COVID 19 Outbreak Prediction with Machine Learning
Several outbreak prediction models for COVID 19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID 19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID 19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e. multi layered perceptron, MLP, and adaptive network based fuzzy inference system, ANFIS) Based on the results reported here, and due to the highly complex nature of the COVID 19 outbreak and variation in its behavior from nation to nation, this study suggests machine learning as an effective tool to model the outbreak.
medRxiv