Pathogen spread on coupled networks: Effect of host and network properties on transmission thresholds

• Published in: Journal of theoretical biology Vol. 320; pp. 47 - 57
• Main Authors: Mills, H.L., Ganesh, A., Colijn, C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 07.03.2013
• Subjects: behavior change; HIV Infections - epidemiology; HIV Infections - transmission; Humans; Interacting networks; mixed infection; Models, Biological; Multi-type hosts; Network models; pathogens; Percolation theory; risk; Tuberculosis - epidemiology; Tuberculosis - transmission; vaccination; Multi-type hosts; Percolation theory; Network models; Interacting networks
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2012.12.006

Human populations are interconnected through a variety of different networks. The complex interactions of diverse populations of individuals and their interconnected network structures affect the diffusion of processes through the population. For example, the different modes of transmission of HIV and TB mean that they are transmitted along very different contact networks: HIV via sexual contact and TB via respiratory contact. In addition, co-infection with HIV raises the risk of progressing to active TB and reduces the response to TB treatment, potentially causing increasing incidence of TB. Here we extend existing network theory to find the effect of multiple networks and multiple host types on epidemic thresholds. We first analyse how transmission of a pathogen via an additional network affects its epidemic threshold. We then use the theory behind branching processes to study how multiple host types in a population affect its threshold. The formulation we obtain enables modellers to determine how multiple networks and host heterogeneity (i.e. vaccination, behavioural change, mixing patterns, etc.) affect the epidemic threshold. We apply the results to the example of HIV and TB to illustrate how the interactions of the diseases can substantially alter the epidemic threshold of TB. ► We use 2 mathematical modelling frameworks to find exact solutions for an epidemic. ► We extend percolation theory to a scenario with two transmission networks. ► We use branching processes to study effect of multiple host types on an epidemic. ► We apply our results to the example of HIV and TB.