Governmental anti‐pandemic policies, vaccination, population mobility, Twitter narratives, and the spread of COVID‐19: Evidence from the European Union countries

Abstract

We provide large‐scale empirical evidence on the effects of multiple governmental regulatory and health policies, vaccination, population mobility, and COVID‐19‐related Twitter narratives on the spread of a new coronavirus infection. Using multiple‐level fixed effects panel data model with weekly data for 27 European Union countries in the period of March 2020–June 2021, we show that governmental response policies were effective both in reducing the number of COVID‐19 infection cases and deaths from it, particularly, in the countries with higher level of rule of law. Vaccination expectedly helped to decrease the number of virus cases. Reductions in population mobility in public places and workplaces were also powerful in fighting the pandemic. Next, we identify four core pandemic‐related Twitter narratives: governmental response policies, people's sad feelings during the pandemic, vaccination, and pandemic‐related international politics. We find that sad feelings’ narrative helped to combat the virus spread in EU countries. Our findings also reveal that while in countries with high rule of law international politics’ narrative helped to reduce the virus spread, in countries with low rule of law the effect was strictly the opposite. The latter finding suggests that trust in politicians played an important role in confronting the pandemic.

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“…This finding further suggests that countries characterized by vibrant economies, social and cultural activities, dense populations and traditional practices should implement strict action restrictions early on to mitigate the potential occurrence of a major infectious disease outbreak [35]. Governmental anti-pandemic policies, such as population mobility restrictions, effectively reduced the number of COVID-19 infection cases and deaths from it [36]. Therefore, this study categorizes the measures that can potentially reduce virus spread and associated demand fluctuation into two groups: "Self-protection measures" and "Restrictive protection".…”

A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China

“…The movement of individuals is inevitable as they live and work in cities and economic development progresses [16,33]. This inter-city population movement drives dynamic population changes [83,84].…”

“…Effective control of COVID-19 spread is of paramount importance. Measures such as vaccination, herd immunization, and lockdowns have been utilized to curb the pandemic [15][16][17]; however, the disparate availability and affordability of vaccines across various populations have significantly influenced the outcomes. Allowing populations to develop natural herd immunity has incited controversy due to the potential to exacerbate already strained healthcare systems, which could potentially increase fatalities [11].…”