Weather impact on daily cases of COVID-19 in Saudi Arabia using machine learning

TL;DR

This study investigates how weather factors like temperature and wind influence COVID-19 daily case numbers in Saudi Arabia using machine learning models, with a focus on data collection, preprocessing, and prediction accuracy.

Contribution

The paper's main contribution is the collection and preprocessing of weather and COVID-19 data, and the development of machine learning models to predict daily cases.

Findings

Temperature and wind are strongly associated with COVID-19 spread.

The random forest model achieved an R² of 82.3%.

Model evaluation showed low error metrics, indicating good prediction performance.

Abstract

COVID-19 was announced by the World Health Organisation (WHO) as a global pandemic. The severity of the disease spread is determined by various factors such as the countries' health care capacity and the enforced lockdown. However, it is not clear if a country's climate acts as a contributing factor towards the number of infected cases. This paper aims to examine the relationship between COVID-19 and the weather of 89 cities in Saudi Arabia using machine learning techniques. We compiled and preprocessed data using the official daily report of the Ministry of Health of Saudi Arabia for COVID-19 cases and obtained historical weather data aligned with the reported case daily reports. We preprocessed and prepared the data to be used in models' training and evaluation. Our results show that temperature and wind have the strongest association with the spread of the pandemic. Our main contribution is data collection, preprocessing, and prediction of daily cases. For all tested models, we used cross-validation of K-fold of K=5. Our best model is the random forest that has a Mean Square Error(MSE), Root Mean Square (RMSE), Mean Absolute Error (MAE), and R{2} of 97.30, 9.86, 1.85, and 82.3\%, respectively.