Analyzing the emotional impact of COVID-19 with Twitter data: Lessons from a B-VAR analysis on Italy.

The novel coronavirus 2019 revolutionized the way of living and the communication of people making social media a popular tool to express concerns and perceptions. Starting from this context we built an original database based on the Twitter users' emotions shown in the early weeks of the pandemic in Italy. Specifically, using a single index we measured the feelings of four groups of stakeholders (journalists, people, doctors, and politicians), in three groups of Italian regions (0,1,2), grouped according to the impact of the COVID-19 crises as defined by the Conte Government Ministerial Decree (8th March 2020). We then applied B-VAR techniques to analyze the sentiment relationships between the groups of stakeholders in every Region Groups. Results show a high influence of doctors at the beginning of the epidemic in the Group that includes most of Italian regions (Group 0), and in Lombardy that has been the region of Italy hit the most by the pandemic (Group 2). Our outcomes suggest that, given the role played by stakeholders and the COVID-19 magnitude, health policy interventions based on communication strategies may be used as best practices to develop regional mitigation plans for the containment and contrast of epidemiological emergencies.

Early warnings of COVID-19 outbreaks across Europe from social media.

We analyze data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2019-2020, before the first public announcements of local sources of infection were made. We show evidence that unexpected levels of concerns about cases of pneumonia were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected.

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic.

This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.

A social network analysis of the organizations focusing on tuberculosis, malaria and pneumonia.

In this paper,we present an original study on the use of social media data to analyze the structure of the global health networks (GHNs) relative to health organizations targeted to malaria, tuberculosis (TBC) and pneumonia as well as twitter popularity, evaluating the performance of their strategies in response to the arising health threats. We use a machine learning ensemble classifier and social network analysis to discover the Twitter users that represent organizations or groups active for each disease. We have found evidence that the GHN of TBC is the more mature, active and global. Meanwhile, the networks of malaria and pneumonia are found to be less connected and lacking global coverage. Our analysis validates the use of social media to analyze GHNs and to propose these networks as an important organizational tool in mobilizing the community versus global sustainable development goals.

The effects of ageing population on health expenditure and economic growth in China: A Bayesian-VAR approach.

The increasing proportion of the population aged 65 or over has generated a global rise of health spending due to higher demand for medical and long-term care services, which has become a growing challenge to the sustainability of public finances across countries. This phenomenon is especially prominent in China, which has experienced accelerated rates of both economic growth and population ageing over the past four decades. Using Bayesian-VAR (B-VAR) models we compute the impulse response functions (IRFs) and the forecast error variance decomposition functions (FEVDs) to empirically examine the dynamic relationships between ageing index, life expectancy, economic growth and health expenditure in China. We compare China with the USA which has distinct trajectories of population structure and economic development to better understand the former's dynamic patterns. We find a pronounced response for both the USA and China of ageing index to life expectancy and of health spending per capita to GDP per capita, while ageing population induces a relatively strong reaction from health expenditure per capita in China. Our results are robust with either nominal or real variables. These findings suggest that, in China, a well-rounded policy accommodating economic, social and health factors is needed to improve the quality of life of the ageing population for a sustainable development of the economy.

The effects of population ageing on health care expenditure: A Bayesian VAR analysis using data from Italy.

Currently, the dynamics of the population have raised concerns about the future sustainability of Italy's national health system. The increasing proportion of people over the age of 65 could lead to a higher incidence of chronic-degenerative diseases and a greater demand for health and social care with a consequent impact on health spending. Although in recent years the quantity and quality of works on the relationship between ageing and health expenditure has increased substantially these works do not always obtain similar results. Starting from this point, we use a B-VAR model and Eurostat data to investigate over the period 1990-2013 the impact of demographic changes on health expenditure in Italy. We estimate these models using impulse-response analysis and variance decomposition. The results show that health expenditure in Italy reacts more to the ageing population compared with life expectancy and per capita GDP. In response to these findings, we conclude that the impact of the increase in the elderly population with disabilities will fall on the long-term care sector. Effective health interventions, such as health-promotion and disease-prevention programs that target the main causes of morbidity, could help to minimize the cost pressures associated with ageing by ensuring that the population stays healthy in old age. We consider the implications of this work for health care policy suggestions and for future research.