46 research outputs found
Endemicity and prevalence of multipartite viruses under heterogeneous between-host transmission
Multipartite viruses replicate through a puzzling evolutionary strategy.
Their genome is segmented into two or more parts, and encapsidated in separate
particles that appear to propagate independently. Completing the replication
cycle, however, requires the full genome, so that a systemic infection of a
host requires the concurrent presence of several particles. This represents an
apparent evolutionary drawback of multipartitism, while its advantages remain
unclear. A transition from monopartite to multipartite viral forms has been
described in vitro under conditions of high multiplicity of infection,
suggesting that cooperation between defective mutants is a plausible
evolutionary pathway towards multipartitism. However, it is unknown how the
putative advantages that multipartitism might enjoy at the microscopic level
affect its epidemiology, or if an explicit advantange is needed to explain its
ecological persistence. To disentangle which mechanisms might contribute to the
rise and fixation of multipartitism, we investigate the interaction between
viral spreading dynamics and host population structure. We set up a
compartmental model of the spread of a virus in its different forms and explore
its epidemiology using both analytical and numerical techniques. We uncover
that the impact of host contact structure on spreading dynamics entails a rich
phenomenology of ecological relationships that includes cooperation,
competition, and commensality. We find that multipartitism might rise to
fixation even in the absence of explicit microscopic advantages. Multipartitism
allows the virus to colonize environments that could not be invaded by the
monopartite form, facilitated by homogeneous contacts among hosts. We
conjecture that these features might have led to an increase in the diversity
and prevalence of multipartite viral forms concomitantly with the expansion of
agricultural practices.Comment: 27 pages, 4 figures, 1 tabl
Analytical computation of the epidemic threshold on temporal networks
The time variation of contacts in a networked system may fundamentally alter
the properties of spreading processes and affect the condition for large-scale
propagation, as encoded in the epidemic threshold. Despite the great interest
in the problem for the physics, applied mathematics, computer science and
epidemiology communities, a full theoretical understanding is still missing and
currently limited to the cases where the time-scale separation holds between
spreading and network dynamics or to specific temporal network models. We
consider a Markov chain description of the Susceptible-Infectious-Susceptible
process on an arbitrary temporal network. By adopting a multilayer perspective,
we develop a general analytical derivation of the epidemic threshold in terms
of the spectral radius of a matrix that encodes both network structure and
disease dynamics. The accuracy of the approach is confirmed on a set of
temporal models and empirical networks and against numerical results. In
addition, we explore how the threshold changes when varying the overall time of
observation of the temporal network, so as to provide insights on the optimal
time window for data collection of empirical temporal networked systems. Our
framework is both of fundamental and practical interest, as it offers novel
understanding of the interplay between temporal networks and spreading
dynamics.Comment: 22 pages, 6 figure
Epidemic Threshold in Continuous-Time Evolving Networks
Current understanding of the critical outbreak condition on temporal networks
relies on approximations (time scale separation, discretization) that may bias
the results. We propose a theoretical framework to compute the epidemic
threshold in continuous time through the infection propagator approach. We
introduce the {\em weak commutation} condition allowing the interpretation of
annealed networks, activity-driven networks, and time scale separation into one
formalism. Our work provides a coherent connection between discrete and
continuous time representations applicable to realistic scenarios.Comment: 13 pages, 2 figure
Analyse quantitative de la vulnérabilité des réseaux temporels aux maladies infectieuses
Infectious disease modeling represents a powerful tool for assessing the vulnerability of a population to the introduction of a new infectious pathogen. The increased availability of highly resolved data tracking host interactions is making epidemic models potentially increasingly accurate. Integrating into them all the features emerging from these data, however, still represents a challenge. In particular, the interaction between disease dynamics and the time evolution of contact structures has been shown to impact the way pathogens spread, changing the conditions that lead to the wide-spreading regime, as encoded in epidemic threshold. Up to now researchers have characterized the epidemic threshold on time evolving contact structures only in specific settings. Using a multilayer formalism, we analytically compute the epidemic threshold on a generic temporal network, accounting for several different disease features. We use this methodology to assess the impact of time resolution and network duration on the estimation of the threshold. Then, thanks to it, we assess the global vulnerability of different systems to pathogen introduction, and in particular we analyze the networks of cattle trade movements Data collection strategies often inform us only about past network configurations, and that limits our prediction capabilities. We face this by developing a data-driven methodology for predicting targeted epidemic that relies only past contact data. Our work provides new methodologies for assessing and predicting the risk associated to an emerging pathogen, both at the population scale and targeting specific hosts.La modélisation des maladies infectieuses représente un outil important pour évaluer la vulnérabilité d'une population à l'introduction d'un nouveau agent pathogène. La possibilité d’enregistrer les contacts responsables de la propagation des maladies représente à la fois une ressource et un défi pour les modèles épidémiques. En particulier, l'interaction entre la dynamique des maladies et l'évolution dans le temps des structures de contact influence la façon dont les agents pathogènes se propagent, en changeant les conditions qui mènent à une flambée épidémique (seuil épidémique). Jusqu'à maintenant, les chercheurs n'ont caractérisé le seuil épidémique sur des structures de contact qui évoluent dans le temps que dans des contextes spécifiques. En utilisant un formalisme multi-couches, nous calculons analytiquement le seuil épidémique sur un réseau temporel générique. Nous utilisons cette méthode pour évaluer l'impact de la résolution temporelle et la durée du réseau sur l'estimation du seuil. De plus, grâce à cette méthode, nous évaluons la vulnérabilité globale de différents systèmes à l'introduction d'agents pathogènes, et en particulier nous analysons les réseaux de mouvements des bovins. Les données de contact souvent ne sont pas disponible en temps réel, et cela limite notre capacité de prévision. Pour répondre à ça, nous développons une méthodologie numérique pour prédire le risque épidémique ciblé, qui repose uniquement sur les données de contact passées. Notre travail fournit de nouvelles méthodologies pour évaluer et prédire le risque associé à un agent pathogène émergent, à la fois à l'échelle de la population et en ciblant des hôtes spécifiques
Predicting epidemic risk from past temporal contact data
Understanding how epidemics spread in a system is a crucial step to prevent
and control outbreaks, with broad implications on the system's functioning,
health, and associated costs. This can be achieved by identifying the elements
at higher risk of infection and implementing targeted surveillance and control
measures. One important ingredient to consider is the pattern of
disease-transmission contacts among the elements, however lack of data or
delays in providing updated records may hinder its use, especially for
time-varying patterns. Here we explore to what extent it is possible to use
past temporal data of a system's pattern of contacts to predict the risk of
infection of its elements during an emerging outbreak, in absence of updated
data. We focus on two real-world temporal systems; a livestock displacements
trade network among animal holdings, and a network of sexual encounters in
high-end prostitution. We define the node's loyalty as a local measure of its
tendency to maintain contacts with the same elements over time, and uncover
important non-trivial correlations with the node's epidemic risk. We show that
a risk assessment analysis incorporating this knowledge and based on past
structural and temporal pattern properties provides accurate predictions for
both systems. Its generalizability is tested by introducing a theoretical model
for generating synthetic temporal networks. High accuracy of our predictions is
recovered across different settings, while the amount of possible predictions
is system-specific. The proposed method can provide crucial information for the
setup of targeted intervention strategies.Comment: 24 pages, 5 figures + SI (18 pages, 15 figures
Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk
The ability to directly record human face-to-face interactions increasingly
enables the development of detailed data-driven models for the spread of
directly transmitted infectious diseases at the scale of individuals. Complete
coverage of the contacts occurring in a population is however generally
unattainable, due for instance to limited participation rates or experimental
constraints in spatial coverage. Here, we study the impact of spatially
constrained sampling on our ability to estimate the epidemic risk in a
population using such detailed data-driven models. The epidemic risk is
quantified by the epidemic threshold of the
susceptible-infectious-recovered-susceptible model for the propagation of
communicable diseases, i.e. the critical value of disease transmissibility
above which the disease turns endemic. We verify for both synthetic and
empirical data of human interactions that the use of incomplete data sets due
to spatial sampling leads to the underestimation of the epidemic risk. The bias
is however smaller than the one obtained by uniformly sampling the same
fraction of contacts: it depends nonlinearly on the fraction of contacts that
are recorded and becomes negligible if this fraction is large enough. Moreover,
it depends on the interplay between the timescales of population and spreading
dynamics.Comment: 21 pages, 7 figure
Estimates of the reproduction ratio from epidemic surveillance may be biased in spatially structured populations
An accurate and timely estimate of the reproduction ratio R of an infectious
disease epidemic is crucial to make projections on its evolution and set up the
appropriate public health response. Estimates of R routinely come from
statistical inference on timelines of cases or their proxies like symptomatic
cases, hospitalizatons, deaths. Here, however, we prove that these estimates of
R may not be accurate if the population is made up of spatially distinct
communities, as the interplay between space and mobility may hide the true
epidemic evolution from surveillance data. This means that surveillance may
underestimate R over long periods, to the point of mistaking a growing epidemic
for a subsiding one, misinforming public health response. To overcome this, we
propose a correction to be applied to surveillance data that removes this bias
and ensures an accurate estimate of R across all epidemic phases. We use
COVID-19 as case study; our results, however, apply to any epidemic where
mobility is a driver of circulation, including major challenges of the next
decades: respiratory infections (influenza, SARS-CoV-2, emerging pathogens),
vector-borne diseases (arboviruses). Our findings will help set up public
health response to these threats, by improving epidemic monitoring and
surveillance.Comment: 11 pages, 4 figures, plus Supplementary Informatio
Dynamics of new strain emergence on a temporal network
Multi-strain competition on networks is observed in many contexts, including
infectious disease ecology, information dissemination or behavioral adaptation
to epidemics. Despite a substantial body of research has been developed
considering static, time-aggregated networks, it remains a challenge to
understand the transmission of concurrent strains when links of the network are
created and destroyed over time. Here we analyze how network dynamics shapes
the outcome of the competition between an initially endemic strain and an
emerging one, when both strains follow a susceptible-infected-susceptible
dynamics, and spread at time scales comparable with the network evolution one.
Using time-resolved data of close-proximity interactions between patients
admitted to a hospital and medical health care workers, we analyze the impact
of temporal patterns and initial conditions on the dominance diagram and
coexistence time. We find that strong variations in activity volume cause the
probability that the emerging strain replaces the endemic one to be highly
sensitive to the time of emergence. The temporal structure of the network
shapes the dominance diagram, with significant variations in the replacement
probability (for a given set of epidemiological parameters) observed from the
empirical network and a randomized version of it. Our work contributes towards
the description of the complex interplay between competing pathogens on
temporal networks.Comment: 9 pages, 4 figure