Tag Archives: Quantitative

Protective Population Behavior Change in Outbreaks of Emerging Infectious Disease

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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, γ, 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 γ 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.

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Behavioral determinants of parasite transmission in a monarch (Danaus plexippus) population

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Anna Schneider, a student from the University of Wisconsin-Stevens Point, worked with mentors Dr. Sonia Altizer, Dr. Richard Hall, and Ania Majewska to look at how butterfly behavior affects parasite transmission.

Abstract: Altered behavior of an infected host can have important consequences for pathogen transmission. Pathogens can cause the host to increase foraging behavior and decrease activity levels due to increased energetic demands, which can significantly change the spread of the pathogen. Monarchs can suffer from a debilitating protozoan parasite, Ophryocystis elektroscirrha (OE), which is transmitted when infected adults inadvertently shed spores on milkweed (Asclepias spp.) leaves that are subsequently consumed by the caterpillars.  While infected adults are known to experience reduced flight ability and survival, less is known about how infection influences milkweed visitation behavior and, therefore, spore deposition.  Here, we investigated whether infection status altered activity budgets of wild adult Monarchs, particularly visitation rates to milkweed for foraging or oviposition.  Behavioral observations and milkweed visitation rates of adult Monarchs, both infected and uninfected, were collected in the butterfly gardens at the Wormsloe Historic Site in Savannah, GA.  Our results concluded that sex, not infection status, showed significance in variation of behavior.  Milkweed visitation rates were higher than previously thought and these are critical for parasite persistence.  These data provide the first field estimates of parasite spore deposition rates in monarchs.  We modified an existing differential equation model of monarch-OE dynamics to include adults contaminated with OE spores through mating and milkweed visitation.  According to this model, late-season OE prevalence varied between 16.5 and 78.6%.  This is consistent with the wide range of OE prevalence recorded in US monarchs (6-20% in the Midwest, up to 100% in tropical milkweed patches in the Southeast).

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Predicting the Effectiveness of Novel Tuberculosis Regimens

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Taylor Joseph, a student from Michigan State University, worked with Dr. Andreas Handel to use computational methods to examine new tuberculosis treatment regimes.

Abstract: Tuberculosis (TB) remains one of the world’s most deadly diseases, as current treatment protocols are far outdated and often ineffective. Furthermore, current regimens are complicated and last many months, often leading to patient non-compliance and drug resistant bacteria. There is thus a need for more effective and efficient treatment strategies, yet conducting human trials on these new strategies is expensive and time consuming. As an alternative or supplement to human and animal trials, computational models may be used to predict the outcomes of new treatment strategies. In this study, we use a system of differential equations to describe within-host dynamics of TB and drug treatment, and we assess the model’s accuracy in comparison to data collected from previous clinical studies. We then use the model to predict and evaluate the outcome of new treatment regimens.

 

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Model Accuracy in Forecasting Pathogen Spread Using Climatic Data

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Annakate Schatz, a student from Mount Holyoke College, worked with Dr. Andrew Kramer to test the accuracy of models used to predict pathogen spread.

Abstract: Species distribution models (SDMs) are commonly used to predict the total possible area a species can occupy. These models, however, rely on an assumption of equilibrium with the environment. When we discover of a new disease or pathogen, it has not necessarily reached equilibrium, but one of the most urgent questions is where else it might spread. This project sought to answer where a pathogen will spread in the future with a model selection case study focused on time-sensitive predictive ability. Previous research on how well SDMs predict disease spread has worked either with a single model-fitting method or with a simulated disease (Vaclavik and Meentemeyer 2012, Patel unpublished). We extend such investigations by modeling a real disease, Batrachochytrium dendrobatidis, using multiple methods trained on time subsets of the available occurrence data. We split our data into training and testing sets, and evaluated models on each set by calculating the AUC, kappa, True Skill Statistic, and false negative rate. We found that Maximum Entropy performed best on the subsetted data, followed by boosted regression trees and randomForest methods. Predictions tended to improve with more training data, although all AUC scores dropped when the last subset was added in. From this project, we gain a better sense of how quickly we can determine the potential extent of an emerging disease.

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Effectiveness of low sensitivity interventions in west Africa Ebola epidemic

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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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Investigating Accuracy of Climate vs. Yearly Weather for Predicting the Spread of White-Nose Syndrome in the United States

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Yaw Kumi-Ansu, a Biology major from Emory University, worked with Dr. Andrew Kramer on an ongoing project to model White-Nose syndrome in bats.

