Alrashed Saleh, Min-Allah Nasro, Saxena Arnav, Ali Ijaz, Mehmood Rashid
Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia.
Management Information Systems Department, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia.
Inform Med Unlocked. 2020;20:100420. doi: 10.1016/j.imu.2020.100420. Epub 2020 Sep 2.
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.
流行病学模型已被广泛用于预测疾病在大量人群中的传播。在这些模型中，易感-感染-暴露-康复（SEIR）模型被认为是预测新冠病毒传播的合适模型。然而，经典形式的SEIR模型无法量化封锁措施的影响。在这项工作中，我们在SEIR方程组中引入一个变量，以研究不同程度的社交距离对疾病传播的影响。作为一个案例研究，我们将改进后的SEIR模型应用于沙特阿拉伯王国（KSA）可获得的初始传播数据（截至2020年4月9日）。我们的分析表明，如果不实施封锁，从沙特阿拉伯首次实施封锁之日（3月25日）起约2个月的传播高峰期，可能有210万人感染。另一方面，按照沙特目前的部分封锁策略，到2020年9月，预测感染人数可降至40万。我们进一步证明，通过更严格的封锁措施，沙特的新冠病毒传播曲线可以有效平缓。
