Intervention-based stochastic disease eradication.
Disease control is of paramount importance in public health, with infectious disease extinction as the ultimate goal. Although diseases may go extinct due to random loss of effective contacts where the infection is transmitted to new susceptible individuals, the time to extinction in the absence of control may be prohibitively long. Intervention controls are typically defined on a deterministic schedule. In reality, however, such policies are administered as a random process, while still possessing a mean period. Here, we consider the effect of randomly distributed intervention as disease control on large finite populations. We show explicitly how intervention control, based on mean period and treatment fraction, modulates the average extinction times as a function of population size and rate of infection spread. In particular, our results show an exponential improvement in extinction times even though the controls are implemented using a random Poisson distribution. Finally, we discover those parameter regimes where random treatment yields an exponential improvement in extinction times over the application of strictly periodic intervention. The implication of our results is discussed in light of the availability of limited resources for control.
Department of Mathematical Sciences, Montclair State University, Montclair, New Jerey, USA. billingsl@mail.montclair.edu
Lora
L
Billings
Luis
L
Mier-y-Teran-Romero
Brandon
B
Lindley
Ira B
IB
Schwartz
D060740:Disease Eradication; D006801:Humans; D008962:Models, Theoretical; D016012:Poisson Distribution; D011157:Population Dynamics