Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data
| العنوان: | Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data |
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| المؤلفون: | Francesco C. Billari, Francesco Trusiano, Matteo Chinazzi, Stefano Merler, Fabrizio Iozzi, Piero Manfredi, Emilio Zagheni, Marco Ajelli, Emanuele Del Fava |
| المصدر: | PLoS Computational Biology, Vol 6, Iss 12, p e1001021 (2010) PLoS Computational Biology PLoS Computational Biology; Vol 6 |
| بيانات النشر: | Public Library of Science (PLoS), 2010. |
| سنة النشر: | 2010 |
| مصطلحات موضوعية: | Herpesvirus 3, Human, Databases, Factual, Computer science, Public Health and Epidemiology/Infectious Diseases, computer.software_genre, Matrix (mathematics), 0302 clinical medicine, Chickenpox, social contact patterns, Simple (abstract algebra), Parvovirus B19, Human, 030212 general & internal medicine, Biology (General), Child, fitting serological data, 0303 health sciences, Ecology, childhood infections, Middle Aged, Transmissibility (vibration), 3. Good health, Time-use survey, Computational Theory and Mathematics, Modeling and Simulation, Child, Preschool, infection transmission, Data mining, Research Article, Infectious Diseases/Epidemiology and Control of Infectious Diseases, Adult, Social contact, Adolescent, QH301-705.5, social contact, agent-based models, epidemics, Sample (statistics), Parvoviridae Infections, 03 medical and health sciences, Cellular and Molecular Neuroscience, Infectious Diseases/Viral Infections, Genetics, Humans, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Aged, Structure (mathematical logic), Estimation, Computational Biology, Infant, Reproducibility of Results, Models, Theoretical, Mathematics/Statistics, Contact Tracing, computer |
| الوصف: | 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. Author Summary Data on social contact patterns are fundamental to design adequate control policies for directly transmissible infectious diseases, ranging from a flu pandemic to tuberculosis, to recurrent epidemics of childhood diseases. Most countries in the world do not dispose of such data. We propose an approach to generate synthetic contact data by simulating an artificial society that integrates routinely available socio-demographic data, such as data on household composition or on school participation, with Time Use data, which are increasingly available. We then validate the ensuing simulated contact data against real epidemiological data for varicella and parvo-virus. The results suggest that the approach is potentially a very fruitful one, and provide some insights on the biology of transmission of close-contact infectious diseases. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1553-7358 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e0aec27dd90dbb024f0879e430ad8eeTest https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21152004/pdf/?tool=EBITest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....4e0aec27dd90dbb024f0879e430ad8ee |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15537358 |
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