Epidemic size and probability in populations with heterogeneous infectivity and susceptibility.
We analytically address disease outbreaks in large, random networks with heterogeneous infectivity and susceptibility. The transmissibility T_{uv} (the probability that infection of u causes infection of v ) depends on the infectivity of u and the susceptibility of v . Initially, a single node is infected, following which a large-scale epidemic may or may not occur. We use a generating function approach to study how heterogeneity affects the probability that an epidemic occurs and, if one occurs, its attack rate (the fraction infected). For fixed average transmissibility, we find upper and lower bounds on these. An epidemic is most likely if infectivity is homogeneous and least likely if the variance of infectivity is maximized. Similarly, the attack rate is largest if susceptibility is homogeneous and smallest if the variance is maximized. We further show that heterogeneity in the infectious period is important, contrary to assumptions of previous studies. We confirm our theoretical predictions by simulation. Our results have implications for control strategy design and identification of populations at higher risk from an epidemic.
Mathematical Modeling & Analysis Group and Center for Nonlinear Studies, MS B284, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. jomiller@lanl.gov
Joel C
JC
Miller
D003141:Communicable Diseases / Q000453:epidemiology*; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004198:Disease Susceptibility / Q000453:epidemiology*; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological; D011157:Population Dynamics; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors