Bayesian time-varying autoregressive models of COVID-19 epidemics.

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.

A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas.

Since the start of the 21st century, the world has not confronted a more serious threat to global public health than the COVID-19 pandemic. While governments initially took radical actions in response to the pandemic to avoid catastrophic collapse of their health care systems, government policies have also had numerous knock-on socioeconomic, political, behavioral and economic effects. Researchers, thus, have a unique opportunity to forward our collective understanding of the modern world and to respond to the emergency situation in a way that optimizes resources and maximizes results. The PERISCOPE project, funded by the European Commission, brings together a large number of research institutions to collect data and carry out research to understand all the impacts of the pandemic, and create predictive models that can be used to optimize intervention strategies and better face possible future health emergencies. One of the main tangible outcomes of this project is the PERISCOPE Atlas: an interactive tool that allows to visualize and analyze COVID-19-related health, economic and sociopolitical data, featuring a WebGIS and several dashboards. This paper describes the first release of the Atlas, listing the data sources used, the main functionalities and the future development.

NetVIX - A network volatility index of financial markets

We construct a network volatility index (NetVIX) via market interconnectedness and volatilities to measure global market risk. The NetVIX multiplicatively decomposes into a network volatility effect and a network contagion effect. It also additively decomposes into volatility contributions of each market. We apply our measure to study the relationship between the interconnectedness among 20 major stock markets and global market risks over the last two decades. We show that the NetVIX has a strong relationship with the VIX index, and therefore able to reliably signal changes in global market volatility. We also show that while the NetVIX tracks to some extent the VIX, it provides much more information about the level of volatility and contagion effects in financial markets. The result shows that during crisis periods, particularly the tech bubble, the global financial crisis, and the Covid-19 pandemic, stock market interconnectedness contributes to global market turmoil by amplifying average market volatility with over 400 percent multiplier. Also during crisis times, the level of risk is relatively higher and more persistent in the US and German markets, which implies market losses for investors with long exposures. The results also reveal that the highest risk-contributing markets are the US, Brazil, Hong Kong, France, and Germany.

The impact of the SARS-CoV-2 pandemic on financial markets: a seismologic approach.

This work investigates financial volatility cascades generated by SARS-CoV-2 related news using concepts developed in the field of seismology. We analyze the impact of socio-economic and political announcements, as well as of financial stimulus disclosures, on the reference stock markets of the United States, United Kingdom, Spain, France, Germany and Italy. We quantify market efficiency in processing SARS-CoV-2 related news by means of the observed Omori power-law exponents and we relate these empirical regularities to investors' behavior through the lens of a stylized Agent-Based financial market model. The analysis reveals that financial markets may underreact to the announcements by taking a finite time to re-adjust prices, thus moving against the efficient market hypothesis. We observe that this empirical regularity can be related to the speculative behavior of market participants, whose willingness to switch toward better performing investment strategies, as well as their degree of reactivity to price trend or mispricing, can induce long-lasting volatility cascades.

Endemic-epidemic models to understand COVID-19 spatio-temporal evolution.

We propose an endemic-epidemic model: a negative binomial space-time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterized by similar non-pharmaceutical policy interventions.

Monitoring COVID-19 contagion growth.

We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective.

Monitoring Covid-19 Policy Interventions.

A very key point in the process of the Covid-19 contagion control is the introduction of effective policy measures, whose results have to be continuously monitored through accurate statistical analysis. To this aim we propose an innovative statistical tool, based on the Gini-Lorenz concentration approach, which can reveal how well a country is doing in reducing the growth of contagion, and its speed.

COVID-19 contagion and digital finance.

Digital finance is going to be heavily affected by the COVID-19 outbreak. We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19, so that its impact on finance can possibly be anticipated, and digitally monitored. The model is a Poisson autoregression of the daily new observed cases, and considers both short-term and long-term dependence in the infections counts. Model results are presented for the observed time series of China, the first affected country, but can be easily reproduced for all countries.

A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics

We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19, which can heavily impact health, economics and finance. The model is a Poisson autoregression of the daily new observed cases, and can reveal whether contagion has a trend, and where is each country on that trend. Model results are exemplified from some observed series.