Cost-benefit Analysis of Epidemics Spreading on Clustered Random Networks

Abstract

We study, control of infectious disease epidemics spreading on random networks with different levels of clustering. We use Gleeson’s et al., Phys. Rev. E80, 036107 (2009) algorithm to create clustered networks in which a proportion of individuals is located in fully-connected cliques of certain size. A SIR model is extended to include delayed and imperfect detection of infectious individuals. We also include a combination of responsive (palliative) and preventive (vaccination) treatments and design cost-effective disease control strategies. Cost-benefit analysis is used in combination with epidemiological simulations to identify an optimal radius for a treatment centred upon the symptomatic individual. Three general control strategies occur depending on the relative cost of treatment and prevention. Network topology and, in particular, clustering also affects the applicability of the control strategy. The average path length appears to be more important; the range for the control strategy is wider with the length, but the optimal radius of control also extends. As the proportion of individuals in cliques and therefore the coefficient of clustering is higher, the range of the costs for which control scenario is optimal is greater. This results have important consequences for designing disease control strategies that also satisfy economic optimality criteria.