# Neural network based country wise risk prediction of COVID-19

arXiv: https://arxiv.org/abs/2004.00959 · HTML: https://mlsanity.com/paper/2004.00959/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA](https://mlsanity.com/paper/2109.12094/) (2021)
- [Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases](https://mlsanity.com/paper/2105.03266/) (2021)
- [A Bayesian generative neural network framework for epidemic inference problems](https://mlsanity.com/paper/2111.03383/) (2021)
- [Convection-Diffusion Equation: A Theoretically Certified Framework for Neural Networks](https://mlsanity.com/paper/2403.15726/) (2024)
- [Fitness Dependent Optimizer with Neural Networks for COVID-19 patients](https://mlsanity.com/paper/2302.02986/) (2023)
- [Impact analysis of recovery cases due to COVID19 using LSTM deep learning model](https://mlsanity.com/paper/2209.02173/) (2022)

## Abstract (arXiv)

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier