Mentor: Dr. John Drake Abstract: Superspreading in disease dynamics arises when a small number of individuals contribute disproportionately to transmission. Macroparasites, including both internal parasites (e.g., worms) and ectoparasites (e.g. fleas), are widely understood to exhibit the skewed infection burdens that give rise to superspreading. Although causal drivers of different burden distributions within the same host species have often been examined, our understanding of the broader biological and macroecological conditions responsible for shaping such distributions is minimal. Following the model of a previous student who cataloged the wide range of “transmission trees” (another way of quantifying superspreading, see their work here: https://outbreaktrees.ecology.uga.edu/), this student will seek to develop the first meta-database of macroparasite burdens. This database will enable us to ask, for the first time, whether there are ecological or biological traits of hosts and parasites that govern the shape of these distributions and therefore the propensity for superspreading. The ideal student for this project is one who likes to play around with data — to collect it, categorize it, think about it, and analyze it. If you like looking for patterns in the world, this project is you. Is the project computational, empirical, or both? Computational (creating a database from existing published literature; requires working at a computer, but less coding expertise than some other computational projects).
Mentors: Dr. Kyle Dahlin and Dr. John Vinson Abstract: Climate change is expected to lead to shifts in the risk of mosquito-borne disease outbreaks throughout the world. However, it is unclear how increases in the variability of temperature and rainfall might impact these outbreaks. The goal of this project is to investigate how environmental and demographic noise affect key measures of the transmission of mosquito-borne diseases. The student will be responsible for conducting simulation experiments in R to explore the effect of noise on the probability of an outbreak and the outbreak size distribution. The student will gain experience in the ecology of vector-borne diseases, mathematical modeling, and programming in R. Is the project computational, empirical, or both? Computational.
Juliana Taube, a student at Bowdoin College, worked with Paige Miller and Dr. John Drake.
Abstract: Transmission trees contain valuable
details about who infected whom in infectious disease outbreaks. We created a
database with 81 published, standardized transmission trees consisting of 12 directly-transmitted
pathogens (mostly viruses). We also demonstrated how the database could be used
to help answer research questions in infectious disease epidemiology. First, we
analyzed overall and pathogen-specific patterns between tree parameters (R0 and
variation in secondary infections). We found that outbreak size is nonlinearly
associated with R0 and the dispersion parameter, but emphasize
that pathogen-specific patterns and intervention efforts may alter theoretical
relationships between these variables. Second, we examined how superspreader
contribution to onward transmission, either directly or through their tree
descendants, varies across pathogens. Superspreaders were responsible for most
cases via their descendants and the number of superspreaders varied across
pathogens. Additional database exploration matched theory1 about
how the proportion of superspreaders increases at intermediate levels of
dispersion, an idea that should be further explored. We hope that our database
will assist both theoretical and applied infectious disease epidemiology
research in the future.
1. Lloyd-Smith, JO, Schreiber, SJ, Kopp, PE, & Getz, WM (2005)
“Superspreading and the effect of individual variation on disease emergence.”Nature, 438(7066):
Kennedy Houck, a junior from Ursinus College, worked with Paige Miller in the lab of Dr. John Drake to study age-based interventions for Tuberculosis.
