tetano
Editor, Senior Moderator
R Soc Open Sci. 2019 Jan 16;6(1):181404. doi: 10.1098/rsos.181404. eCollection 2019 Jan.
[h=1]Disease persistence on temporal contact networks accounting for heterogeneous infectious periods.[/h] Darbon A[SUP]1[/SUP], Colombi D[SUP]1[/SUP], Valdano E[SUP]2[/SUP], Savini L[SUP]3[/SUP], Giovannini A[SUP]3[/SUP], Colizza V[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] epidemic spread; epidemic threshold; mathematical modelling; susceptible-infectious-susceptible model; temporal network
PMID: 30800384 PMCID: PMC6366198 DOI: 10.1098/rsos.181404
[h=1]Disease persistence on temporal contact networks accounting for heterogeneous infectious periods.[/h] Darbon A[SUP]1[/SUP], Colombi D[SUP]1[/SUP], Valdano E[SUP]2[/SUP], Savini L[SUP]3[/SUP], Giovannini A[SUP]3[/SUP], Colizza V[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] epidemic spread; epidemic threshold; mathematical modelling; susceptible-infectious-susceptible model; temporal network
PMID: 30800384 PMCID: PMC6366198 DOI: 10.1098/rsos.181404