Pandemics pose a huge threat to the global community because, in addition to the deadly consequences, they end up causing irreparable damage to the global economy and widespread poverty especially in underdeveloped countries that lack the resources to stem the tide. Some estimates show that potential pandemics may cost over 500 billion dollars per year. Thus, investing resources and effort in global health security and improving our ability to prevent, detect, and respond to diseases has become imperative. Currently, we are in the midst of the deadly coronavirus pandemic. It has brought two important points to light. Firstly, we need a system to suppress the virus at the initial stages and the undertaken strategies must be economical so that they can be scaled. In this project, we have attempted to approach the problem of economical curing policy using Genetic Algorithms and find a suitable solution.

This project is structured as follows. In Part I, we describe the concepts of epidemic modeling and implement the single objective cost minimization problem formulated in Ref[1]. This is followed by Part-II, where we extend this idea to find an optimal curing policy in a scenario where certain nodes are very highly infected as compared to the others (maybe referred to pandemic hotspots). We achieve this by solving a multiobjective optimization problem where cost is minimized and the average curing rate of the hotspots is maximized. Find our complete report here
Contributors : Nesara S R, Ankit Kumar