Pronóstico del COVID-19 en Colombia utilizando Redes Neuronales Recurrentes con celdas de gran memoria de corto plazo y unidades recurrentes cerradas
Forecasting of COVID-19 in Colombia using recurrent neural networks with long short term memory and gated recurrent units
Abstract (en)
On march 6 of 2020, the first case of COVID-19 was reported in Colombia. This virus, declared a public health emergency of international importance, has affected different sectors. There is a boom in the number of studies that make forecasts in various aspects that have to do with this virus. The present work shows the theoretical aspects of recurrent neuronal networks and his use to create a 60-day forecast on cumulative cases, cumulative deaths and cumulative recovered, available from march 6 2020 to march 6 2022. Neural networks with GRU and LSTM cells along with the classic RNN were used to make these forecasts.
Abstract (es)
El 6 de marzo del 2020, el primer caso de COVID-19 fue reportado en Colombia, este virus, declarado como una emergencia de salud pública de importancia internacional ha afectado diferentes sectores. Existe un auge en cuanto al número de estudios que buscan hacer pronósticos en diversos aspectos que tienen que ver con este virus. El presente trabajo muestra los aspectos teóricos de las redes neuronales recurrentes y se utilizan para crear una predicción de 60 días sobre los casos acumulados, fallecidos acumulados y recuperados acumulados disponibles desde el 6 de marzo del 2020 hasta el 6 de marzo del 2022. Redes neuronales con celdas GRU y LSTM junto con las clásicas RNN fueron utilizadas para hacer estos pronósticos.
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