Abstract: Tuberculosis (TB) represents a widespread public health concern. The World Health Organization’s “End TB Strategy” has set the goal for global TB eradication by 2050. Previous studies have suggested that current public health intervention strategies may not achieve this goal in many parts of the world that experience high TB incidence rates. The goal of this project was to determine whether age-based interventions could enhance current interventions, which are currently implemented. A standard TB model, which includes five state variables (Susceptible, Latent, Infectious, Noninfectious, and Removed), was modified to include 16 different age classes, and parameterized with previously published information for India and South Africa. The model was run for 500 years until equilibrium was reached. Once equilibrium was reached, 18 different interventions, all simulating faster rates of testing and treating, or shorter infectious periods, among active TB cases, were tested by calculating the rate of decrease of TB cases in each population over time. A “baseline” scenario where the rate of treatment was held constant was compared to interventions where the infectious period was reduced by 10, 50, 70, and 90% independently for either a specific age class or overall (i.e. a “blanket strategy”). To test the validity of model predictions, we calculated the correlation between the stable age distribution of cases at equilibrium and WHO TB prevalence data. In general, age-targeted interventions were found to be more effective at reducing TB cases than the “blanket” strategy. In India, targeting 15-19 year olds was predicting to result in the greatest overall decline in incidence of both latent and active TB at all intervention levels. In South Africa, targeting 10-14 year olds was predicted to result in the greatest overall decline of latent TB at all intervention levels; however, targeting 10-14 year olds at lower intervention levels and a blanket strategy at higher intervention levels, were more effective at reducing infectious TB burden. These results suggest that age-based interventions may complement current public health interventions by further reducing TB burden to achieve WHO eradication goals. Future studies should utilize a more detailed model for TB dynamics to generate a more realistic prediction.
Keri-Niyia Cooper, a student from Savannah State University, worked with Drs. John Drake and Andrew Park over the summer to investigate parasite sharing in mammals.
Abstract: After a literature review was performed to compile a list of parasites that infect marine mammals, we created a database of parasite-host pairings found in the articles. We then merged this database with information gathered from the GMPD and CLC Life Cycle, after subsetting certain traits. We used the resulting database to examine three questions: (1) Is parasite generalism greater in marine environments or terrestrial environments? (2) Is parasite sharing greater when hosts are grouped by taxonomy (cetacean/ungulate v carnivore) or habitat (marine v terrestrial)? (3) Are parasites that infect hosts of both environments drawn disproportionately from some parasite taxonomic groups? It was noted that parasite generalism is greater in terrestrial environments. Hosts have a higher chance of being infected by the same parasite if they are found in the same environment. There is a higher chance of helminth species being more commonly found in a single environment.
Magdalene Walters, a student from the University of Notre Dame, worked with Dr. John Drake to study the age distribution of a measles outbreak.
Abstract: In 1997, São Paulo, Brazil experienced a measles outbreak with an unusually high average age of infection. It has since been hypothesized that this high age of infection was due to unvaccinated rural adults traveling to urban communities.1 This project tested this hypothesis through the use of descriptive statistics and nonparametric analyses of variance. Evidence was found for varying adult transmission patterns between urban and rural communities. Forty-nine counties display a multimodal distribution of age of infection, and the rest were categorized as moderately multimodal or non-multimodal. The average outbreak size was significantly different between the multimodal, moderately multimodal, and non-multimodal counties. Counties which were not multimodal, displayed a high modal age of infection. Small outbreak sizes consistently displayed patterns associated with spread of infection between adults and evidence suggests a correlation between outbreak size and proportion of children infected.
Sarah Rainey, a Biology major at Radford University, studied structural uncertainty in disease models with Dr. John Drake.
Abstract: Mathematical models are an idealization used for disease forecasting and decision making. There is a tradeoff between timeliness and detail for models produced in response to an epidemic. It is important to quantify the level of uncertainty in a model to mitigate inaccuracies that could arise from parameter uncertainty and structural uncertainty. The impact of parameter uncertainty has been heavily investigated; however structural uncertainty is scarcely addressed. The 2014 Ebola outbreak in West Africa was used as a case study to investigate structural uncertainty. Models produced early in the course of an epidemic tend to be simpler due to a lack of information with which to estimate parameters and an abbreviated development time. Complex models are more cumbersome to develop but take into account more complex transmission scenarios reported by in-country observers. The goal of this project is to determine when a simple model can capture the trajectory of an Ebola outbreak generated by a more complex transmission process. The statistical software R was used to solve three mathematical models: a branching-process model (Drake et al., 2015), a modified SEIR model (Legrand et al., 2007), and the classical SIR model (Kermack and McKendrick, 1927). During the 2014 Ebola outbreak in West Africa, the model produced by Legrand et al. was widely used because it included all of the key components to model an Ebola epidemic simplistically. The model published by Drake et al. (2015) included greater detail about contact patterns, interventions, behavior change, and other features that may be necessary to realistically model the Ebola outbreak. The complex branching-process model published by Drake et al. (2015) was used to generate an Ebola epidemic with different scenarios by varying parameter values. The two simpler models were fit to simulated data. The correlation coefficient was calculated to test the fit of the models to observe how well they were able to capture the trajectory of the outbreak. Our findings conclude that the modified SEIR model published by Legrand et al. (2007) was superior to the classical SIR model in representing the disease trajectory of multiple Ebola outbreaks simulated with unique circumstances.
