Online citations, reference lists, and bibliographies.
Referencing for people who value simplicity, privacy, and speed.
Get Citationsy
← Back to Search

Social Contacts And Mixing Patterns Relevant To The Spread Of Infectious Diseases

J. Mossong, N. Hens, M. Jit, P. Beutels, K. Auranen, R. Mikolajczyk, M. Massari, S. Salmaso, G. Tomba, J. Wallinga, Janneke C. M. Heijne, M. Sadkowska-Todys, M. Rosińska, W. Edmunds
Published 2008 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy Visualize in Litmaps
Reduce the time it takes to create your bibliography by a factor of 10 by using the world’s favourite reference manager
Time to take this seriously.
Get Citationsy
Background Mathematical modelling of infectious diseases transmitted by the respiratory or close-contact route (e.g., pandemic influenza) is increasingly being used to determine the impact of possible interventions. Although mixing patterns are known to be crucial determinants for model outcome, researchers often rely on a priori contact assumptions with little or no empirical basis. We conducted a population-based prospective survey of mixing patterns in eight European countries using a common paper-diary methodology. Methods and Findings 7,290 participants recorded characteristics of 97,904 contacts with different individuals during one day, including age, sex, location, duration, frequency, and occurrence of physical contact. We found that mixing patterns and contact characteristics were remarkably similar across different European countries. Contact patterns were highly assortative with age: schoolchildren and young adults in particular tended to mix with people of the same age. Contacts lasting at least one hour or occurring on a daily basis mostly involved physical contact, while short duration and infrequent contacts tended to be nonphysical. Contacts at home, school, or leisure were more likely to be physical than contacts at the workplace or while travelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible. Conclusions To our knowledge, our study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.
This paper references
Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures
J. Wallinga (2004)
Comprar Negative Binomial Regression | Joseph M. Hilbe | 9780521857727 | Cambridge University Press
J. Hilbe (2007)
Comparison of paper diary vs computer assisted telephone interview for collecting social contact data relevant to the spread of airborne infectious diseases
O Akakzia (2007)
Sexual mixing patterns and sex-differentials in teenage exposure to HIV infection in rural Zimbabwe
S. Gregson (2002)
Contact Diaries
Y. Fu (2007)
Entry screening for severe acute respiratory syndrome (SARS) or influenza: policy evaluation
R. Pitman (2005)
Estimating the impact of vaccination using age–time-dependent incidence rates of hepatitis B
N. Hens (2007)
Influenza A (Asian) 1957
J. Fry (1958)
Estimation of the basic reproduction number for infectious diseases from age‐stratified serological survey data
C. Farrington (2001)
Negative Binomial Regression
J. Hilbe (2007)
Real-time Estimates in Early Detection of SARS
S. Cauchemez (2006)
Mixing patterns and the spread of close-contact infectious diseases
W. Edmunds (2006)
Generalized Additive Models
S. Wood (2006)
Reducing the impact of the next influenza pandemic using household-based public health interventions.
J. Wu (2009)
Transmission Dynamics and Control of Severe Acute Respiratory Syndrome
M. Lipsitch (2003)
Modelling disease outbreaks
S Eubank (2004)
Who mixes with whom? A method to determine the contact patterns of adults that may lead to the spread of airborne infections
W. Edmunds (1997)
Mitigation strategies for pandemic influenza in the United States.
T. Germann (2006)
Containing Pandemic Influenza at the Source
I. Longini (2005)
Sexual Mixing Patterns of Patients Attending Sexually Transmitted Diseases Clinics
G. Garnett (1996)
Contact Surface Models for Infectious Diseases
C. Farrington (2005)
Analyses of the 1957 (Asian) influenza pandemic in the United Kingdom and the impact of school closures
E. Vynnycky (2007)
Recent developments in network measurement
PV Marsden (2005)
Social mixing patterns for transmission models of close contact infections: exploring self-evaluation and diary-based data collection through a web-based interface
P. Beutels (2006)
Evaluation of Interviewing Techniques to Enhance Recall of Sexual and Drug Injection Partners
D. Brewer (2001)
arules: Mining Assocation Rules and Frequent Itemsets
M Hahsler (2006)
Generalized Additive Models: An Introduction With R
A. Chiang (2007)
Mathematical Epidemiology of Infectious Diseases
O. Diekmann (1996)
A Comparative Analysis of Influenza Vaccination Programs
S. Bansal (2006)
Social contacts of school children and the transmission of respiratory-spread pathogens
R. Mikolajczyk (2007)
Models and Methods in Social Network Analysis: Recent Developments in Network Measurement
P. Marsden (2005)
Strategies for mitigating an influenza
NM Ferguson (2006)
New probabilistic interest measures for association rules
Michael Hahsler (2007)
A 'small-world-like' model for comparing interventions aimed at preventing and controlling influenza pandemics
F. Carrat (2006)
Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.
J. Wallinga (2006)
Collecting social contact data in the context of disease transmission: Prospective and retrospective study designs
R. Mikolajczyk (2008)
Modelling disease outbreaks in realistic urban social networks
S. Eubank (2004)
Transmission Dynamics of the Etiological Agent of SARS in Hong Kong: Impact of Public Health Interventions
S. Riley (2003)
Sexual behaviour in Britain: reported sexually transmitted infections and prevalent genital Chlamydia trachomatis infection
K. Fenton (2001)
Strategies for mitigating an influenza pandemic
N. Ferguson (2006)
Epidemiology. Modeling the SARS epidemic.
C. Dye (2003)
arules: Mining Assocation Rules and Frequent Itemsets. R package version 0.4–3
M Hahsler (2006)
Public health interventions and epidemic intensity during the 1918 influenza pandemic
Richard J Hatchett (2007)

