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As the infectious population proliferates dramatically in the past months, the COVID-19 has become a severe global challenge. For the purpose of forecasting the inclination of the epidemic and evaluate the effectiveness of public interventions, we focus on constructing models consisting of differential equations and machine learning in this paper. Applying the SIR and SEIR epidemic models and the LSTM RNN model in Beijing, China, we demonstrate the comparison between models implemented public health interventions and without it. The similar accuracy of SEIR-2 (adopted interventions) and the LSTM RNN model to the true value present the necessity to implement public health interventions such as quarantines to postpone the crest point, obtaining a prolonged time to confront the epidemic. The utility of the model is compelling and worthy to apply in other cities. [ABSTRACT FROM AUTHOR]

This study examines how Twitter users located in Beijing and Delhi tweeted about climate change over a six-month period (July-December 2016) using a mixed-method design combining machine learning and textual analysis. Three dimensions of the Twitter discourse are studied: types of dominant users, types of topics discussed, and types of information sources used. The analysis reveals a strong correspondence among these three dimensions. Beijing's Twitter discourse is heavily influenced by international organizations, their officials, and environment finance groups and is heavily capitalist and West-centric. Delhi's Twitter discourse, which has a stronger presence of civil society groups and local activists, is more locally-oriented. Theoretical and policy implications are discussed. [ABSTRACT FROM AUTHOR]