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 these models 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 ofmachine learning and soft computingmodels to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. 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 across nations, this study suggests machine Learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.publishedVersio

Exploring the Association between Mobility Fluctuations and Socioeconomic Indicators Using Data Mining Techniques in Indonesia and Malaysia

Human mobility has become a global issue during the Covid-19 pandemic and is believed to be a critical factor in the transmission of Covid -19. Thetimetable for the government'smovement controlhasstimulated the fluctuation ofnational mobility. However, the characteristics of variations between regions of the country are not yet understood. The purpose of this study was to characterise community mobility fluctuations in Indonesia and Malaysia and identify the association between socioeconomic indicators and mobility fluctuations in regions. This secondary and exploratory research investigated 34 Indonesian provinces and 14 Malaysian states. Data mining approaches using the CRISP-DM framework and the Knime Analytics platform were used. As a result, Indonesia and Malaysia show strength of mobility fluctuations in decreasing order: transit stations, workplaces, residential areas. Malaysia shows higher mobility fluctuations than Indonesia, which may indicate the community's response to the Covid-19 pandemic. As socioeconomic indicators, Human Development Index (HDI), poverty rate, and labor force participation are associated with the fluctuation of mobility. Therefore, regions with high fluctuation in mobility will likely have high HDI, high labour force participation rates, and low poverty rates. High-mobility areas include capitals and other cities with high density populations. This study provides evidence that socioeconomic indicators are determinants of mobility fluctuation during the pandemic. Regional governments may use the findings to anticipate community mobility and planning policies when similar pandemic conditions occur