# Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning

arXiv: https://arxiv.org/abs/2103.08178 · HTML: https://mlsanity.com/paper/2103.08178/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Epidemic processes in complex networks](https://mlsanity.com/paper/1408.2701/) (2014)
- [Forecasting Economics and Financial Time Series: ARIMA vs. LSTM](https://mlsanity.com/paper/1803.06386/) (2018)
- [Vital nodes identification in complex networks](https://mlsanity.com/paper/1607.01134/) (2016)
- [Saving Human Lives: What Complexity Science and Information Systems can Contribute](https://mlsanity.com/paper/1402.7011/) (2014)
- [Statistical physics of vaccination](https://mlsanity.com/paper/1608.09010/) (2016)
- [COVID-19 growth prediction using multivariate long short term memory](https://mlsanity.com/paper/2005.04809/) (2020)

## Built on by (most cited first, 5 of 5)

- [Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India](https://mlsanity.com/paper/2108.13823/) (2021)
- [Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases](https://mlsanity.com/paper/2305.13333/) (2023)
- [Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges](https://mlsanity.com/paper/2308.01319/) (2023)
- [A comprehensive review of deep learning in lung cancer](https://mlsanity.com/paper/2308.02528/) (2023)
- [Review of deep learning in healthcare](https://mlsanity.com/paper/2310.00727/) (2023)

## Abstract (arXiv)

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.