The infectious period of a transmissible disease is a key factor for disease spread and persistence. Epidemic models on networks typically assume an identical average infectious period for all individuals, thus allowing an analytical treatment. This simplifying assumption is, however, often unrealistic, as hosts may have different infectious periods, due, for instance, to individual host–pathogen interactions or inhomogeneous access to treatment. While previous work accounted for this heterogeneity in static networks, a full theoretical understanding of the interplay of varying infectious periods and time-evolving contacts is still missing. Here, we consider a susceptible-infectious-susceptible epidemic on a temporal network with host-specific average infectious periods, and develop an analytical framework to estimate the epidemic threshold, i.e. the critical transmissibility for disease spread in the host population. Integrating contact data for transmission with outbreak data and epidemiological estimates, we apply our framework to three real-world case studies exploring different epidemic contexts—the persistence of bovine tuberculosis in southern Italy, the spread of nosocomial infections in a hospital, and the diffusion of pandemic influenza in a school. We find that the homogeneous parametrization may cause important biases in the assessment of the epidemic risk of the host population. Our approach is also able to identify groups of hosts mostly responsible for disease diffusion who may be targeted for prevention and control, aiding public health interventions.

The endemic circulation of bovine brucellosis in cattle herds has a markedly negative impact on economy, due to decreased fertility, increased abortion rates, reduced milk and meat production. It also poses a direct threat to human health. In Italy, despite the long lasting efforts and the considerable economic investment, complete eradication of this disease still eludes the southern regions, as opposed to the northern regions that are disease-free. Here we introduced a novel quantitative network-based approach able to fully exploit the highly resolved databases of cattle trade movements and outbreak reports to yield estimates of the vulnerability of a cattle market to brucellosis. Tested on the affected regions, the introduced vulnerability indicator was shown to be accurate in predicting the number of bovine brucellosis outbreaks (Spearman r= 0.82, p= 0.04), thus confirming the suitability of our tool for epidemic risk assessment. We evaluated the dependence of regional vulnerability to brucellosis on a set of factors including premises spatial distribution, trading patterns, farming practices, herd market value, compliance to outbreak regulations, and exploring different epidemiological conditions. Animal trade movements were identified as a major route for brucellosis spread between farms (r=0.85,p<10 between vulnerability and number of inbound movements), with an additional potential risk attributed to the use of shared pastures (r=0.4,p=0.04). By comparing the vulnerability of disease-free regions in the north to affected regions in the south, we found that more intense trade and higher market value of the cattle sector in the north (r=0.56,p=0.01) likely inducing more efficient biosafety measures, together with poor compliance to trade restrictions following outbreaks in the south were key factors explaining the diverse success in eradicating brucellosis. Our modeling scheme is both synthetic and effective in gauging regional vulnerability to brucellosis persistence. Its general formulation makes it adaptable to other diseases and host species, providing a useful tool for veterinary epidemiology and policy assessment.

The infectious period of a transmissible disease is a key factor for disease spread and persistence. Epidemic models on networks typically assume an identical average infectious period for all individuals, thus allowing an analytical treatment. This simplifying assumption is however often unrealistic, as hosts may have different infectious periods, due, for instance, to individual host-pathogen interactions or inhomogeneous access to treatment. While previous work accounted for this heterogeneity in static networks, a full theoretical understanding of the interplay of varying infectious periods and time-evolving contacts is still missing. Here we consider an SIS epidemic on a temporal network with host-specific average infectious periods, and develop an analytical framework to estimate the epidemic threshold, i.e. the critical transmissibility for disease spread in the host population. Integrating contact data for transmission with outbreak data and epidemiological estimates, we apply our framework to three real-world case studies exploring different epidemic contexts -the persistence of bovine tuberculosis in southern Italy, the spread of nosocomial infections in a hospital, and the diffusion of pandemic influenza in a school. We find that the homogeneous parameterization may cause important biases in the assessment of the epidemic risk of the host population. Our approach is also able to identify groups of hosts mostly responsible for disease diffusion who may be targeted for prevention and control, aiding public health interventions.