Source: Adv. in Appl. Probab. Volume 43, Number 4
(2011), 1048-1065.
This paper is concerned with a stochastic model for the spread of an epidemic
with a contact tracing scheme, in which diagnosed individuals may name some of
their infectious contacts, who are then removed if they have not been already.
Traced individuals may or may not also be asked to name their own contacts. The
epidemic is studied by considering an approximating, modified birth-death
process with intersibling dependencies, for which a threshold parameter and
expressions from which extinction probabilities may be calculated are derived.
When all individuals can name their contacts, it is shown that this
threshold parameter depends on the infectious period distribution only through
its mean. Numerical studies show that the infectious period distribution choice
can have a material effect on the threshold behaviour of an epidemic, while the
dependencies help reduce spread.
Full-text: Access denied (no subscription
detected)
We're sorry, but we are unable to provide
you with the full text of this article because we are not able to identify
you as a subscriber.
If you have a personal subscription to
this journal, then please login. If you are already logged in, then you
may need to update your profile to register your subscription.
Read more about accessing full-text
References
Athreya, K. B. and Karlin, S. (1971). On branching processes with random environments. I. Extinction probabilities. Ann. Math. Statist. 42, 1499–1520.
Mathematical Reviews (MathSciNet):
MR298780
Ball, F. G. (1983). The threshold behaviour of epidemic models. J. Appl. Prob. 20, 227–241.
Mathematical Reviews (MathSciNet):
MR698527
Ball, F. and Donnelly, P. (1995). Strong approximations for epidemic models. Stoch. Process. Appl. 55, 1–21.
Ball, F. G., Knock, E. S. and O'Neill, P. D. (2008). Control of emerging infectious diseases using responsive imperfect vaccination and isolation. Math. Biosci. 216, 100–113.
Ball, F., O'Neill, P. D. and Pike, J. (2007). Stochastic epidemic models in structured populations featuring dynamic vaccination and isolation. J. Appl. Prob. 44, 571–585.
Becker, N. G., Glass, K., Li, Z. and Aldis, G. K. (2005). Controlling emerging infectious diseases like SARS. Math. Biosci. 193, 205–221.
Britton, T., Janson, S. and Martin-Löf, A. (2007). Graphs with specified degree distributions, simple epidemics, and local vaccination strategies. Adv. Appl. Prob. 39, 922–948.
Haccou, P., Jagers, P. and Vatutin, V. A. (2005). Branching processes: Variation, Growth, and Extinction of Populations. Cambridge University Press.
Heesterbeek, J. A. P. and Roberts, M. G. (2007). The type-reproduction number T in models for infectious disease control. Math. Biosci. 206, 3–10.
Kaplan, E. H., Craft, D. L. and Wein, L. M. (2002). Emergency response to a smallpox attack: the case for mass vaccination. Proc. Nat. Acad. Sci. USA 99, 10935–10940.
Klebaner, F. C. (1989). Linear growth in near-critical population-size-dependent multitype Galton–Watson processes. J. Appl. Prob. 26, 431–445.
Klinkenberg, D., Fraser, C. and Heesterbeek, H. (2006). The effectiveness of contact tracing in emerging epidemics. PLoS ONE 1, e13.
Knock, E. S. (2011). Stochastic epidemic models for emerging diseases incorporating household structure and contact tracing. Doctoral Thesis, University of Nottingham. Available at http://etheses.nottingham.ac.uk/2046.
Lambert, A. (2010). The contour of splitting trees is a Lévy process. Ann. Prob. 38, 348–395.
Müller, J., Kretzschmar, M. and Dietz, K. (2000). Contact tracing in stochastic and deterministic epidemic models. Math. Biosci. 164, 39–64.
Olofsson, P. (1996). Branching processes with local dependencies. Ann. Appl. Prob. 6, 238–268.
Shaban, N., Andersson, M., Svensson, Å. and Britton, T. (2008). Networks, epidemics and vaccination through contact tracing. Math. Biosci. 216, 1–8.