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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11567

Title: Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns-Fitting Predicted Matrices to Serological Data
Authors: Iozzi, Fabrizio
Trusiano, Francesco
Chinazzi, Matteo
Billari, Francesco C.
Zagheni, Emilio
Merler, Stefano
Ajelli, Marco
DEL FAVA, Emanuele
Manfredi, Piero
Issue Date: 2010
Abstract: 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.
Notes: [Iozzi, Fabrizio] Bocconi Univ, Dept Decis Sci, Milan, Italy. [Trusiano, Francesco] George Mason Univ, Dept Computat Social Sci, Fairfax, VA 22030 USA. [Chinazzi, Matteo] St Anna Sch Adv Studies, Pisa, Italy. [Billari, Francesco C.] Bocconi Univ, Dondena Ctr Res Social Dynam, Milan, Italy. [Zagheni, Emilio] Univ Calif Berkeley, Dept Demog, Berkeley, CA 94720 USA. [Merler, Stefano; Ajelli, Marco] Bruno Kessler Fdn, Trento Povo, Italy. [Del Fava, Emanuele] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium. [Manfredi, Piero] Univ Pisa, Dipartimento Stat & Matemat Applicata Econ, Pisa, Italy. manfredi@ec.unipi.it
URI: http://hdl.handle.net/1942/11567
DOI: 10.1371/journal.pcbi.1001021
ISI #: 000285574600009
ISSN: 1553-734X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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