Utilizing Auto-Regressive Integrated Moving Average to Predict Newly Coronavirus Cases in Libya

Abstract

Currently, Coronavirus is a major worldwide threat. It has affected millions of people around the world, resulting in hundreds of thousands of deaths. It is indeed important to forecast the number of new cases in aims to assist in disease prevention and healthcare service readiness. Many researchers used different mathematical and machine learning methods to forecast the pandemic's future trend. This research proposes an autoregressive integrated moving average model to forecast the estimated daily new cases in Libya over the next three months. The total number of confirmed cases is pre-processed and used to forecast the virus's spread. The cumulative number of confirmed cases is pre-processed and used to forecast the virus's spread. Based on the result obtained from the experiment, the number of cases expected to rise in the near future, reaching up to 1250 new cases every day. This research would help the government and medical staff members to plan for the upcoming conditions, as a result, increase the readiness of healthcare systems