202 research outputs found
Traffic Optimization to Control Epidemic Outbreaks in Metapopulation Models
We propose a novel framework to study viral spreading processes in
metapopulation models. Large subpopulations (i.e., cities) are connected via
metalinks (i.e., roads) according to a metagraph structure (i.e., the traffic
infrastructure). The problem of containing the propagation of an epidemic
outbreak in a metapopulation model by controlling the traffic between
subpopulations is considered. Controlling the spread of an epidemic outbreak
can be written as a spectral condition involving the eigenvalues of a matrix
that depends on the network structure and the parameters of the model. Based on
this spectral condition, we propose a convex optimization framework to find
cost-optimal approaches to traffic control in epidemic outbreaks
Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history
Spatial spread of infectious diseases among populations via the mobility of
humans is highly stochastic and heterogeneous. Accurate forecast/mining of the
spread process is often hard to be achieved by using statistical or mechanical
models. Here we propose a new reverse problem, which aims to identify the
stochastically spatial spread process itself from observable information
regarding the arrival history of infectious cases in each subpopulation. We
solved the problem by developing an efficient optimization algorithm based on
dynamical programming, which comprises three procedures: i, anatomizing the
whole spread process among all subpopulations into disjoint componential
patches; ii, inferring the most probable invasion pathways underlying each
patch via maximum likelihood estimation; iii, recovering the whole process by
assembling the invasion pathways in each patch iteratively, without burdens in
parameter calibrations and computer simulations. Based on the entropy theory,
we introduced an identifiability measure to assess the difficulty level that an
invasion pathway can be identified. Results on both artificial and empirical
metapopulation networks show the robust performance in identifying actual
invasion pathways driving pandemic spread.Comment: 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetic
A decision-support framework to optimize border control for global outbreak mitigation
The introduction and spread of emerging infectious diseases is increasing in both prevalence and scale. Whether naturally, accidentally or maliciously introduced, the substantial uncertainty surrounding the emergence of novel viruses, specifically where they may come from and how they will spread, demands robust and quantifiably validated outbreak control policies that can be implemented in real time. This work presents a novel mathematical modeling framework that integrates both outbreak dynamics and outbreak control into a decision support tool for mitigating infectious disease pandemics that spread through passenger air travel. An ensemble of border control strategies that exploit properties of the air traffic network structure and expected outbreak behavior are proposed. A stochastic metapopulation epidemic model is developed to evaluate and rank the control strategies based on their effectiveness in reducing the spread of outbreaks. Sensitivity analyses are conducted to illustrate the robustness of the proposed control strategies across a range of outbreak scenarios, and a case study is presented for the 2009 H1N1 influenza pandemic. This study highlights the importance of strategically allocating outbreak control resources, and the results can be used to identify the most robust border control policy that can be implemented in the early stages of an outbreak.
Document type: Preprin
Traffic Control for Network Protection Against Spreading Processes
Epidemic outbreaks in human populations are facilitated by the underlying
transportation network. We consider strategies for containing a viral spreading
process by optimally allocating a limited budget to three types of protection
resources: (i) Traffic control resources, (ii), preventative resources and
(iii) corrective resources. Traffic control resources are employed to impose
restrictions on the traffic flowing across directed edges in the transportation
network. Preventative resources are allocated to nodes to reduce the
probability of infection at that node (e.g. vaccines), and corrective resources
are allocated to nodes to increase the recovery rate at that node (e.g.
antidotes). We assume these resources have monetary costs associated with them,
from which we formalize an optimal budget allocation problem which maximizes
containment of the infection. We present a polynomial time solution to the
optimal budget allocation problem using Geometric Programming (GP) for an
arbitrary weighted and directed contact network and a large class of resource
cost functions. We illustrate our approach by designing optimal traffic control
strategies to contain an epidemic outbreak that propagates through a real-world
air transportation network.Comment: arXiv admin note: text overlap with arXiv:1309.627
Impact of origin-destination information in epidemic spreading
The networked structure of contacts shapes the spreading of epidemic processes. Recent advances on network theory have improved our understanding of the epidemic processes at large scale. The relevance of several considerations still needs to be evaluated in the study of epidemic spreading. One of them is that of accounting for the influence of origin and destination patterns in the flow of the carriers of an epidemic. Here we compute origin-destination patterns compatible with empirical data of coarse grained flows in the air transportation network. We study the incidence of epidemic processes in a metapopulation approach considering different alternatives to the flows prior knowledge. The data-driven scenario where the estimation of origin and destination flows is considered turns out to be relevant to assess the impact of the epidemics at a microscopic level (in our scenario, which populations are infected). However, this information is irrelevant to assess its macroscopic incidence (fraction of infected populations). These results are of interest to implement even better computational platforms to forecast epidemic incidence
Heterogeneous length of stay of hosts’ movements and spatial epidemic spread
Infectious diseases outbreaks are often characterized by a spatial component induced by hosts’ distribution, mobility, and interactions. Spatial models that incorporate hosts’ movements are being used to describe these processes, to investigate the conditions for propagation, and to predict the spatial spread. Several assumptions are being considered to model hosts’ movements, ranging from permanent movements to daily commuting, where the time spent at destination is either infinite or assumes a homogeneous fixed value, respectively. Prompted by empirical evidence, here we introduce a general metapopulation approach to model the disease dynamics in a spatially structured population where the mobility process is characterized by a heterogeneous length of stay. We show that large fluctuations of the length of stay, as observed in reality, can have a significant impact on the threshold conditions for the global epidemic invasion, thus altering model predictions based on simple assumptions, and displaying important public health implications
Impact of origin-destination information in epidemic spreading
The networked structure of contacts shapes the spreading of epidemic
processes. Recent advances on network theory have improved our understanding of
the epidemic processes at large scale. The relevance of several considerations
still needs to be evaluated in the study of epidemic spreading. One of them is
that of accounting for the influence of origin and destination patterns in the
flow of the carriers of an epidemic. Here we compute origin-destination
patterns compatible with empirical data of coarse grained flows in the air
transportation network. We study the incidence of epidemic processes in a
metapopulation approach considering different alternatives to the flows prior
knowledge. The data-driven scenario where the estimation of origin and
destination flows is considered turns out to be relevant to assess the impact
of the epidemics at a microscopic level (in our scenario, which populations are
infected). However, this information is irrelevant to assess its macroscopic
incidence (fraction of infected populations). These results are of interest to
implement even better computational platforms to forecast epidemic incidence.Comment: 12 pages, 5 figures. To appear in Scientific Report
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