The scaling of contact rates with population density for the infectious disease models

Math Biosci. 2013 Aug;244(2):125-34. doi: 10.1016/j.mbs.2013.04.013. Epub 2013 May 9.


Contact rates and patterns among individuals in a geographic area drive transmission of directly-transmitted pathogens, making it essential to understand and estimate contacts for simulation of disease dynamics. Under the uniform mixing assumption, one of two mechanisms is typically used to describe the relation between contact rate and population density: density-dependent or frequency-dependent. Based on existing evidence of population threshold and human mobility patterns, we formulated a spatial contact model to describe the appropriate form of transmission with initial growth at low density and saturation at higher density. We show that the two mechanisms are extreme cases that do not capture real population movement across all scales. Empirical data of human and wildlife diseases indicate that a nonlinear function may work better when looking at the full spectrum of densities. This estimation can be applied to large areas with population mixing in general activities. For crowds with unusually large densities (e.g., transportation terminals, stadiums, or mass gatherings), the lack of organized social contact structure deviates the physical contacts towards a special case of the spatial contact model - the dynamics of kinetic gas molecule collision. In this case, an ideal gas model with van der Waals correction fits well; existing movement observation data and the contact rate between individuals is estimated using kinetic theory. A complete picture of contact rate scaling with population density may help clarify the definition of transmission rates in heterogeneous, large-scale spatial systems.

Keywords: Contact rate; Crowd dynamics; Epidemic model; Population density; Transmission scaling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Communicable Diseases / epidemiology*
  • Epidemics* / statistics & numerical data
  • Humans
  • Models, Biological*
  • Population Density*