Abstract
The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the epidemic, one problem is to determine which factors play an important role in a patient’s progression of the disease. Here, we construct an enhanced COVID-19 structured dataset from more than one source, using natural language processing to add local weather conditions and country-specific research sentiment. The enhanced structured dataset contains 301,363 samples and 43 features, and we applied both machine learning algorithms and deep learning algorithms on it so as to forecast patient’s survival probability. In addition, we import alignment sequence data to improve the performance of the model. Application of Extreme Gradient Boosting (XGBoost) on the enhanced structured dataset achieves 97% accuracy in predicting patient’s survival; with climatic factors, and then age, showing the most importance. Similarly, the application of a Multi-Layer Perceptron (MLP) achieves 98% accuracy. This work suggests that enhancing the available data, mostly basic information on patients, so as to include additional, potentially important features, such as weather conditions, is useful. The explored models suggest that textual weather descriptions can improve outcome forecast.
Highlights
The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARSCoV-2 virus, is affecting many aspects of society, in particular human health, and social issues [2,3], mental health, and the economy [4]
The enhanced structured dataset has significantly higher area under the receiver operating characteristic (ROC) curve (AUC) scores than the model built on the initial structured dataset
For the model based on the enhanced structured dataset, the weather description, temperature, and humidity are more important than age; the level of importance of weather is higher than that of age
Summary
The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARSCoV-2 virus, is affecting many aspects of society, in particular human health (at the time of writing, over 66 million diagnosed cases and 1.5 million deaths [1]), and social issues [2,3], mental health, and the economy [4]. Shahid et al [10] use several models, including ARIMA, SVR, LSTM, and Bi-LSTM, for time series prediction of confirmed cases, deaths, and recoveries in ten major countries affected by COVID-19. Shreshth et al [11] present a machine learning model to predict how the number of cases of COVID-19 will develop, and to forecast when a specific country can expect to see an end of the pandemic, using the FogBus framework. Other researchers have built machine learning models for the classification and diagnosis of COVID-19 that are based on medical images [12,13]. Most machine learning and deep learning research [15,16] on COVID-19 build a classification model on various types of data to investigate which might be the important features to predict a specific outcome. The presence or absence of features will impact the accuracy of a model
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