COVID-19 Outbreak Prediction with Machine Learning

Abstract

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 need 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.

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

“…Although ML methods were used in modeling former pandemics (e.g., Ebola, Cholera, swine fever, H1N1 influenza, dengue fever, Zika, oyster norovirus [11,[39][40][41][42][43][44][45][46][47][48]), there is a gap in the literature for peer-reviewed papers dedicated to COVID-19. Nevertheless, machine learning has been strongly proposed as a great potential for the fight against COVID-19 [49,50]. Machine learning delivered promising results in several aspects for mitigation and prevention and have been endorsed in the scientific community for, e.g., case identifications [51], classification of novel pathogens [52], modification of SIR-based models [53], diagnosis [54,55], survival prediction [56], and ICU demand prediction [57].…”

“…Although the research is in the very early stage, the trend in outbreak prediction with machine learning can be classified in two directions. Firstly, improvement of the SIR-based models, e.g., [53,58], and secondly time-series prediction [59,60]. Consequently, the state-of-the-art machine learning methods for outbreak modeling suggest two major research gaps for machine learning to address.…”

“…More sophisticated ML methods are yet to be explored. A recent paper by Ardabili et al, [50], explored the potential of MLP and ANFIS in time series prediction of COVID-19 in several countries. Contribution of the present paper is to improve the quality of prediction through proposing a hybrid machine learning and compare the results with ANFIS.…”

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

“…Although ML methods were used in modeling former pandemics (e.g., Ebola, Cholera, swine fever, H1N1 influenza, dengue fever, Zika, oyster norovirus [11,[39][40][41][42][43][44][45][46][47][48]), there is a gap in the literature for peer-reviewed papers dedicated to COVID-19. Nevertheless, machine learning has been strongly proposed as a great potential for the fight against COVID-19 [49,50]. Machine learning delivered promising results in several aspects for mitigation and prevention and have been endorsed in the scientific community for, e.g., case identifications [51], classification of novel pathogens [52], modification of SIR-based models [53], diagnosis [54,55], survival prediction [56], and ICU demand prediction [57].…”

Forecasting Covid-19 dynamics in Brazil: a data driven approach

“…Nonetheless, based on reports of these works that were more closely analyzed [5,7,6,12], we found that many of these methods rely on parameters that are dependent on the advance of the spreading and on the regional context.…”