Modified SEIR and machine learning prediction of the trend of the epidemic of COVID-19 in Jordan under lockdowns impact

Abstract

Susceptible exposed infectious recovered (SEIR) is a fitting model for coronavirus disease (COVID-19) spread prediction. Hence, to examine the effect of different levels of social distancing on the spreading of the disease, a variable was introduced in the SEIR equations system used in this work. We also used an artificial intelligence approach using a machine learning (ML) method known as deep neural network. This modified SEIR model was applied on the available initial spread data until June 25th, 2021 for the Hashemite Kingdom of Jordan. Without lockdown in Jordan, the analysis demonstrates potential infection to roughly 3.1 million people during the peak of spread approximately 3 months, starting from the date of lockdown (March 21st). Conversely, the present partial lockdowns strategy by the Kingdom was expected to reduce the predicted number of infections to 0.5 million in 9 months period. The analysis also demonstrates the ability of stricter lockdowns to effectively flatten the graph curve of COVID-19 in Jordan. Our modified SEIR and deep neural network (DNN) model were efficient in the prediction of COVID-19 epidemic sizes and peaks. The measures taken to control the epidemic by the government decreased the size of the COVID-19 epidemic.