This paper is referenced by
E. Klein (2015)
Assessment of the Prevalence of Pediculosis capitis among Primary School Girls in Riyadh, Saudi Arabia
Wafa A.I. AL-Me (2015)
The potential for “ spillover ” in outpatient antibiotic stewardship interventions 1 among US states 2 3 Short title : Geography of outpatient stewardship interventions 4 5
S. Olesen (2019)
Optimizing Vaccine Distribution for Different Age Groups of Population Using DE Algorithm
X. Hu (2013)
Health Versus Wealth: On the Distributional Effects of Controlling a Pandemic
A. Glover (2020)
Simulation of influenza propagation: Model development, parameter estimation, and mitigation strategies
S. Andradóttir (2014)
Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors
M. Kiti (2016)
Mathematical Models in Infectious Disease Epidemiology
P. White (2017)
Essential epidemiological mechanisms underpinning the transmission dynamics of seasonal influenza
J. Truscott (2011)
Selecting nonpharmaceutical interventions for influenza.
R. Jones (2013)
Can informal social distancing interventions minimize demand for antiviral treatment during a severe pandemic?
Amy L Greer (2013)
Cost-effectiveness of paediatric influenza vaccination in the Netherlands
P. D. de Boer (2020)
Dynamic modelling approaches for the analysis of the cost-effectiveness of seasonal influenza control
B. V. van Maanen (2017)
The importance of who infects whom: the evolution of diversity in host resistance to infectious disease.
M. Boots (2012)
Predicting and containing epidemic risk using friendship networks
Lorenzo Coviello (2016)
Assessing the efficiency of catch-up campaigns for introduction of pneumococcal conjugate vaccine; a modelling study based on data from Kilifi, Kenya
S. Flasche (2017)
Cost-Effectiveness of Rotavirus Vaccination in France-Accounting for Indirect Protection.
Dan Yamin (2016)
Integrating social contact and environmental data in evaluating tuberculosis transmission in a South African township.
J. Andrews (2014)
Implications of the COVID-19 lockdown on dengue transmission in Malaysia
S. Ong (2020)
Dynamic population flow based risk analysis of infectious disease propagation in a metropolis.
N. Zhang (2016)
Implications of the COVID-19 lockdown on dengue transmission and the occurrence of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in Malaysia
S. Ong (2020)
Detecting critical slowing down in high-dimensional epidemiological systems
Tobias S. Brett (2020)
Epidemic prediction and control in weighted networks.
K. Eames (2009)
Key node selection for containing infectious disease spread using particle swarm optimization
X. Fu (2009)
Pandemic modeling and the renormalization group equations: Effect of contact matrices, fixed points and nonspecific vaccine waning
M. McGuigan (2020)
A Flexible Agent-Based Framework for Infectious Disease Modeling
F. Miksch (2014)
Household Members Do Not Contact Each Other at Random: Implications for Infectious Disease Modelling
N. Goeyvaerts (2017)
Mixing in Meta-Population Models
Z. Feng (2019)
Simulation of the COVID-19 pandemic on the social network of Slovenia: effective strategies of the virus containment
Ž. Zaplotnik (2020)
Exit strategies: optimising feasible surveillance for detection, elimination and ongoing prevention of COVID-19 community transmission
K. Lokuge (2020)
Lower respiratory tract infections and gastrointestinal infections among mature babies in Japan
T. Kawai (2011)
Statistical methods for modeling of drug-related and close-contact infections
E. Fava (2012)
See more
Semantic Scholar Logo Some data provided by SemanticScholar