Modelling governmental risk perception and coping appraisal during the COVID-19 pandemic in the Netherlands

Abstract

The novel coronavirus 2019 (SARS-CoV-2) is a highly infectious virus that started in December 2019 in Wuhan, China, and caused the disease COVID-19. It has since then rapidly spread across the globe. On 27 February 2020, the first person in the Netherlands was diagnosed with COVID-19, and on 23 March 2020, measures were taken by the Rutte III cabinet, which resulted in an ‘intelligent lockdown’, a lighter version of a full lock-down. Since vaccines were not yet available in 2020, the virus had to be controlled by measures taken by the government, such as social distancing, quarantine, isolation, or community containment.

Models can be very useful in evaluating the effectiveness of these measures and in predicting the expected results of different combinations of interventions. For this purpose, models are needed to simulate the spread of the disease, the interventions a government can take, and the country's response. The risk perceived by the government and subsequent interventions are two steps in the decision-making process of the Protection Motivation Theory (PMT). PMT divides the process into two steps: risk assessment (risk perception) and coping appraisal. Based on these two steps, different interventions scenarios can be evaluated.

For the simulation of the COVID-19 outbreak in the Netherlands, a pertussis model was converted into a COVID-19 model. In the model, three scenarios were created: the Roadmap scenario, the economic scenario and the age scenario. The Roadmap scenario is based on the “Routekaart” the Dutch government set up to identify when to implement measures. In the economic scenario, the order of measure implementation depends on the economic impact of the measures. And in the age scenario, measures are ordered based on their effect on the number of infections in one of nine age groups and implemented once the number of infected individuals in the different age groups reaches a certain risk level. Prior to the creation of the scenarios, the outbreak of COVID-19 in the Netherlands in 2020 is stimulated to test the base model by implementing predefined lockdowns. Eventually, the model is run without any measures, with predefined lockdowns, with the Roadmap scenario, with the economic scenario and with the age scenario. The model was calibrated using available COVID-19 data, travel data and a survey. The calibration was made challenging since many variables and risk factors are still unknown, undocumented or unspecified. According to the model, predefined lockdowns and the three scenarios have a similar effect on the number of infected individuals and hospitalizations. But, by looking at the periods the measures need to be implemented, the age and economic scenario are both more attractive ways to implement measures as the duration of the measures are shorter. In the model, the measures fluctuate a lot, which would mean that the government would need to change the measures every day. This does not seem to be realistic as the changes in measures would work counterproductively, as changing the measures often could cause confusion, dismay and unwillingness among the population. Furthermore, which scenario is better also depends on the priorities of the government in terms of the impact of the measures on the age groups within the Netherlands and economy. The model of this thesis contributes to a better understanding of risk perception and coping appraisal of the Dutch government during a pandemic. Even though the model did not possess all initially planned functionalities, a lot of information can be derived on the impact of different measures from the outcomes. Forasmuch as the virus is relatively new, just like the COVID-19 literature itself, research about risk perception and coping appraisal together with the spatial diffusion of the virus is limited. There is thus much potential in future research about risk perception and coping appraisal with a geographical component.