COVID-19 spread forecast using recurrent auto-encoders

Abstract

Since December 2019, the COVID-19 disease has become one of the most concerning issues across the Globe. The world has experienced serious prevention measures, from lockdown of cities to entire countries, as it is still unknown when will this pandemic end. Thus, it is no surprise that predicting the spread of this novel coronavirus has quickly become a hot topic among Artificial Intelligence researchers. In this paper we try to solve this task, by leveraging the capabilities that Recurrent Auto-Encoders have on time-series and implying a semi-supervised training process. Furthermore, the concept of nearest neighbour countries is introduced to estimate the cumulative number of confirmed cases for any country. The results are promising, showing that our proposed method is capable of making reliable predictions for a 30-days period. It is worth mentioning that, while this study uses information related to COVID-19, the proposed method can be used to estimate the evolution of any kind of disease, provided that the associated data comes in form of time-series.