Effectiveness of low sensitivity interventions in west Africa Ebola epidemic

Richard Williams, a student from Morehouse College, worked with Dr. John Drake to develop a model examining the effectiveness of low-sensitivity interventions in disease outbreaks.

Abstract: We conducted a theoretical study to investigate the effect of low sensitivity interventions on the containment of an emerging pathogen. Low sensitivity interventions  such as thermal scans for febrile patients in airports are outbreak interventions that may yield many false negatives, and are implemented because of their political or logistical feasibility. The first step of our study was to derive a discrete-time SEIR model for transmission at a given site and develop computer code to implement the associated stochastic simulation algorithm. We next developed a simulation program to link multiple sites updated as in the first step, incorporating pairwise movement probabilities between sites (which may be randomly constructed or data-driven) and an intervention that prohibits movement based on low sensitivity diagnostic (thermal scans) with tunable sensitivity and specificity. For reporting, this code also tracks the number of uninfected persons incorrectly denied permission to travel. In future work, this software will be used to compare the effectiveness of low-sensitivity interventions applied to pathogens with a range of incubation periods.


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Development of deterministic and stochastic models for a T7 phage-E. coli system with vaccination strategy implementation

Abigail Smith, from Carnegie Mellon University, worked with Reni Kaul in the lab of Dr. John Drake to develop models to study the effectiveness of vaccination strategies.

Abigail L. Smith1, RajReni B. Kaul2 and John M. Drake2

1Carnegie Mellon University, 2Odum School of Ecology, University of Georgia

Vaccination is widely considered the most effective method of preventing the spread of infectious disease.  Pulse vaccination strategy, the repeated application of a vaccine over a defined population at a set time interval is gaining prominence as a strategy for the elimination of diseases such as measles, hepatitis, and smallpox. In order to study the effectiveness of this strategy, a bench experiment will be designed using E.coli bacteria and T7 bacteriophage, and studying the interactions and mechanisms in a chemostat. Using this system allows us to study the spread of infectious disease in laboratory setting. To test vaccination in system, a concentration of IPTG will be used to induce expression of the rcsA gene (immunity) in E. coli. Results can be generalized from an experimental bench system (E. coli bacteria and T7 phage) by developing a deterministic compartmental model, and then factoring in noise to form a stochastic model. Additional classes were added to track phage populations and experiment with vaccination strategy.  Preliminary studies were designed to study early warning signs for approaching a bifurcation point and critical slowing down, by examining the phage being driven to extinction.


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Photo credit: School of Life Sciences, Gibbet Hill Campus, The University of Warwick, Coventry, CV4 7AL


Effects of pH and Temperature Variability on Fungal Pathogen Development and Population Survival in Daphnia

For this project, based in the lab of Dr. John Drake, Trianna Humphrey worked with Tad Dallas to study the effect of changing environmental conditions on a host-parasite relationship.

Trianna Humphrey, Tougaloo College

Tad Dallas, Odum School of Ecology, University of Georgia

 Extreme environmental conditions can have an influence on host-parasite relationships and capable of altering interactions. Extreme conditions on Daphnia dentifera, also known as water fleas, and it’s fungal pathogen (Metschnikowia bicuspidata), can alter disease dynamics and population dynamics. This study was designed to answer the question, “Does the influence of temperature variability have an effect on the host-parasite relationships within Daphnia.”  Also we wanted to answer the question ”Does a change in pH have an effect on the host-parasite relationships or an effect on the population?” We show that we exposed half the population to the pathogen. A temperature of 20C was used as a control and 12C and 28C were the two extreme temperatures, with time variations of either 0,1,2, or 4 hours. This study is ongoing and data are still being collected. To alter the pH, we added a HCL solution to the populations and exposed half the populations to the pathogen to answer the question ”Does pH have an effect on infection and spores within Daphnia?”  After examining the data, we didn’t find any useful data to answer our big question.

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When ideas go viral: Early warning signals in theoretical and real-world social contagion systems

Lexi Lerner, a biology major from Brown University, worked with Dr. John Drake to look for critical slowing down in consumer fad systems.

Abstract: Consumer fads are often characterized by unpredictable explosive outbreaks. Early warning signals have been retroactively successful at anticipating critical phenomena of complex systems such as infectious disease epidemics, but they have yet to be extended to consumer fads. Here we study both theoretical and real-world consumer fad systems to see whether their approach to an epidemic is characterized by critical slowing down (CSD). We propose a new model of social contagion transmission that includes the accumulation of buzz, or aggregate ubiquity, around an idea. We derived deterministic and stochastic solutions for this model and showed that it can push a subcritical system to criticality. We evaluated four candidate early warning signals by their sensitivity and specificity using various rolling window bandwidths to understand CSD detection performance. We also analyzed data for a faddish product line over a two-year timespan to determine if CSD was detectable in real-world systems. Our results show that variance was the best-scoring and most consistent predictor (AUC > 0.8) of CSD in the theoretical stochastic SIBR model. It was also determined that CSD is present in the product line data and, at small bandwidths, is characterized by “breaking points” that can be traced back to events in the original time series. We hope these findings will help guide statistical analysis of consumer behavior and further early warning signal analysis of economic interest.

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Quantifying the Performance of Spatial and Temporal Early Warning Signals of Disease Elimination

Dominic Gray, a student from Norfolk State University, and Dr. John Drake from the Odum School of Ecology examined the use of temporal early warning signals in disease dynamics.

Dominic Gray, Norfolk State University

John Drake, Odum School of Ecology, University of Georgia

Early warning signals of disease emergence and elimination seek to forecast changes of state in infectious disease system. Most such signals are a result of critical slowing down and other universal patterns near bifurcations. Most work to date has focused on temporal early warning signals, which are known to be statistically inefficient and discard information contained in the spatial pattern of cases. We sought to quantify the performance of spatial indicators and compare them to temporal indicators by simulating a spatial SIR compartmental model with vaccine induced immunity over a spatially homogenous environment. We found that spatial indicators greatly outperform their temporal counterparts, suggesting that additional gains in statistical efficiency could be achieved by adopting these newer methods.

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Using the power ratio as an early warning signal to detect critical transitions for disease emergence and eradication

Paige Miller, from Gustavus Adolphus College, and Dr. John Drake in the Odum School of Ecology, examined early warning signals in disease systems.

Paige Miller, Gustavus Adolphus College

John Drake, Odum School of Ecology, University of Georgia

 Infectious diseases have ravaged the human population since the beginning of time. Eradication of human infectious diseases has been a public health initiative for more than a century. Emerging and re-emerging infectious diseases, such as Ebola, multi-drug-resistant TB, and pertussis, continually threaten lives of people across the world. Predicting when eradication of a disease is almost achieved or when emergence events are likely to occur could give policy makers specific evidence to stop the disease emergence or continue eradication efforts. Early warning signals (EWS) have been studied in many systems such as fishery collapses, economic market fluctuations, and global climate change. In the case of infectious diseases, however, EWS are difficult to use because of inherent periodicities (seasonality or multi-yearly cycling) and under-reporting issues in the datasets along with violations of normality, independence, and stationarity assumptions. We evaluate wavelet-based methods, which make fewer assumptions and allow us to specify which periodicities to study. Wavelets are a method for representing a time-series in terms of coefficients that are associated with a particular time and a particular frequency. The power ratio, the ratio of low frequency waves to high frequency waves, is calculated using wavelet-based analysis. Here, we determine if the power ratio can be used as a more reliable early warning signal for detecting infectious disease emergence and eradication.

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