Public Preferences for Introducing a COVID-19 Certificate: A Discrete Choice Experiment in the Netherlands.

OBJECTIVE: Here we investigate public preferences for coronavirus disease 2019 (COVID-19) certificates in the Netherlands, and whether these preferences differ between subgroups in the population. METHODS: A survey including a discrete choice experiment was administered to 1500 members of the adult population of the Netherlands. Each participant was asked to choose between hypothetical COVID-19 certificates that differed in seven attributes: the starting date, and whether the certificate allowed gathering with multiple people, shopping without appointment, visiting bars and restaurants, visiting cinemas and theatres, attending events, and practising indoor sports. Latent class models (LCMs) were used to determine the attribute relative importance and predicted acceptance rate of hypothetical certificates. RESULTS: Three classes of preference patterns were identified in the LCM. One class a priori opposed a certificate (only two attributes influencing preferences), another class was relatively neutral and included all attributes in their decision making, and the final class was positive towards a certificate. Respondents aged > 65 years and those who plan to get vaccinated were more likely to belong to the latter two classes. Being allowed to shop without appointment and to visit bars and restaurants was most important to all respondents, increasing predicted acceptance rate by 12 percentage points. CONCLUSIONS: Preferences for introduction of a COVID-19 certificate are mixed. A certificate that allows for shopping without appointment and visiting bars and restaurants is likely to increase acceptance. The support of younger citizens and those who plan to get vaccinated seems most sensitive to the specific freedoms granted by a COVID-19 certificate.

Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments

We propose three procedures based on association rules (AR) learning and random forests (RF) to support the specification of a portfolio choice model applied in data from complex choice experiment data, specifically a Participatory Value Evaluation (PVE) choice experiment. In a PVE choice experiment, respondents choose a combination of alternatives, subject to a resource constraint. We combine a methodological-iterative (MI) procedure with AR learning and RF models to support the specification of parameters of a portfolio choice model. Additionally, we use RF model predictions to contrast the validity of the behavioural assumptions of different specifications of the portfolio choice model. We use data of a PVE choice experiment conducted to elicit the preferences of Dutch citizens for lifting COVID-19 measures. Our results show model fit and interpretation improvements in the portfolio choice model, compared with conventional model specifications. Additionally, we provide guidelines on the use of outcomes from AR learning and RF models from a choice modelling perspective. © 2022 The Author(s)

COVID-19 and transport. A review of factors of relevance to the design of measures and their effects worldwide

The COVID-19 pandemic has led to the introduction of transport-related policy measures worldwide. In this paper, we review the literature on factors that are important for the design of those measures, and their effects on safety, physical and mental health, economy and environment. We conclude that factors underlying the introduction of transport related measures are related to the broader discussion on COVID-19 measures (e.g. on social distancing). This makes it impossible to determine the independent influence of determinants aimed at transport on the effects (virus spread, economy, well-being). Furthermore, the effects of measures appear to differ strongly between countries. Important determinants for these effects are (1) socio-economic factors, (2) cultural factors, (3) political factors and (4) individual factors. In addition, the extent to which people can work at home appears to be very important for the introduction and effectiveness of COVID-19 measures. In the category of 'cultural factors', the degree to which people have a 'sense of civic responsibility' and trust in the government and institutions plays a major role in the compliance with advice and coercive measures. Furthermore, experiences with previous viruses appear to have made a positive contribution to COVID-19 policies that are successful at containing the virus. Finally, individual factors play a role in the compliance with COVID-19 measures. For example, a pro-social attitude is associated with better compliance. And: if people rate the effectiveness of such measures higher, they are more likely to act on them. The paper also provides recommendations for policy and further research.