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.

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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.

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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.

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Background: The COVID-19 outbreak early 2020 was followed by an unprecedented package of measures. The relative calmness of the pandemic early 2022 provides a momentum to prepare for various scenarios. Objectives: As acceptance of COVID-19 measures is key for public support we investigated citizens’ preferences towards imposing measures in four scenarios: 1) spring/summer scenario with few hospitalizations; 2) autumn/winter scenario with many hospitalizations; 3) a new contagious variant, the impact on hospitalizations is unclear; 4) a new contagious variant, hospitalizations will substantially increase. Methods: Study 1 comprised a Participatory Value Evaluation (PVE) in which 2011 respondents advised their government on which measures to impose in the four scenarios. Respondents received information regarding the impact of each measure on the risk that the health system would be overloaded. To triangulate the results, 2958 respondents in Study 2 evaluated the acceptability of the measures in each scenario. Results: Measures were ranked similarly by respondents in Study 1 and 2: 1) the majority of respondents thought that hygiene measures should be upheld, even in the spring/summer; 2) the majority supported booster vaccination, working from home, encouraging self-testing, and mandatory face masks from scenario 2 onwards; 3) even in scenario 4, lockdown measures were not supported by the majority. Young respondents were willing to accept more risks for the health system than older respondents. Conclusion: The results suggest that policies that focus on prevention (through advising low-impact hygiene measures) and early response to moderate threats (by scaling up to moderately restrictive measures and boostering) can count on substantial support. There is low support for lockdown measures even under high-risk conditions, which further emphasizes the importance of prevention and a timely response to new threats. Our results imply that young citizens’ concerns, in particular, should be addressed when restrictive COVID-19 measures are to be implemented.@en