Balancing socio-economic and public health impact of COVID19 for its sustainable control and mitigation (SOPHIA)

Grant number: G0G9820N

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Key facts

  • Disease

    COVID-19
  • Known Financial Commitments (USD)

    $292,500
  • Funder

    FWO Belgium
  • Principal Investigator

    Ingmar Nopens, Christel Faes, Jan Baetens, Mattias Desmet, Ellen Van De Vijver, Marc Van Meirvenne, Thomas Nuyens
  • Research Location

    Belgium
  • Lead Research Institution

    Katholieke Universiteit Leuven, Ghent University, University of Hasselt
  • Research Priority Alignment

    N/A
  • Research Category

    Secondary impacts of disease, response & control measures

  • Research Subcategory

    Indirect health impacts

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

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.

Publicationslinked via Europe PMC

Comparison of Soft Indicator and Poisson Kriging for the Noise-Filtering and Downscaling of Areal Data: Application to Daily COVID-19 Incidence Rates.

A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data.