Covid-19 Spread Simulation based on control measures in UAE

Abstract

By February 2021, the total number of reported COVID-19 cases are over 110 million with 2.4 million death cases. In response to that, governments all over the world have imposed a variety of control measures including travel ban, lockdowns, events cancellation and closure of workspace and schools. In addition, continuous monitoring, contacts tracing and increasing the testing capacity were applied in most of the countries. With continuous growth of spread momentum, more strict rules are applied causing unprecedented disruption for society and economy. In this thesis, a SEIR compartmental model is proposed and developed by python-based program in order to analyze the virus transmission between different model compartments. The program executes the standard SEIR model differential equations over time under different combinations of control measures in order to examine their effectiveness on the virus development. Three control measures were discussed and analyzed in this thesis which namely are closure of schools, closure of universities and limitation of business capacity. Results show that if schools get closed, then the number of infections will surge rapidly starting from day 100 and reach a peak of 5% of population at day 150. Similarly, closing the universities will cause the number of infections to start surging at day 70 and reach a peak of 7% of population at day 120. Finally, forcing all employees to work remotely from home will lead to flattening the infection curve. Results show also that if we set the effectiveness value of control measure to 45%, then infections curve will get flattened and hence keep the infection rate under control. Finally, an optimized policy of control measures is proposed which will not only control the virus infection rate, but also will minimize the unnecessary control measures and keep the infected population below the capacity of the healthcare system.