Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The endemic persistence of infectious diseases can often not be understood without taking into account the relevant heterogeneities of host mixing. Here, we consider spatial heterogeneity, defined as 'patchiness' of the host population. After briefly reviewing how disease persistence is influenced by population size, reproduction number and infectious period, we explore its dependence on the level of spatial heterogeneity. Analysis and simulation of disease transmission in a symmetric meta-population suggest that disease persistence typically becomes worse as spatial heterogeneity increases, although local persistence optima can occur for infections with oscillatory population dynamics. We obtain insight into the dynamics that underlie the observed persistence patterns by studying the infection prevalence correlation between patches and by comparing full-model simulations to results obtained using simplified patch-level descriptions of the interplay between local extinctions and between-patch transmissions. The observed patterns are interpreted in terms of rescue effects for strong spatial heterogeneity and in terms of between-patch coherence and synchronization effects at intermediate and weak levels of heterogeneity.

Original publication




Journal article


Journal of theoretical biology

Publication Date





349 - 359


Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK.


Humans, Communicable Diseases, Stochastic Processes, Disease Outbreaks, Population Density, Population Dynamics, Models, Biological