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Activity Number: 110 - Spatio-Temporal Modeling of the COVID-19 Pandemic: Statistics, Data, and the Stories They Tell
Type: Invited
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #317030
Title: Geographical Trends in the COVID-19 Epidemic in Belgium
Author(s): Christel Faes*
Companies: Hasselt University
Keywords: Distributed lag non-linear models; Prediction; Bayesian nowcasting; Covid-19

Belgium has been hit particularly hard by the coronavirus placing the country near the top in international rankings when looking at the number of confirmed cases per 100,000 and the number of deaths per million. Belgium accounted for more than half a million confirmed cases and over 17,000 SARS-CoV-2 confirmed and suspected deaths in 2020. Belgium’s location at the centre of Europe, high international mobility, high population density, high average household size and an older population structure combined with a relatively high mixing behaviour increases transmission potential. Short-term predictions were used to help local and national governments in decision-making on interventions during the outbreak and preserving the hospital capacity. Information on local mobility, absenteeism, testing strategy and GP consultations are used in the prediction model, using distributed lag non-linear models. Bayesian nowcasting methods are used to account for delay in reporting. Spatio-temporal trends are tracked to raise alarms when growth rate in hospitalizations and cases change.

Authors who are presenting talks have a * after their name.

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