Contact patterns and influenza outbreaks in Russian cities: A proof-of-concept study via agent-based modeling

Abstract

In this paper we model influenza propagation in the Russian setting using a spatially explicit model and a detailed human agent database as its input. The aim of our research is to assess the applicability of this modeling method using influenza incidence data for the 2010–2011 epidemic outbreak in Saint Petersburg, and to demonstrate the influence of human contact patterns on the simulated influenza dynamics with a real-case scenario. For this purpose, we use several types of synthetic populations which reflect different ways of distributing people among the workplaces and different contact patterns within the households and the workplaces. The agent-based model is implemented to perform the simulations using Python programming language. We show that in the populations where intensive mixing of people occurs, the influenza outbreaks have higher incidence peak and shorter duration, compared to the populations with less variety of contacts. Hence, the features of contact patterns may dramatically alter the course of epidemics in Russian cities and explain the differences between their outbreak dynamics in the situation when infection virulence and the average number of contacts stay the same.