Human mobility increased with vaccine coverage and attenuated the protection of COVID-19 vaccination: A longitudinal study of 107 countries.

Background: The World Health Organization has raised concerns that vaccinated people may reduce physical and social distancing more than necessary. With imperfect vaccine protection and the lifting of mobility restrictions, understanding how human mobility responded to vaccination and its potential consequence is critical. We estimated vaccination-induced mobility (VM) and examined whether it attenuates the effect of COVID-19 vaccination on controlling case growth. Methods: We collected a longitudinal data set of 107 countries between 15 February 2020 and 6 February 2022 from Google COVID-19 Community Mobility Reports, the Oxford COVID-19 Government Response Tracker, Our World in Data, and World Development Indicators. We measured mobility in four categories of location: retail and recreational places, transit stations, grocery stores and pharmacies, and workplaces. We applied panel data models to address unobserved country characteristics and used Gelbach decomposition to evaluate the extent to which VM has offset vaccination effectiveness. Results: Across locations, a 10-percentage-point (pp) increase in vaccine coverage was associated with a 1.4-4.3 pp increase in mobility (P < 0.001). VM was greater in lower-income countries (up to 7.9 pps; 95% confidence interval (CI) = 5.3 to 10.5, P < 0.001) and in earlier stages of vaccine rollouts (up to 19.2 pps; 95% CI = 15.1 to 23.2%, P < 0.001). VM decreased the effectiveness of vaccines in controlling case growth by 33.4% in retail and recreation places (P < 0.001), 26.4% in transit stations (P < 0.001), and 15.4% in grocery stores and pharmacies (P = 0.002). Conclusions: VM provides support for the Peltzman effect; it attenuates but does not completely counter vaccine effectiveness. Our study findings suggest strategies for mitigating the unintended consequences of VM, including reducing short-term mobility responses after vaccination, prioritizing mobility in grocery-type places and workplaces, and accelerating rollouts at earlier stages of vaccination, especially in lower-income countries.

The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model.

Background: Messages on one's stance toward vaccination on microblogging sites may affect the reader's decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited. Objective: This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message. Methods: English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user. Results: Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001). Conclusions: The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics.

The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model

Background Messages on one’s stance toward vaccination on microblogging sites may affect the reader’s decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial;however, studies on this topic have remained limited. Objective This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message. Methods English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user. Results Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively;emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03;IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001). Conclusions The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics.