting the dataset to generate the temporal network and possible mitigations including simulating dummy data for contact information with time series using Python programming. This work, therefore, presented a designed system that provides real- time recommendations to minimise the rate of infection/propagation of COVID-19 in Saudi Arabian firms and was observed to be highly effective in testing 10 and 20 employees within a one-hour time frame each.