COVID-19 vaccine intentions in Aotearoa New Zealand: Behaviour, risk perceptions, and collective versus individual motivations.

The global SARS-CoV-2 (COVID-19) pandemic presents a pressing health challenge for all countries, including Aotearoa New Zealand (NZ). As of early 2022, NZ public health measures have reduced impacts of the pandemic, but ongoing efforts to limit illness and fatalities will be significantly aided by widescale uptake of available vaccines including COVID-19 booster doses. Decades of research have established a broad range of demographic, social, cognitive, and behavioural factors which influence peoples' uptake of vaccinations, including a large amount of research in the last two years focused on COVID-19 vaccination in particular. In this study, we surveyed people in New Zealand (N = 660) in May and June of 2021, at which point the vaccine had been made available to high-risk groups. We explored individual versus collective motivations, finding that people who were hesitant about COVID-19 vaccination scored lower on independent self-construals (how people define themselves) but higher on community identity, weaker but still positive perceived social norms, lower general risk of COVID-19 to New Zealanders and higher vaccine risk for both themselves and others, and lower response-efficacy both for personal and collective benefits. Overall, the findings suggest some benefit of collective over individual appeals, but that generally messaging to encourage vaccination should focus on conveying social norms, risk from COVID-19 broadly, and vaccine safety and efficacy.

Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review.

Mechanisms for sharing information in a disaster situation have drastically changed due to new technological innovations throughout the world. The use of social media applications and collaborative technologies for information sharing have become increasingly popular. With these advancements, the amount of data collected increases daily in different modalities, such as text, audio, video, and images. However, to date, practical Disaster Response (DR) activities are mostly depended on textual information, such as situation reports and email content, and the benefit of other media is often not realised. Deep Learning (DL) algorithms have recently demonstrated promising results in extracting knowledge from multiple modalities of data, but the use of DL approaches for DR tasks has thus far mostly been pursued in an academic context. This paper conducts a systematic review of 83 articles to identify the successes, current and future challenges, and opportunities in using DL for DR tasks. Our analysis is centred around the components of learning, a set of aspects that govern the application of Machine learning (ML) for a given problem domain. A flowchart and guidance for future research are developed as an outcome of the analysis to ensure the benefits of DL for DR activities are utilized.

Perceptions of risk characteristics of earthquakes compared to other hazards and their impact on risk tolerance.

People tolerate different levels of risk owing to a variety of hazards. Previous research shows that the psychometric properties of hazards predict people's tolerance of them. However, this work has not taken into account events such as earthquakes. The present study tested how earthquakes score vis-à-vis risk properties and risk tolerance as compared to five other familiar hazards. Participants from Wellington, New Zealand (N=139) rated these six hazards using measures of risk characteristics and risk tolerance. Participants demonstrated different levels of risk tolerance for the different hazards and viewed earthquakes as having similar risk features to nuclear power. They also preferred different risk mitigation strategies for earthquakes (more government funding) to the other five hazards (stronger legislation). In addition, earthquake risk tolerance was predicted by different risk characteristics than the other five hazards. These findings will help risk communicators in identifying which risk characteristics to target to influence citizens' risk tolerance.

Behavioral Response in the Immediate Aftermath of Shaking: Earthquakes in Christchurch and Wellington, New Zealand, and Hitachi, Japan.

This study examines people's response actions in the first 30 min after shaking stopped following earthquakes in Christchurch and Wellington, New Zealand, and Hitachi, Japan. Data collected from 257 respondents in Christchurch, 332 respondents in Hitachi, and 204 respondents in Wellington revealed notable similarities in some response actions immediately after the shaking stopped. In all four events, people were most likely to contact family members and seek additional information about the situation. However, there were notable differences among events in the frequency of resuming previous activities. Actions taken in the first 30 min were weakly related to: demographic variables, earthquake experience, contextual variables, and actions taken during the shaking, but were significantly related to perceived shaking intensity, risk perception and affective responses to the shaking, and damage/infrastructure disruption. These results have important implications for future research and practice because they identify promising avenues for emergency managers to communicate seismic risks and appropriate responses to risk area populations.