Exploring the role of non-pharmaceutical interventions (NPIs) in flattening the Greek COVID-19 epidemic curve.

Due to the COVID-19 pandemic originating in China in December 2019, apart from the grave concerns on the exponentially increasing casualties, the affected countries are called to deal with severe repercussions in all aspects of everyday life, from economic recession to national and international movement restrictions. Several regions managed to handle the pandemic more successfully than others in terms of life loss, while ongoing heated debates as to the right course of action for battling COVID-19 have divided the academic community as well as public opinion. To this direction, in this paper, an autoregressive COVID-19 prediction model with heterogeneous explanatory variables for Greece is proposed, taking past COVID-19 data, non-pharmaceutical interventions (NPIs), and Google query data as independent variables, from the day of the first confirmed case-February 26th-to the day before the announcement for the quarantine measures' softening-April 24th. The analysis indicates that the early measures taken by the Greek officials positively affected the flattening of the epidemic curve, with Greece having recorded significantly decreased COVID-19 casualties per million population and managing to stay on the low side of the deaths over cases spectrum. In specific, the prediction model identifies the 7-day lag that is needed in order for the measures' results to actually show, i.e., the optimal time-intervention framework for managing the disease's spread, while our analysis also indicates an appropriate point during the disease spread where restrictive measures should be applied. Present results have significant implications for effective policy making and in the designing of the NPIs, as the second wave of COVID-19 is expected in fall 2020, and such multidisciplinary analyses are crucial in order to understand the evolution of the Daily Deaths to Daily Cases ratio along with its determinants as soon as possible, for the assessment of the respective domestic health authorities' policy interventions as well as for the timely health resources allocation.

COVID-19 predictability in the United States using Google Trends time series.

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

Predictability analysis of the Pound's Brexit exchange rates based on Google Trends data.

During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users' online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of exploring the Sterling Pound's predictability, we employ Google Trends data from the last 5 years (March 1st, 2015 to February 29th, 2020) and perform predictability analysis on the Pound's exchange rates to Euro and Dollar. The period selected includes the 2016 UK referendum as well as the actual Brexit day (January 31st, 2020), with the analysis aiming at analyzing the Pound's relationships with Google query data on Pound-related keywords and topics. A quantile dependence method is employed, i.e., cross-quantilograms, to test for directional predictability from Google Trends data to the Pound's exchange rates for lags from zero to 30 (in weeks). The results indicate that statistically significant quantile dependencies exist between Google query data and the Pound's exchange rates, which point to the direction of one of the main implications in this field, that is to examine whether the movements in one economic variable can cause reactions in other economic variables.

Jumps beyond the realms of cricket: India's performance in One Day Internationals and stock market movements.

We examine the impact of the Indian cricket team's performance in one-day international cricket matches on return, realized volatility and jumps of the Indian stock market, based on intraday data covering the period of 30th October, 2006 to 31st March, 2017. Using a nonparametric causality-in-quantiles test, we were able to detect evidence of predictability from wins or losses for primarily volatility and jumps, especially over the lower-quantiles of the conditional distributions, with losses having stronger predictability than wins. However, the impact on the stock return is weak and restricted towards the upper end of the conditional distribution.