Several studies examined what drives citizens’ support for COVID-19 measures, but no works have addressed how the effects of these drivers are distributed at the individual level. Yet, if significant differences in support are present but not accounted for, policymakers’ interpretations could lead to misleading decisions. In this study, we use XGBoost, a supervised machine learning model, combined with SHAP (Shapley Additive eXplanations) to identify the factors associated with differences in policy support for COVID-19 measures and how such differences are distributed across different citizens and measures. We use secondary data from a Participatory Value Evaluation (PVE) experiment, in which 1,888 Dutch citizens answered which COVID-19 measures should be imposed under four risk scenarios. We identified considerable heterogeneity in citizens’ support for different COVID-19 measures regarding different age groups, the weight given to citizens’ opinions and the perceived risk of getting sick of COVID-19. Data analysis methods employed in previous studies do not reveal such heterogeneity of policy support. Policymakers can use our results to tailor measures further to increase support for specific citizens/measures.

In this article, the affiliation details for author Jose Ignacio Hernandez were incorrectly given as ‘Center of Economics for Sustainable Development (CEDES), Faculty of Economics and Government, Universidad San Sebastian, Lientur 1457, Concepción, Chile ' but should have been ‘Center of Economics for Sustainable Development (CEDES), Faculty of Economics and Government, Universidad San Sebastian, Concepción, Chile. The affiliation ‘Transport and Logistics Group, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands’ for authors Jose Ignacio Hernandez and Niek Mouter was missing. The original article has been corrected.

Background: The stage of the pandemic significantly affects people’s preferences for (the societal impacts of) COVID-19 policies. No discrete choice experiments were conducted when the COVID-19 pandemic was in a transition phase. Objectives: This is the first study to empirically investigate how citizens weigh the key societal impacts of pandemic policies when the COVID-19 pandemic transitions into an endemic. Methods: We performed two discrete choice experiments among 2181 Dutch adults that included six attributes: COVID-19 deaths, physical health problems, mental health problems, financial problems, surgery delays and the degree to which individual liberties are restricted. We used latent class choice models to identify heterogeneous preferences for the impacts of COVID-19 measures across different groups of respondents. Results: A large majority of the participants in this study was willing to accept deaths to avoid that citizens experience physical complaints, mental health issues, financial problems and the postponement of surgeries. The willingness to tolerate COVID-19 deaths to avoid these societal impacts differed substantially between participants. When participants were provided with information about the stringency of COVID-19 measures, they assigned relatively less value to preventing the postponement of non-urgent surgeries for 1–3 months across all classes. Conclusions: Having gone through a pandemic, most Dutch citizens clearly prefer pandemic policies that consider citizens’ financial situations, physical problems, mental health problems and individual liberties, alongside the effects on excess mortality and pressure on healthcare.