Assessing the effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in Belgium by means of an extended SEIQRD model and public mobility data

We present a compartmental extended SEIQRD metapopulation model for SARS-CoV-2 spread in Belgium. We demonstrate the robustness of the calibration procedure by calibrating the model using incrementally larger datasets and dissect the model results by computing the effective reproduction number at home, in workplaces, in schools, and during leisure activities. We find that schools and home contacts are important transmission pathways for SARS-CoV-2 under lockdown measures. School reopening has the potential to increase the effective reproduction number from Re=0.66±0.04(95 % CI) to Re=1.09±0.05... Mehr ...

Verfasser: Tijs W. Alleman
Jenna Vergeynst
Lander De Visscher
Michiel Rollier
Elena Torfs
Ingmar Nopens
Jan M. Baetens
Dokumenttyp: Artikel
Erscheinungsdatum: 2021
Reihe/Periodikum: Epidemics, Vol 37, Iss , Pp 100505- (2021)
Verlag/Hrsg.: Elsevier
Schlagwörter: SARS-CoV-2 / Compartmental SEIQRD model / Non-pharmaceutical interventions / Google Community Mobility data / COVID-19 hospital length-of-stay / Social contact effectivity / Infectious and parasitic diseases / RC109-216
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-28971883
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.1016/j.epidem.2021.100505

We present a compartmental extended SEIQRD metapopulation model for SARS-CoV-2 spread in Belgium. We demonstrate the robustness of the calibration procedure by calibrating the model using incrementally larger datasets and dissect the model results by computing the effective reproduction number at home, in workplaces, in schools, and during leisure activities. We find that schools and home contacts are important transmission pathways for SARS-CoV-2 under lockdown measures. School reopening has the potential to increase the effective reproduction number from Re=0.66±0.04(95 % CI) to Re=1.09±0.05(95 % CI) under lockdown measures. The model accounts for the main characteristics of SARS-CoV-2 transmission and COVID-19 disease and features a detailed representation of hospitals with parameters derived from a dataset consisting of 22 136 hospitalized patients. Social contact during the pandemic is modeled by scaling pre-pandemic contact matrices with Google Community Mobility data and with effectivity-of-contact parameters inferred from hospitalization data. The calibrated social contact model with its publically available mobility data, although coarse-grained, is a cheap and readily available alternative to social–epidemiological contact studies under lockdown measures, which were not available at the start of the pandemic.