Effect of risk status for severe COVID-19 on individual contact behaviour during the SARS-CoV-2 pandemic in 2020/2021—an analysis based on the German COVIMOD study

[1]  P. Klepac,et al.  Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study , 2022, PLoS medicine.

[2]  C. Faes,et al.  The influence of risk perceptions on close contact frequency during the SARS-CoV-2 pandemic , 2021, Scientific reports.

[3]  R. Mikolajczyk,et al.  Individual social contact data and population mobility data as early markers of SARS-CoV-2 transmission dynamics during the first wave in Germany—an analysis based on the COVIMOD study , 2021, BMC Medicine.

[4]  V. Jaeger,et al.  Einfluss von Impfungen und Kontaktreduktionen auf die dritte Welle der SARS-CoV-2-Pandemie und perspektivische Rückkehr zu prä-pandemischem Kontaktverhalten , 2021 .

[5]  O. Hamouda,et al.  Retrospektive Phaseneinteilung der COVID-19-Pandemie in Deutschland bis Februar 2021 , 2021 .

[6]  H. D. de Melker,et al.  Impact of physical distancing measures against COVID-19 on contacts and mixing patterns: repeated cross-sectional surveys, the Netherlands, 2016–17, April 2020 and June 2020 , 2021, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[7]  N. Hens,et al.  CoMix: comparing mixing patterns in the Belgian population during and after lockdown , 2020, Scientific Reports.

[8]  Frank Ball,et al.  A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2 , 2020, Science.

[9]  Eleanor M. Rees,et al.  The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements , 2020, BMC Medicine.

[10]  D. Feehan,et al.  Quantifying population contact patterns in the United States during the COVID-19 pandemic , 2020, Nature Communications.

[11]  Quentin J. Leclerc,et al.  Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK , 2020, BMC Medicine.

[12]  J. Wallinga,et al.  A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections , 2018, bioRxiv.

[13]  Mark Jit,et al.  Projecting social contact matrices in 152 countries using contact surveys and demographic data , 2017, PLoS Comput. Biol..

[14]  R. Mikolajczyk,et al.  Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel , 2017, BMC Medical Research Methodology.

[15]  Mark Jit,et al.  Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong , 2017, Scientific Reports.

[16]  R. Mikolajczyk,et al.  Comparison of response patterns in different survey designs: a longitudinal panel with mixed-mode and online-only design , 2017, Emerging Themes in Epidemiology.

[17]  Ariel Linden,et al.  Conducting Interrupted Time-series Analysis for Single- and Multiple-group Comparisons , 2015 .

[18]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[19]  R. Mikolajczyk,et al.  Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases , 2008, PLoS medicine.

[20]  M. Kretzschmar,et al.  Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. , 2006, American journal of epidemiology.

[21]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..