501 research outputs found
Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing
In this paper, we outline the theory of epidemic percolation networks and
their use in the analysis of stochastic SIR epidemic models on undirected
contact networks. We then show how the same theory can be used to analyze
stochastic SIR models with random and proportionate mixing. The epidemic
percolation networks for these models are purely directed because undirected
edges disappear in the limit of a large population. In a series of simulations,
we show that epidemic percolation networks accurately predict the mean outbreak
size and probability and final size of an epidemic for a variety of epidemic
models in homogeneous and heterogeneous populations. Finally, we show that
epidemic percolation networks can be used to re-derive classical results from
several different areas of infectious disease epidemiology. In an appendix, we
show that an epidemic percolation network can be defined for any
time-homogeneous stochastic SIR model in a closed population and prove that the
distribution of outbreak sizes given the infection of any given node in the SIR
model is identical to the distribution of its out-component sizes in the
corresponding probability space of epidemic percolation networks. We conclude
that the theory of percolation on semi-directed networks provides a very
general framework for the analysis of stochastic SIR models in closed
populations.Comment: 40 pages, 9 figure
Generation interval contraction and epidemic data analysis
The generation interval is the time between the infection time of an infected
person and the infection time of his or her infector. Probability density
functions for generation intervals have been an important input for epidemic
models and epidemic data analysis. In this paper, we specify a general
stochastic SIR epidemic model and prove that the mean generation interval
decreases when susceptible persons are at risk of infectious contact from
multiple sources. The intuition behind this is that when a susceptible person
has multiple potential infectors, there is a ``race'' to infect him or her in
which only the first infectious contact leads to infection. In an epidemic, the
mean generation interval contracts as the prevalence of infection increases. We
call this global competition among potential infectors. When there is rapid
transmission within clusters of contacts, generation interval contraction can
be caused by a high local prevalence of infection even when the global
prevalence is low. We call this local competition among potential infectors.
Using simulations, we illustrate both types of competition.
Finally, we show that hazards of infectious contact can be used instead of
generation intervals to estimate the time course of the effective reproductive
number in an epidemic. This approach leads naturally to partial likelihoods for
epidemic data that are very similar to those that arise in survival analysis,
opening a promising avenue of methodological research in infectious disease
epidemiology.Comment: 20 pages, 5 figures; to appear in Mathematical Bioscience
Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees
Recent work has attempted to use whole-genome sequence data from pathogens to
reconstruct the transmission trees linking infectors and infectees in
outbreaks. However, transmission trees from one outbreak do not generalize to
future outbreaks. Reconstruction of transmission trees is most useful to public
health if it leads to generalizable scientific insights about disease
transmission. In a survival analysis framework, estimation of transmission
parameters is based on sums or averages over the possible transmission trees. A
phylogeny can increase the precision of these estimates by providing partial
information about who infected whom. The leaves of the phylogeny represent
sampled pathogens, which have known hosts. The interior nodes represent common
ancestors of sampled pathogens, which have unknown hosts. Starting from
assumptions about disease biology and epidemiologic study design, we prove that
there is a one-to-one correspondence between the possible assignments of
interior node hosts and the transmission trees simultaneously consistent with
the phylogeny and the epidemiologic data on person, place, and time. We develop
algorithms to enumerate these transmission trees and show these can be used to
calculate likelihoods that incorporate both epidemiologic data and a phylogeny.
A simulation study confirms that this leads to more efficient estimates of
hazard ratios for infectiousness and baseline hazards of infectious contact,
and we use these methods to analyze data from a foot-and-mouth disease virus
outbreak in the United Kingdom in 2001. These results demonstrate the
importance of data on individuals who escape infection, which is often
overlooked. The combination of survival analysis and algorithms linking
phylogenies to transmission trees is a rigorous but flexible statistical
foundation for molecular infectious disease epidemiology.Comment: 28 pages, 11 figures, 3 table
Perspective review of what is needed for molecular-specific fluorescence-guided surgery
Molecular image-guided surgery has the potential for translating the tools of molecular pathology to real-time guidance in surgery. As a whole, there are incredibly positive indicators of growth, including the first United States Food and Drug Administration clearance of an enzyme-biosynthetic-activated probe for surgery guidance, and a growing number of companies producing agents and imaging systems. The strengths and opportunities must be continued but are hampered by important weaknesses and threats within the field. A key issue to solve is the inability of macroscopic imaging tools to resolve microscopic biological disease heterogeneity and the limitations in microscopic systems matching surgery workflow. A related issue is that parsing out true molecular-specific uptake from simple-enhanced permeability and retention is hard and requires extensive pathologic analysis or multiple in vivo tests, comparing fluorescence accumulation with standard histopathology and immunohistochemistry. A related concern in the field is the over-reliance on a finite number of chosen preclinical models, leading to early clinical translation when the probe might not be optimized for high intertumor variation or intratumor heterogeneity. The ultimate potential may require multiple probes, as are used in molecular pathology, and a combination with ultrahigh-resolution imaging and image recognition systems, which capture the data at a finer granularity than is possible by the surgeon. Alternatively, one might choose a more generalized approach by developing the tracer based on generic hallmarks of cancer to create a more "one-size-fits-all" concept, similar to metabolic aberrations as exploited in fluorodeoxyglucose-positron emission tomography (FDG-PET) (i.e., Warburg effect) or tumor acidity. Finally, methods to approach the problem of production cost minimization and regulatory approvals in a manner consistent with the potential revenue of the field will be important. In this area, some solid steps have been demonstrated in the use of fluorescent labeling commercial antibodies and separately in microdosing studies with small molecules. (C) The Authors
