  The recent global outbreak of covid-19 is affecting many countries around the
world. Due to the growing number of newly infected individuals and the
health-care system bottlenecks, it will be useful to predict the upcoming
number of patients. This study aims to efficiently forecast the is used to
estimate new cases, number of deaths, and number of recovered patients in Iran
for 180 days, using the official dataset of the Iranian Ministry of Health and
Medical Education and the impact of control measures on the spread of COVID-19.
Four different types of forecasting techniques, time series, and machine
learning algorithms, are developed and the best performing method for the given
case study is determined. Under the time series, we consider the four
algorithms including Prophet, Long short-term memory, Autoregressive,
Autoregressive Integrated Moving Average models. On comparing the different
techniques, we found that deep learning methods yield better results than time
series forecasting algorithms. More specifically, the least value of the error
measures is observed in seasonal ANN and LSTM models. Our findings showed that
if precautionary measures are taken seriously, the number of new cases and
deaths will decrease, and the number of deaths in September 2021 will reach
zero.
