# COVID-19 growth prediction using multivariate long short term memory

arXiv: https://arxiv.org/abs/2005.04809 · HTML: https://mlsanity.com/paper/2005.04809/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2020.

## Built on by (most cited first, 3 of 3)

- [Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning](https://mlsanity.com/paper/2103.08178/) (2021)
- [Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction](https://mlsanity.com/paper/2207.06356/) (2022)
- [Prediction of COVID-19 by Its Variants using Multivariate Data-driven Deep Learning Models](https://mlsanity.com/paper/2301.10616/) (2023)

## Abstract (arXiv)

Coronavirus disease (COVID-19) spread forecasting is an important task to track the growth of the pandemic. Existing predictions are merely based on qualitative analyses and mathematical modeling. The use of available big data with machine learning is still limited in COVID-19 growth prediction even though the availability of data is abundance. To make use of big data in the prediction using deep learning, we use long short-term memory (LSTM) method to learn the correlation of COVID-19 growth over time. The structure of an LSTM layer is searched heuristically until the best validation score is achieved. First, we trained training data containing confirmed cases from around the globe. We achieved favorable performance compared with that of the recurrent neural network (RNN) method with a comparable low validation error. The evaluation is conducted based on graph visualization and root mean squared error (RMSE). We found that it is not easy to achieve the same quantity of confirmed cases over time. However, LSTM provide a similar pattern between the actual cases and prediction. In the future, our proposed prediction can be used for anticipating forthcoming pandemics. The code is provided here: https://github.com/cbasemaster/lstmcorona