ABSTRACT
Following the outbreak of the COVID-19 pandemic, the scientific community responded promptly by developing effective vaccines. Still, even though effective vaccines against COVID-19 became available, many people did not seem to be in a rush to become immunized. Community protection can be enhanced if more people decide to vaccinate, and thus it is necessary to identify relevant factors involved in vaccination behavior to find better ways of encouraging it. Vaccination behavior is the result of a decision process that might vary according to individual differences in information processing. We investigated the role of cognitive reflection ability and thinking styles in predicting self-reported vaccination behavior against COVID-19. A sample of 274 Romanian participants was surveyed for the present study, out of which 217 (Mage = 24.58, SD = 8.31; 53% female) declared they had the possibility to become vaccinated. Results showed that a higher level of cognitive reflection ability significantly increased the odds of becoming vaccinated. A rational thinking style was not linked to vaccination behavior. However, an experiential thinking style indirectly predicted vaccination behavior by means of attitudes towards vaccination. Since individual differences in information processing are, to a certain extent, linked to vaccination behavior, the design of vaccination campaigns could consider that people have specific information needs and address them as such.
ABSTRACT
Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.