Eamon O'Dea

Eamon O'Dea
University of Georgia | UGA · Odum School of Ecology

PhD

About

27
Publications
2,201
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236
Citations
Introduction
Eamon O'Dea currently works at the Odum School of Ecology, University of Georgia. Eamon does research in Epidemiology and Population Biology.
Additional affiliations
May 2015 - present
University of Georgia
Position
  • PostDoc Position
June 2013 - April 2015
Georgetown University
Position
  • PostDoc Position

Publications

Publications (27)
Article
Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emerge...
Article
Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this ar...
Preprint
Full-text available
Short-term forecasts of the dynamics of COVID-19 in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. A major obstacle has been capturing variations in the underlying kinetics of transmission resulting from changes in public policy, individual behaviors, an...
Article
The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of...
Article
Full-text available
Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches—rooted in dynamical systems and the theory of stochastic processes—have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that thes...
Article
Full-text available
Second-order statistics such as the variance and autocorrelation can be useful indicators of the stability of randomly perturbed systems, in some cases providing early warning of an impending, dramatic change in the system’s dynamics. One specific application area of interest is the surveillance of infectious diseases. In the context of disease (re...
Article
Full-text available
Many ecological systems are subject critical transitions, which are abrupt changes to contrasting states triggered by small changes in some key component of the system. Temporal early warning signals such as the variance of a time series, and spatial early warning signals such as the spatial correlation in a snapshot of the system’s state, have bee...
Article
Full-text available
Epidemic transitions are an important feature of infectious disease systems. As the transmissibility of a pathogen increases, the dynamics of disease spread shifts from limited stuttering chains of transmission to potentially large scale outbreaks. One proposed method to anticipate this transition are early-warning signals (EWS), summary statistics...
Data
Fig 5 repeated R0(0) = 0.5 for the test model and R0 = 0.5 for the null model. For both models, bandwidth b = 36. (TIFF)
Data
Fig 5 repeated for 3 year bandwidth. (Weekly aggregation: b = 156; monthly aggregation: b = 36). (TIFF)
Data
Example time series for the test model and null model. All parameters are the same as shown in Fig 6. (TIFF)
Data
Fig 5 repeated for b = 100. (TIFF)
Data
Fig 5 repeated with R0(0) = 0.5 for the test model and R0 = 0.5 for the null model. For both models T = 10 years and bandwidth b = 36. (TIFF)
Article
Full-text available
The epidemic threshold of the susceptible-infected-recovered model is a boundary separating parameters that permit epidemics from those that do not. This threshold corresponds to parameters where the system's equilibrium becomes unstable. Consequently, we use the average rate at which deviations from the equilibrium shrink to define a distance to t...
Preprint
Full-text available
The epidemic threshold of the susceptible-infected-recovered (SIR) model is a boundary separating parameters that can permit epidemics from those that cannot. This threshold corresponds to points where the stability of the system’s equilibrium reaches zero. Consequently, we use the average rate at which deviations from the equilibrium shrink to def...
Article
Full-text available
Background Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also...
Article
Full-text available
Emerging diseases must make a transition from stuttering chains of transmission to sustained chains of transmission, but this critical transition need not coincide with the system becoming supercritical. That is, the introduction of infection to a supercritical system results in a significant fraction of the population becoming infected only with a...
Data
Supplemental analyses: model sensitivity, frequency-dependent transmission, and KL divergence partitioning
Article
Full-text available
In 2013, U.S. swine producers were confronted with the disruptive emergence of porcine epidemic diarrhoea (PED). Movement of animals among farms is hypothesised to have played a role in the spread of PED among farms. Via this or other mechanisms, the rate of spread may also depend on the geographic density of farms and climate. To evaluate such eff...
Preprint
Full-text available
In 2013, U.S. swine producers were confronted with the disruptive emergence of porcine epidemic diarrhoea (PED). Movement of animals among farms is hypothesised to have played a role in the spread of PED among farms. Via this or other mechanisms, the rate of spread may also depend on the geographic density of farms and climate. To evaluate such eff...
Article
Emerging diseases must make a transition from stuttering chains of transmission to sustained chains of transmission, but this critical transition need not coincide with the system becoming supercritical. That is, the introduction of infection to a supercritical system results in a significant fraction of the population becoming infected only with a...
Article
Full-text available
Infectious disease often occurs in small, independent outbreaks in populations with varying characteristics. Each outbreak by itself may provide too little information for accurate estimation of epidemic model parameters. Here we show that using standard stochastic epidemic models for each outbreak and allowing parameters to vary between outbreaks...
Article
Full-text available
The inference of population dynamics from molecular sequence data is becoming an important new method for the surveillance of infectious diseases. Here, we examine how heterogeneity in contact shapes the genealogies of parasitic agents. Using extensive simulations, we find that contact heterogeneity can have a strong effect on how the structure of...
Article
Full-text available
Lethal mutagenesis is a promising new antiviral therapy that kills a virus by raising its mutation rate. One potential shortcoming of lethal mutagenesis is that viruses may resist the treatment by evolving genomes with increased robustness to mutations. Here, we investigate to what extent mutational robustness can inhibit extinction by lethal mutag...

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Projects

Project (1)
Archived project
This project seeks to evaluate the use of generic indicators, derived from dynamical systems theory, to monitor the distance and approach of an infectious disease system to thresholds that could lead to drastic changes in disease dynamics (e.g., epidemics and subsequent endemicity). This project is a specialization of more general research being done by several other research groups on whether and how one might manage and anticipate critical transitions in complex systems.