Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data

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.

The impact of demographic changes on the epidemiology of herpes zoster: Spain as a case study

“…Actual mixing patterns may change significantly over time, following changes in socio-demographic characteristics [37,47] (e.g. household size and composition, characteristics of the educational system, school attendance levels).…”

Close encounters of the infectious kind: methods to measure social mixing behaviour

“…Two studies have compared contact patterns predicted by synthetic population modelling with observed patterns. The ‘Little Italy’ study [91] found a good comparison between the model’s age-based mixing patterns and Italian contact patterns measured by the POLYMOD study [60]. Yang et al .…”

The role of different social contexts in shaping influenza transmission during the 2009 pandemic

“…Accounting for the time spent by individuals of different ages in different social contexts and employment types allows us to mimic the complex heterogeneous mixing of individuals within the population. Similar approaches have already been proposed, for instance in562132 for studying airborne-transmitted diseases (like smallpox and influenza) and for deriving synthetic contact matrices by age. Such a high level of detail allows us to disentangle the contribution of the distinct social settings in the spread of the 2009 influenza pandemic.…”