Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.

By Jacco Wallinga, P. Teunis, Mirjam E. Kretzschmar. American Journal of Epidemiology, 2006

692 citations recorded by the source corpus.

Subfields: Epidemiology, Infectious Diseases, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/82bfe557794e2ebf7c4dbc64ea7038dffc89900c

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Citations in the corpus

Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (2008, background): “Contact rates between individuals are often critical determinants of model outcomes [14].”
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees (2011, methodology): “Compared with other approaches [11,26,27], the data collection method used in this study makes it possible to gather information on actual face-to-face contacts, with high temporal and spatial resolution [28,30,36].”
Mathematical models of infectious disease transmission (2008, background): “Empirical data that describe the contact process could come from questionnaires, such as those used to investigate conversational contacts that are important for the spread of respiratory infection”
Mining social mixing patterns for infectious disease models based on a two-day population survey in Belgium (2009, neutral): “While [28] implemented these contact structures in a mathematical framework and compared it to the traditional WAIFW-matrices approach and the mass action principle, [29] focused on the statistical uncertainty in the estimation procedure and questioned the proportionality assumption on which [5] bas”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, methodology): “We adopt the contact matrix formalism and the age classes defined by Wallinga and collaborators [42].”
Networks and the Epidemiology of Infectious Disease (2010, methodology): “When the network is known and details of individual infections are available, contact tracing data may be used to infer the network; this data could also be used for inference on the epidemic parameters [116, 149].”
FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model (2010, result): “Simulated epidemics struck school-age children earlier than adults, which had been observed in earlier studies [6,34].”
High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School (2011, background): “This may help to refine the young age groups of the contact matrices proposed by Zagheni [6], Wallinga [7] and Mossong [8].”
Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model (2010, methodology): “[43] who measured the contact rates using a group of 1813 Dutch survey participants.”
Projecting social contact matrices in 152 countries using contact surveys and demographic data (2017, methodology): “Simulation studies using the POLYMOD data [23] and others [26, 45, 46 ] support this conclusion and explain why children and teenagers were the major channels for the initial transmission of infection during the 2009 influenza A(H1N1) pandemic [47] , and why school closure is one of the main non-pha”
The geography of COVID‐19 and the structure of local economies: The case of Italy (2020, background): “Social contact plays indeed a strong role in the transmission of diseases, as suggested by epidemiology studies, especially in the case of respiratory infectious agents (Wallinga et al., 2006; Meyer & Held, 2017).”