Richard A J Post1, Marta Regis1, Zhuozhao Zhan1, Edwin R van den Heuvel2,3. 1. Department of Mathematics and Computer science, Eindhoven University of Technology, P.O. Box 513, 5600, MB, Eindhoven, The Netherlands. 2. Department of Mathematics and Computer science, Eindhoven University of Technology, P.O. Box 513, 5600, MB, Eindhoven, The Netherlands. e.r.v.d.heuvel@tue.nl. 3. Department of Preventive Medicine and Epidemiology, School of Medicine, Boston University, Boston, MA, 02118, USA. e.r.v.d.heuvel@tue.nl.
Abstract
BACKGROUND: To reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures. METHODS: We developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures. RESULTS: The change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45). CONCLUSIONS: There seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban.
BACKGROUND: To reduce the transmission of thesevere acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. Weexamined how theeffective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures. METHODS: We developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated theECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures. RESULTS: The change points in the daily ECRs were found to align with the implementation of governmental interventions. At theend of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while theECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45). CONCLUSIONS: There seemed to be an immediateeffect of banning events and closing schools, typically among the first measures taken by the governments. Theeffect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and theECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban.
Authors: Marc Lipsitch; Ted Cohen; Ben Cooper; James M Robins; Stefan Ma; Lyn James; Gowri Gopalakrishna; Suok Kai Chew; Chorh Chuan Tan; Matthew H Samore; David Fisman; Megan Murray Journal: Science Date: 2003-05-23 Impact factor: 47.728
Authors: Seth Flaxman; Swapnil Mishra; Axel Gandy; H Juliette T Unwin; Thomas A Mellan; Helen Coupland; Charles Whittaker; Harrison Zhu; Tresnia Berah; Jeffrey W Eaton; Mélodie Monod; Azra C Ghani; Christl A Donnelly; Steven Riley; Michaela A C Vollmer; Neil M Ferguson; Lucy C Okell; Samir Bhatt Journal: Nature Date: 2020-06-08 Impact factor: 49.962
Authors: Benjamin J Cowling; Sheikh Taslim Ali; Tiffany W Y Ng; Tim K Tsang; Julian C M Li; Min Whui Fong; Qiuyan Liao; Mike Yw Kwan; So Lun Lee; Susan S Chiu; Joseph T Wu; Peng Wu; Gabriel M Leung Journal: Lancet Public Health Date: 2020-04-17
Authors: Martijn H H Schoot Uiterkamp; Martijn Gösgens; Hans Heesterbeek; Remco van der Hofstad; Nelly Litvak Journal: J R Soc Interface Date: 2022-08-31 Impact factor: 4.293