Given the uncertainty about the further development of the COVID-19 pandemic, decision makers urgently need to balance the immediate public health impact of the virus and the - yet uninvestigated - psychological and socio-economic impacts of the mitigation measures that were imposed to safeguard our health care system. Just as the spread of COVID-19 itself, these effects are spatially heterogeneous and scale dependent, hence the need to study the intertwined psychological and socio-economic impacts at multiple spatial scales. To better understand the spatial heterogeneity of these effects, the inverse question is equally important: how does the socio-economic condition of a region affect both the virus spread and the impact of the measures? We will consider data on suicides, use of psychofarmaca, absenteeism due to psychological suffering, burnouts,... Since analysis of these data by the responsible governmental agencies lags at least one year, we will collect raw data and conduct (geostatistical) data analyses in relation to spatio-temporal variation in the measures to support decision-making on further control and mitigation strategies. We will use available socio-economic data at a high spatial resolution to infer relationships among the spacedependent parameters in the spatial COVID-19 model, the observed local spread of the virus and the psychological and socio-economic response on the measures. At the smallest spatial scales, this will require geostatistical methods.