Second look at the spread of epidemics on networks
In an important paper, M.E.J. Newman claimed that a general network-based
stochastic Susceptible-Infectious-Removed (SIR) epidemic model is isomorphic to
a bond percolation model, where the bonds are the edges of the contact network
and the bond occupation probability is equal to the marginal probability of
transmission from an infected node to a susceptible neighbor. In this paper, we
show that this isomorphism is incorrect and define a semi-directed random
network we call the epidemic percolation network that is exactly isomorphic to
the SIR epidemic model in any finite population. In the limit of a large
population, (i) the distribution of (self-limited) outbreak sizes is identical
to the size distribution of (small) out-components, (ii) the epidemic threshold
corresponds to the phase transition where a giant strongly-connected component
appears, (iii) the probability of a large epidemic is equal to the probability
that an initial infection occurs in the giant in-component, and (iv) the
relative final size of an epidemic is equal to the proportion of the network
contained in the giant out-component. For the SIR model considered by Newman,
we show that the epidemic percolation network predicts the same mean outbreak
size below the epidemic threshold, the same epidemic threshold, and the same
final size of an epidemic as the bond percolation model. However, the bond
percolation model fails to predict the correct outbreak size distribution and
probability of an epidemic when there is a nondegenerate infectious period
distribution. We confirm our findings by comparing predictions from percolation
networks and bond percolation models to the results of simulations. In an
appendix, we show that an isomorphism to an epidemic percolation network can be
defined for any time-homogeneous stochastic SIR model.Comment: 29 pages, 5 figure
Mixed Ancestry and Admixture in Kauai\u27s Feral Chickens: Invasion of Domestic Genes into Ancient Red Junglefowl Reservoirs
A major goal of invasion genetics is to determine how establishment histories shape nonânative organisms\u27 genotypes and phenotypes. While domesticated species commonly escape cultivation to invade feral habitats, few studies have examined how this process shapes feral gene pools and traits. We collected genomic and phenotypic data from feral chickens (Gallus gallus) on the Hawaiian island of Kauai to (i) ascertain their origins and (ii) measure standing variation in feral genomes, morphology and behaviour. Mitochondrial phylogenies (Dâloop & whole Mt genome) revealed two divergent clades within our samples. The rare clade also contains sequences from Red Junglefowl (the domestic chicken\u27s progenitor) and ancient DNA sequences from Kauai that predate European contact. This lineage appears to have been dispersed into the east Pacific by ancient Polynesian colonists. The more prevalent MtDNA clade occurs worldwide and includes domesticated breeds developed recently in Europe that are farmed within Hawaii. We hypothesize this lineage originates from recently feralized livestock and found supporting evidence for increased G. gallus density on Kauai within the last few decades. SNPs obtained from wholeâgenome sequencing were consistent with historic admixture between Kauai\u27s divergent (G. gallus) lineages. Additionally, analyses of plumage, skin colour and vocalizations revealed that Kauai birds\u27 behaviours and morphologies overlap with those of domestic chickens and Red Junglefowl, suggesting hybrid origins. Together, our data support the hypotheses that (i) Kauai\u27s feral G. gallus descend from recent invasion(s) of domestic chickens into an ancient Red Junglefowl reservoir and (ii) feral chickens exhibit greater phenotypic diversity than candidate source populations. These findings complicate management objectives for Pacific feral chickens, while highlighting the potential of this and other feral systems for evolutionary studies of invasions
Predation thresholds for reintroduction of native avifauna following suppression of invasive Brown Treesnakes on Guam
The brown treesnake (BTS) (Boiga irregularis) invasion on GuĂ„han (in English, Guam) led to the extirpation of nearly all native forest birds. In recent years, methods have been developed to reduce BTS abundance on a landscape scale. To help assess the prospects for the successful reintroduction of native birds to GuĂ„han following BTS suppression, we modeled bird population persistence based on their life history characteristics and relative sensitivity to BTS predation. We constructed individual-based models and simulated BTS predation in hypothetical founding populations for each of seven candidate bird species. We represented BTS predation risk in two steps: risk of being encountered and risk of mortality if encountered. We link encounter risk from the birdâs perspective to snake contact rates at camera traps with live animal lures, the most direct practical means of estimating BTS predation risk. Our simulations support the well-documented fact that GuĂ„hanâs birds cannot persist with an uncontrolled population of BTS but do indicate that bird persistence in GuĂ„hanâs forests is possible with suppression short of total eradication. We estimate threshold BTS contact rates would need to be below 0.0002â0.0006 snake contacts per bird per night for these birds to persist on the landscape, which translates to an annual encounter probability of 0.07â0.20. We simulated the effects of snake-proof nest boxes for Sihek (Todiramphus cinnamominus) and SĂ„li (Aplonis opaca), but the benefits were small relative to the overall variation in contact rate thresholds among species. This variation among focal bird species in sustainable predation levels can be used to prioritize species for reintroduction in a BTS-suppressed landscape, but variation among these species is narrow relative to the required reduction from current BTS levels, which may be four orders of magnitude higher (\u3e0.18). Our modeling indicates that the required predation thresholds may need to be lower than have yet been demonstrated with current BTS management. Our predation threshold metric provides an important management tool to help estimate target BTS suppression levels that can be used to determine when bird reintroduction campaigns might begin and serves as a model for other systems to match predator control with reintroduction efforts
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