Impact of lockdowns on the spread of COVID-19 in Saudi Arabia. (2020)

• Record Type: Journal Article
• Authors: Alrashed, Saleh; Min-Allah, Nasro; Saxena, Arnav; Ali, Ijaz; Mehmood, Rashid
• Is Part Of: Informatics in medicine unlocked. Volume 20(2020)
• Publication Date: 2020
• Subjects: COVID-19 -- Disease spread -- Social distancing -- Prediction model -- SEIR -- Saudi Arabia; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2020.100420
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Epidemiological models have been used extensively to predict disease spread in large populations. Among these models, Susceptible Infectious Exposed Recovered (SEIR) is considered to be a suitable model for COVID-19 spread predictions. However, SEIR in its classical form is unable to quantify the impact of lockdowns. In this work, we introduce a variable in the SEIR system of equations to study the impact of various degrees of social distancing on the spread of the disease. As a case study, we apply our modified SEIR model on the initial spread data available (till April 9, 2020) for the Kingdom of Saudi Arabia (KSA). Our analysis shows that with no lockdown around 2.1 million people might get infected during the peak of spread around 2 months from the date the lockdown was first enforced in KSA (March 25th). On the other hand, with the Kingdom's current strategy of partial lockdowns, the predicted number of infections can be lowered to 0.4 million by September 2020. We further demonstrate that with a stricter level of lockdowns, the COVID-19 curve can be effectively flattened in KSA.