Abstract: The objective of this study is to quantitatively estimate the probability of transmission of the SARS-CoV-2 virus inside the train carriage and to develop a simulation for the model based on the passenger demand for calculating the risk associated with origin-destination. The transmission rate of the virus in train carriage was analyzed using trip data from Pakenham Station to Flinders Street Station in Melbourne, Victoria, Australia, and the modified Wells-Riley model.

Abstract: This paper studies the potential drivers of governments’ approval rates in 18 Latin American countries using Internet search query data from Google Trends and traditional data sources. It employs monthly panel data between January 2006 and December 2015. The analysis tests several specifications including traditional explanatory variables of governments’ approval rates – i.e. inflation, unemployment rate, GDP growth, output gap – and subjective explanatory variables – e.g. perception of corruption and insecurity. For the latter, it uses Internet search query data to proxy citizens’ main social concerns, which are expected to drive governments’ approval rates. The results show that the perception of corruption and insecurity, and complaints about public services have a statistically significant association with governments’ approval rates. This paper also discusses the potential of Internet search query data as a tool for policy makers to understand better citizens’ perceptions, since it provides highly anonymous and high-frequency series in real-time.