[Population Modeling] work on modeling social mixing patterns

[Marco Ajelli]

<popmodwkgrpimag-news at simtk.org>Dear modeling working group,

below my contribution where I present our work "Fumanelli, et al, (2012)
Inferring the Structure of Social Contacts from Demographic Data in the
Analysis of Infectious Diseases Spread. PLoS Comput Biol 8(9): e1002673".

Best,
Marco





*Author and affiliations:* Marco Ajelli, Laboratory for the Modeling of
Biological and Socio-technical Systems, Northeastern University, Boston,
MA, USA & Bruno Kessler Foundation, Trento, Italy



*Research area*: Epidemiology and public health



*Keywords*: Agent based model





*Summary*



Social contacts are essential for the spread of human-to-human
transmissible infectious diseases. As such, they are a key element of
mathematical/computational models of infectious diseases transmission as
well (1). In the last decade lot of attention has been posed to quantify
social mixing patterns – age mixing patterns in particular – and a variety
of approaches has been used. Among them, diary-based surveys (2–4), use of
wearable sensors (5,6), analysis of time-use data (7,8), and the
development of synthetic populations of agents (8).

In the latter category falls our work (9), where we estimated age-mixing
patterns in 26 European countries through the simulation of a synthetic
population of agents mimicking real-world contacts between individuals. To
build the synthetic populations we relies on publicly available census and
survey data on the main socio-demographic characteristics of each country
(e.g., household size, age distribution by household size, schooling and
employment rates by age). The resulting contact matrices describing the
average frequency of “adequate” contacts that an individual of age *i* has
with individuals aged *j* is derived by analysis of the contact network of
the agents of the simulated population. The inferred contact matrices are
validated by a detailed comparison with the matrices obtained in six
European countries by the most extensive diary-based study on mixing
patterns (2) and against epidemiological influenza data (10).

Our methodology allows a large-scale comparison of mixing patterns in
Europe, highlighting general common features as well as country-specific
differences. We find clear relations between epidemiologically relevant
quantities (such as reproduction number and epidemic size) and
socio-demographic characteristics of the populations (e.g., average age of
the population). In this study we provide a numerical approach for the
generation of human mixing patterns, which is straightforward to apply to
other countries as it is entirely based on routinely collected
socio-demographic statistics. Our approach could be instrumental for
improving model accuracy, especially in the absence of specific empirical
data on human mixing patterns.




*References*
1.         Eames K, Bansal S, Frost S, Riley S. Six challenges in measuring
contact networks for use in modelling. Epidemics. 2015; 10:72–7.

2.         Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R,
et al. Social Contacts and Mixing Patterns Relevant to the Spread of
Infectious Diseases. PLOS Med. 2008; 5(3):e74.

3.         Read JM, Lessler J, Riley S, Wang S, Tan LJ, Kwok KO, et al.
Social mixing patterns in rural and urban areas of southern China. Proc R
Soc B 2014; 281(1785):20140268.

4.         Ajelli M, Litvinova M. Estimating contact patterns relevant to
the spread of infectious diseases in Russia. J Theor Biol. 2017; 419:1–7.

5.         Cattuto C, Broeck WV den, Barrat A, Colizza V, Pinton J-F,
Vespignani A. Dynamics of Person-to-Person Interactions from Distributed
RFID Sensor Networks. PLOS ONE. 2010; 5(7):e11596.

6.         Kiti MC, Tizzoni M, Kinyanjui TM, Koech DC, Munywoki PK, Meriac
M, et al. Quantifying social contacts in a household setting of rural Kenya
using wearable proximity sensors. EPJ Data Sci. 2016; 5(1):1–21.

7.         Zagheni E, Billari FC, Manfredi P, Melegaro A, Mossong J,
Edmunds WJ. Using Time-Use Data to Parameterize Models for the Spread of
Close-Contact Infectious Diseases. Am J Epidemiol. 2008; 168(9):1082–90.

8.         Iozzi F, Trusiano F, Chinazzi M, Billari FC, Zagheni E, Merler
S, et al. Little Italy: an agent-based approach to the estimation of
contact patterns- fitting predicted matrices to serological data. PLOS
Comput Biol. 2010; 6(12):e1001021.

9.         Fumanelli L, Ajelli M, Manfredi P, Vespignani A, Merler S.
Inferring the Structure of Social Contacts from Demographic Data in the
Analysis of Infectious Diseases Spread. PLOS Comput Biol. 2012;
8(9):e1002673.

10.       Hardelid P, Andrews N, Hoschler K, Stanford E, Baguelin M, Waight
P, et al. Assessment of baseline age-specific antibody prevalence and
incidence of infection to novel influenza AH1N1 2009. Health Technol
Assess. 2010; 14(55).





-- 
Marco Ajelli, PhD

Associate Research Scientist
Laboratory for the Modeling of Biological and Socio-technical Systems
Northeastern University
177 Huntington Avenue
Boston, MA 02115
USA

Tenured Research Scientist
Bruno Kessler Foundation
Via Sommarive 18
I-38123 Trento
Italy

Tel (Boston): +1 617 373 4222
Tel (Trento): +39 0461 314 520

E-mail: m.ajelli at northeastern.edu
E-mail: ajelli at fbk.eu
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