Ada W.C.Yan ^{a,b} , Andrew J. Black ^{c }, James M. McCaw ^{b,d,e} , Nicolas Rebuli^{ c} , Joshua V. Ross ^{c }, Annalisa J. Swan^{ a} , Roslyn I. Hickson ^{a}. *The distribution of the time taken for an epidemic to spread between two communities.* Mathematical Biosciences, Volume 303, September 2018, Pages 139-147. https://doi.org/10.1016/j.mbs.2018.07.004

**Abstract**

Assessing the risk of disease spread between communities is important in our highly connected modern world. However, the impact of disease- and population-specific factors on the time taken for an epidemic to spread between communities, as well as the impact of stochastic disease dynamics on this spreading time, are not well understood. In this study, we model the spread of an acute infection between two communities (‘patches’) using a susceptible-infectious-removed (SIR) metapopulation model. We develop approximations to efficiently evaluate the probability of a major outbreak in a second patch given disease introduction in a source patch, and the distribution of the time taken for this to occur. We use these approximations to assess how interventions, which either control disease spread within a patch or decrease the travel rate between patches, change the spreading probability and median spreading time.

We find that decreasing the basic reproduction number in the source patch is the most effective way of both decreasing the spreading probability, and delaying epidemic spread to the second patch should this occur. Moreover, we show that the qualitative effects of interventions are the same regardless of the approximations used to evaluate the spreading time distribution, but for some regions in parameter space, quantitative findings depend upon the approximations used. Importantly, if we neglect the possibility that an intervention prevents a large outbreak in the source patch altogether, then intervention effectiveness is not estimated accurately.

a IBM Research Australia, Melbourne, VIC 3006, Australia

b School of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia

c School of Mathematical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia

d Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC 3010, Australia

e Modelling and Simulation, Infection and Immunity Theme, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia