17,980 research outputs found
Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model
Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes
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
Stochasticity in pandemic spread over the World Airline Network explained by local flight connections
Massive growth in human mobility has dramatically increased the risk and rate
of pandemic spread. Macro-level descriptors of the topology of the World
Airline Network (WAN) explains middle and late stage dynamics of pandemic
spread mediated by this network, but necessarily regard early stage variation
as stochastic. We propose that much of early stage variation can be explained
by appropriately characterizing the local topology surrounding the debut
location of an outbreak. We measure for each airport the expected force of
infection (AEF) which a pandemic originating at that airport would generate. We
observe, for a subset of world airports, the minimum transmission rate at which
a disease becomes pandemically competent at each airport. We also observe, for
a larger subset, the time until a pandemically competent outbreak achieves
pandemic status given its debut location. Observations are generated using a
highly sophisticated metapopulation reaction-diffusion simulator under a
disease model known to well replicate the 2009 influenza pandemic. The
robustness of the AEF measure to model misspecification is examined by
degrading the network model. AEF powerfully explains pandemic risk, showing
correlation of 0.90 to the transmission level needed to give a disease pandemic
competence, and correlation of 0.85 to the delay until an outbreak becomes a
pandemic. The AEF is robust to model misspecification. For 97% of airports,
removing 15% of airports from the model changes their AEF metric by less than
1%. Appropriately summarizing the size, shape, and diversity of an airport's
local neighborhood in the WAN accurately explains much of the macro-level
stochasticity in pandemic outcomes.Comment: article text: 6 pages, 5 figures, 28 reference
Progression of extrapyramidal signs in Alzheimer's disease. clinical and neuropathological correlates
Extrapyramidal signs (EPS) are frequent in Alzheimer's disease (AD) and core manifestation of related diseases, i.e., dementia with Lewy bodies and Parkinson's disease; furthermore, Lewy bodies and AD-type pathology occur in all three conditions
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