A deep learning LSTM approach to predict COVD-19 deaths in North Africa

The countries of Tunisia, Algeria, and Morocco are part of the North Africa region, also called the Maghreb. As of October 16, 2022, there are 52413 COVID-19 related deaths reported for these countries despite the significant progress in vaccination.1 A notable interest by scholars has emerged recently to model and to forecast the spread and the lethality of the pandemic and its impact in the Middle East and North Africa region. In a recent study, spatial panel-data models were used to identify the factors for the spike of COVID-19 infections in North Africa.2 In another study, a statistical analysis was performed based on zero-inflation models and autoregressive conditional count models to forecast death counts with evidence from Tunisian data.3 Furthermore, quantitative analyses including statistical modeling and deep learning methods have also been performed to forecast the pandemic outbreak in different parts of the world.4 For instance, some authors presented long short-term memory (LSTM) based models to predict novel infections of the coronavirus in India, whereas in other studies deep learning methods were used to forecast new COVID-19 cases and death rates in Australia and Iran.5 Using COVID-19 datasets of several countries including Brazil, Germany, Italy, Spain, United Kingdom, China, India, Israel, Russia, and United States, alternative deep learning methods were studied and their results were compared in terms of forecast performance.6 In this study, we contribute to this ongoing literature, by conducting a statistical analysis with publicly available data on the coronavirus death counts for the Maghreb countries, and we show that the method of deep learning with LSTM network outperforms time series autoregressive integrated moving average (ARIMA) models in terms of forecast accuracy for the pandemic deaths.