Nicole Solano, a student at Agnes Scott College, worked with Dr. John Drake to model factors that could influence the risk of Chikungunya virus in the United States.
Abstract: Chikungunya virus (CHIKV) is an arbovirus endemic to Africa and South and East Asia, that is transmitted to humans by the bite of an infected mosquito, primarily Aedes aegypti or Aedes albopictus. Since its identification in Tanzania in 1952, CHIKV has spread around the globe, making itself a very prevalent infectious disease. To date (20 June 2015) there have been eleven reported cases of autochthonous transmission in the U.S. (in Florida). Since its introduction into the Americas, concerns have been raised about which areas in the United States are most vulnerable to importation of CHIKV. We examined the correlation between human West Nile Virus (WNV) cases and human Meningitis and Encephalitis cases. A strong correlation was observed (p-value < 2.2-16) informing us that Meningitis and Encephalitis is a good predictor of WNV infection. Given this, we wanted to know which socio-economic covariates were important to consider when thinking about exposure to a disease. A regression analysis helped us identify age and poverty level as the most important covariates. Presence of Aedes albopictus and relative exposure per county was mapped to depict which counties are most vulnerable for onward Chikungunya virus transmission.
Timothy Wildauer, a student from Bethany Lutheran College, worked with Dr. John Drake to test new methods of determining time of infection for Ebola patients.
Abstract: Data recorded during the 2014-2015 West Africa Ebola Outbreak indicates when patients presented themselves at a hospital for treatment. However, to know if interventions were successful, we need to know when patients became infected. The number of people who became infected on a given date is corrupted by the incubation period for us to see when patients begin showing symptoms. The current method for finding when people became infected is to shift the dates on the observed data set backwards by the average infection period. We developed two non-parametric filters to find when patients became infected with Ebola: multiple trials of randomly selecting incubation periods for patients, and a Ridge Regression. These methods were tested on simulated outbreaks of different difficulties to see which method worked best. We selected four interventions during the outbreak to see if there was an effect on the transmissibility of the disease. In Sierra Leone, the transmission rate dropped significantly after the country was shut down for three days in September 2014. After other events, the transmission changed significantly, but the change may be the natural course of the outbreak. Through testing we found that Random deconvolution achieves a high correlation with the infection curve.
Evans Lodge, a student from Calvin College, worked with Dr. John Drake to measure how human behaviors change during disease outbreaks.
Abstract: In outbreaks of emerging infectious disease, public health interventions aim at increasing the speed with which infected individuals are removed from the susceptible population, limiting opportunities for secondary infection. Isolation, hospitalization, and barrier-nursing practices are crucial for controlling disease spread in these contexts. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are all zoonotic infections that have caused significant international outbreaks in the past. Here, we use patient-level data from the 2014-2015 Liberian Ebola epidemic, 2003 Hong Kong SARS epidemic, 2014 Saudi Arabia MERS outbreaks, and 2015 South Korea MERS outbreak to quantify changing removal rates, burial practices, contact tracing, and other measures of protective behavior change. Using the removal rate, Î³, as a measure of protective behavior change allows direct comparison of health behavior development in different outbreaks and locations. Robust regression analysis and analyses of covariance are used to estimate the rate at which Î³ increases in each outbreak by epidemic week and serial interval. Measured interactions between models show that mean removal rates varied within a factor of three, falling between the 2003 Hong Kong SARS outbreak and the 2014-2015 Ebola epidemic in Liberia.
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.
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.
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.
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.
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.
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.