<div dir="ltr"><div><font color="#0066cc"><span style="font-size:12.8px"><br></span></font></div><div><font color="#000000"><a href="mailto:popmodwkgrpimag-news@simtk.org" target="_blank"></a><span style="font-size:12.8px">Dear modeling working group,</span></font></div><div><span style="font-size:12.8px"><font color="#000000"><br></font></span></div><div><span style="font-size:12.8px"><font color="#000000">below my contribution where I present our work "Fumanelli, et al, </font></span>(2012) Inferring the Structure of Social Contacts from Demographic Data in the Analysis of Infectious Diseases Spread. PLoS Comput Biol 8(9): e1002673"<span style="color:rgb(0,0,0);font-size:12.8px">.</span></div><div><span style="font-size:12.8px"><font color="#000000"><br></font></span></div><div><span style="font-size:12.8px"><font color="#000000">Best,</font></span></div><div><span style="font-size:12.8px"><font color="#000000">Marco<br clear="all"></font></span><div><font color="#000000"><br></font></div><div><br></div><div><font color="#000000"><br></font></div><div>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><b>Author and
affiliations:</b> Marco Ajelli, Laboratory for the Modeling of Biological and
Socio-technical Systems, Northeastern University, Boston, MA, USA & Bruno
Kessler Foundation, Trento, Italy<span></span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><b>Research area</b>: Epidemiology
and public health<span></span></p>
<p class="MsoNormal"><b> </b></p>
<p class="MsoNormal"><b>Keywords</b>: Agent
based model<span></span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><b>Summary<span></span></b></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal">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 <span style="color:rgb(38,38,38)">diseases transmission as well </span><span style="color:rgb(38,38,38)">(1)</span><span style="color:rgb(38,38,38)">. </span>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). <br>
<br>
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>i</i> has with individuals aged <i>j</i>
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). <br>
<br>
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. <br>
<br>
<span></span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><b>References<br></b><br>1. Eames
K, Bansal S, Frost S, Riley S. Six challenges in measuring contact networks for
use in modelling. Epidemics. 2015; 10:72–7.<br><br>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.<br><br>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.<br><br>4. Ajelli M, Litvinova M. Estimating
contact patterns relevant to the spread of infectious diseases in Russia. J
Theor Biol. 2017; 419:1–7.</p><p class="gmail-MsoBibliography">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.</p><p class="gmail-MsoBibliography">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.<br><br>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.<br><br>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.</p><p class="gmail-MsoBibliography">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.<br><br>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).</p><p class="gmail-MsoBibliography"><span></span></p>
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</div><div><font color="#000000"><br></font></div><div><br></div><div><br></div>-- <br><div class="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div>Marco Ajelli, PhD<br><br>Associate Research Scientist<br>Laboratory for the Modeling of Biological and Socio-technical Systems<br>Northeastern University<br>177 Huntington Avenue<br>Boston, MA 02115<br>USA<br><br><span style="font-size:12.8px">Tenured Research Scientist</span><br style="font-size:12.8px"><span style="font-size:12.8px">Bruno Kessler Foundation</span><br style="font-size:12.8px"><span style="font-size:12.8px">Via Sommarive 18</span><br style="font-size:12.8px"><span style="font-size:12.8px">I-38123 Trento</span><br style="font-size:12.8px"><span style="font-size:12.8px">Italy</span><br><br><span style="font-size:12.8px">Tel (Boston): +1 617 373 4222</span><br>Tel (Trento): +39 0461 314 520<br><br><span style="font-size:12.8px">E-mail: </span><a href="mailto:m.ajelli@northeastern.edu" style="font-size:12.8px" target="_blank">m.ajelli@northeastern.edu</a><br></div><div>E-mail: <a href="mailto:ajelli@fbk.eu" target="_blank">ajelli@fbk.eu</a><br></div></div></div></div></div>
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