Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.

Knowledge of social contact patterns still represents the most critical step for understanding the spread of directly transmitted infections. Data on social contact patterns are, however, expensive to obtain. A major issue is then whether the simulation of synthetic societies might be helpful to rel...

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Autores principales:	Fabrizio Iozzi, Francesco Trusiano, Matteo Chinazzi, Francesco C Billari, Emilio Zagheni, Stefano Merler, Marco Ajelli, Emanuele Del Fava, Piero Manfredi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e06531d9491d4f0791e3d3626a603cc4
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spelling	oai:doaj.org-article:e06531d9491d4f0791e3d3626a603cc42021-11-18T05:50:50ZLittle Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.1553-734X1553-735810.1371/journal.pcbi.1001021https://doaj.org/article/e06531d9491d4f0791e3d3626a603cc42010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21152004/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Knowledge of social contact patterns still represents the most critical step for understanding the spread of directly transmitted infections. Data on social contact patterns are, however, expensive to obtain. A major issue is then whether the simulation of synthetic societies might be helpful to reliably reconstruct such data. In this paper, we compute a variety of synthetic age-specific contact matrices through simulation of a simple individual-based model (IBM). The model is informed by Italian Time Use data and routine socio-demographic data (e.g., school and workplace attendance, household structure, etc.). The model is named "Little Italy" because each artificial agent is a clone of a real person. In other words, each agent's daily diary is the one observed in a corresponding real individual sampled in the Italian Time Use Survey. We also generated contact matrices from the socio-demographic model underlying the Italian IBM for pandemic prediction. These synthetic matrices are then validated against recently collected Italian serological data for Varicella (VZV) and ParvoVirus (B19). Their performance in fitting sero-profiles are compared with other matrices available for Italy, such as the Polymod matrix. Synthetic matrices show the same qualitative features of the ones estimated from sample surveys: for example, strong assortativeness and the presence of super- and sub-diagonal stripes related to contacts between parents and children. Once validated against serological data, Little Italy matrices fit worse than the Polymod one for VZV, but better than concurrent matrices for B19. This is the first occasion where synthetic contact matrices are systematically compared with real ones, and validated against epidemiological data. The results suggest that simple, carefully designed, synthetic matrices can provide a fruitful complementary approach to questionnaire-based matrices. The paper also supports the idea that, depending on the transmissibility level of the infection, either the number of different contacts, or repeated exposure, may be the key factor for transmission.Fabrizio IozziFrancesco TrusianoMatteo ChinazziFrancesco C BillariEmilio ZagheniStefano MerlerMarco AjelliEmanuele Del FavaPiero ManfrediPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 12, p e1001021 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Fabrizio Iozzi Francesco Trusiano Matteo Chinazzi Francesco C Billari Emilio Zagheni Stefano Merler Marco Ajelli Emanuele Del Fava Piero Manfredi Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
description	Knowledge of social contact patterns still represents the most critical step for understanding the spread of directly transmitted infections. Data on social contact patterns are, however, expensive to obtain. A major issue is then whether the simulation of synthetic societies might be helpful to reliably reconstruct such data. In this paper, we compute a variety of synthetic age-specific contact matrices through simulation of a simple individual-based model (IBM). The model is informed by Italian Time Use data and routine socio-demographic data (e.g., school and workplace attendance, household structure, etc.). The model is named "Little Italy" because each artificial agent is a clone of a real person. In other words, each agent's daily diary is the one observed in a corresponding real individual sampled in the Italian Time Use Survey. We also generated contact matrices from the socio-demographic model underlying the Italian IBM for pandemic prediction. These synthetic matrices are then validated against recently collected Italian serological data for Varicella (VZV) and ParvoVirus (B19). Their performance in fitting sero-profiles are compared with other matrices available for Italy, such as the Polymod matrix. Synthetic matrices show the same qualitative features of the ones estimated from sample surveys: for example, strong assortativeness and the presence of super- and sub-diagonal stripes related to contacts between parents and children. Once validated against serological data, Little Italy matrices fit worse than the Polymod one for VZV, but better than concurrent matrices for B19. This is the first occasion where synthetic contact matrices are systematically compared with real ones, and validated against epidemiological data. The results suggest that simple, carefully designed, synthetic matrices can provide a fruitful complementary approach to questionnaire-based matrices. The paper also supports the idea that, depending on the transmissibility level of the infection, either the number of different contacts, or repeated exposure, may be the key factor for transmission.
format	article
author	Fabrizio Iozzi Francesco Trusiano Matteo Chinazzi Francesco C Billari Emilio Zagheni Stefano Merler Marco Ajelli Emanuele Del Fava Piero Manfredi
author_facet	Fabrizio Iozzi Francesco Trusiano Matteo Chinazzi Francesco C Billari Emilio Zagheni Stefano Merler Marco Ajelli Emanuele Del Fava Piero Manfredi
author_sort	Fabrizio Iozzi
title	Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
title_short	Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
title_full	Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
title_fullStr	Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
title_full_unstemmed	Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
title_sort	little italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/e06531d9491d4f0791e3d3626a603cc4
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Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.

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