Estimating contact patterns relevant to the spread of infectious diseases in Russia.
Understanding human mixing patterns is the key to provide public health decision makers with model-based evaluation of strategies for the control of infectious diseases. Here we conducted a population-based survey in Tomsk, Russia, asking participants to record all their contacts in physical person during the day. We estimated 9.8 contacts per person per day on average, 15.2 when including additional estimated professional contacts. We found that contacts were highly assortative by age, especially for school-age individuals, and the number of contacts negatively correlated with the age of the participant. The network of contacts was quite clustered, with the majority of contacts (about 72%) occurring between family members, students of the same school/university, and work colleagues. School represents the location where the largest number of contacts was recorded - students contacted about 7 individuals per day at school. Our modeling analysis based on the recorded contact patterns supports the importance of modeling age-mixing patterns - we show that, in the case of an epidemic caused by a novel influenza virus, school-age individuals would be the most affected age group, followed by adults aged 35-44 years. In conclusion, this study reveals an age-mixing pattern in general agreement with that estimated for European countries, although with several quantitative differences. The observed differences can be attributable to sociodemographic and cultural differences between countries. The age- and setting-specific contact matrices provided in this study could be instrumental for the design of control measures for airborne infections, specifically targeted on the characteristics of the Russian population.
Bruno Kessler Foundation, Trento, Italy. Electronic address: ajelli@fbk.eu.
Marco
M
Ajelli
School of Social Sciences, University of Trento, Trento, Italy; Tomsk Polytechnic University, Tomsk, Russia.
Maria
M
Litvinova
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003141:Communicable Diseases / Q000453:epidemiology / Q000635:transmission*; D016358:Contact Tracing / Q000379:methods* / Q000706:statistics & numerical data; D058872:Epidemics; D005260:Female; D006306:Health Surveys / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007251:Influenza, Human / Q000453:epidemiology / Q000635:transmission*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012426:Russia / Q000453:epidemiology; D012574:Schools; D012931:Social Environment; D055815:Young Adult
Age; Airborne infectious diseases; Contact pattern; Human behavior; Mathematical modeling