Considering network interventions

Model based on COVID-19 evidence to predict and improve pandemic control

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.

Social network interventions in the space of topological relationships between communities

A social network intervention is a process of intentionally altering a social network to achieve an objective. The objective in question may concern accelerating behaviour change or improving organisational performance. In this work we propose a novel model of social network interventions which considers topological properties of relationships existing between communities. Broadly speaking, topological properties of such relationships include properties described by natural language descriptions such as contains, partial overlap and disjoint. The proposed model provides an abstraction which in many cases is useful for solving problems involving social network interventions. We demonstrate this by simulating interventions on a number of hypothetical and real social networks in the domains of health and security.

A comprehensive examination of association between belief in vaccine misinformation and vaccination intention in the COVID-19 context

Misinformation about COVID-19 vaccines is widely available in the public communication environment. Exposure to the misinformation may increase perceived risk of and evoke negative emotions toward COVID-19 vaccines that may eventually reduce COVID-19 vaccination intentions. The negative influences of misinformation may vary by aspects of individuals' social networks. Expanding the reasoned action approach, we proposed a comprehensive model to examine the roles of misinformation beliefs, perceived risk, fear, worry, and social networks in explaining COVID-19 vaccination intentions. We tested the model using survey data of South Korean adults, collected when the Korean government launched its nationwide vaccination program in April 2021 (n = 744). The results from our step-by-step path analyses indicated that COVID-19 vaccination intentions had positive direct associations with vaccination-specific factors such as attitudes toward, injunctive norms on, and perceived behavioral control over COVID-19 vaccination. Perceived risk was also directly linked to intentions. Among these factors, attitudes and injunctive norms were most strongly related to intentions. Misinformation beliefs and worry had negative indirect relationships with intentions via the mediation of these variables directly connected to intentions. The negative influences of misinformation beliefs were greater among respondents reported stronger tie strengths. Theoretical and practical implications were discussed.

Are Stringent Containment and Closure Policies Associated with a Lower COVID-19 Spread Rate? Global Evidence

Stringent government policies, in general, and strict containment and closure policies in particular including workplace closing, restrictions on gatherings, close of public transport, stay-at-home order, restrictions on internal movement, and international travel control are associated with a lower spread rate of COVID-19 cases. On the other hand, school closures and public event cancellations have not been found to be associated with lower COVID-19 spread. Restrictions on international travel and the closing of public transport are two policies that stand out and have a consistent and slowing effect on the spread of COVID-19. The slowing effect of the containment and closure policies on the spread of COVID-19 becomes stronger one week after the policies have been implemented, consistent with the SARS-CoV-2 transmission pattern and the incubation period evolution. Furthermore, the slowing effect becomes stronger for culturally tight countries and countries with a higher population density. Our findings have important policy implications, implying that governments need to carefully implement containment and closure policies in their own countries’ social and cultural contexts, with an emphasis on the ideas of the common interest, personal responsibility, and the sense of community.