Abstract: White-Nose Syndrome is an epizootic fungal disease caused by Pseudogymnoascus destructans which has caused a significant decline in Vespertilionid bat populations in the United States. It is a psychrophilic fungus and produces infectious conidia during the hibernation season of most cave-dwelling bat species. Studies have found strong ties between the rate and pattern of spread and factors such as the density of caves within an area and temperature. In our foundational paper (Maher et al. 2012), studies based on  models designed to predict the spatial spread of WNS showed that the model based on average length of winter (number of days under 10°C) and density of caves within and between counties (Gravity(caves)+Winter) provided the best fit for projections and observed spread of the disease. In this project, we wanted to know whether yearly variations in temperature (weather) was a better environmental factor than climate (average length of winter) in predicting the spread of White-Nose Syndrome in the contiguous United States. We modified code for the Gravity(caves)+Winter model to run yearly maximum and minimum temperature in place of average length of winter and we also calculated average temperature from 2006 to 2014. Our results showed that Average length of winter remained the best environmental factor in predicting the spatial spread of WNS based on NLL and AIC values which were obtained from the MLE parameter sets. Spatial spread in both the climate and yearly weather models were similar but climate models projected faster spread to counties with caves. In the future, we hope to improve upon our study of spatial spread by using yearly variations in length of winter as well as data on co-occurring species to get a better understanding of how inter-specific differences in hibernation patterns and length of hibernation could contribute to the spread of the disease.

 

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Spore persistence in the environment drives infection dynamics of a butterfly pathogen

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Mary-Kate Williams, from the University of Arkansas at Little Rock, examined parasites of Monarch butterflies with Dr. Sonia Altizer, Dr. Richard Hall and graduate student Dara Satterfield.

Mary-Kate Williams1, Sonia Altizer2, Richard Hall2, Dara Satterfield2

1University of Arkansas at Little Rock, 2Odum School of Ecology, University of Georgia

Environmentally transmitted parasites commonly infect humans and wildlife. Environmental transmission is particularly important for insect pathogens, yet the factors affecting the persistence of infectious stages in the environment are poorly understood. Monarch butterflies are commonly infected by Ophryocystis elektroschirrha (OE); recent years have seen an increase in pathogen prevalence at the same time monarch populations in eastern North America have declined. OE is transmitted both vertically (from infected females to their progeny) and environmentally (when infected adults scatter spores onto milkweed leaves that are consumed by unrelated larvae). By using a combination of a mathematical modeling and an experimental study, we examined (1) how environmental conditions affect persistence of a free-living stage pathogen and (2) how pathogen shedding rate and environmental persistence time affect pathogen prevalence and host population size during the summer breeding season. We found that increased time spent fully exposed to environmental conditions (sun, rain, heat) reduced average infection severity induced by parasites, but did not reduce the fraction of monarchs infected by spores; therefore, parasites were able to remain viable after 15 days outdoors. Consistent with the experimental results, modeling findings showed that, parasite spores must persist for at least 20 days, in combination with a high shedding rate onto host plant leaves, for predicted prevalence to match the minimum prevalence observed in prior field studies.

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Impact of patient non-compliance on tuberculosis treatment regimens

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Kylie Balotin, a student at Rice University, and Dr. Andreas Handel, in the UGA Department of Epidemiology and Biostatistics, worked together to model the effect of patient compliance on the effectiveness of tuberculosis treatments.

Kylie Balotin1 and Andreas Handel2

1 Rice University, Houston, TX, 77005, USA1

2 Department of Epidemiology and Biostatistics, College of Public Health,

University of Georgia, Athens, GA 30602, USA

Tuberculosis is a leading cause of death in the world today and infects about one third of the world’s population. WHO currently recommends a standard treatment for TB consisting of multiple drugs. Alternative drug combinations are also being investigated as possible regimens. Although the current standard treatment is fairly effective, due to factors including the long treatment time of tuberculosis, many patients do not follow the entire treatment regimen. This noncompliance could lead to the relapse of the patient and the emergence of resistance to anti-TB drugs. The objective of this study is to use a mathematical model that simulates TB drug treatment and patient non-compliance in order to investigate the effect of patient compliance with three TB treatment regimens (the standard regimen, Remox 1, and Remox 2) a percentage of the time. We show that Remox 2 is generally more forgiving towards patient non-compliance than the other two regimens.

 

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Host Breadth of Parasites in Ungulates and Carnivores

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Emili Price, a student from Winthrop University, worked with Drs. Patrick Stephens and John Gittleman in the Odum School of Ecology to look at host breath of parasites in ungulates and carnivores.

Emili Price1, Patrick R. Stephens2, John L. Gittleman2

1. Winthrop University, Rock Hill, South Carolina

2. Odum School of Ecology, University of Georgia, Athens, Georgia

Most parasites infect multiple hosts, but few studies have focused on characteristics of hosts and parasites that may cause differences in the host breadth. We investigated two facets of host breadth: variation in the number of host species different parasite species infect and the similarity of parasite communities among host species (i.e., overlap in the parasite species that infect different pairs of host species).  We first tested for the effects of parasite transmission mode and taxonomic identity on host breadth among parasites of ungulates and carnivores using a number of definitions of host breadth, and using several methods to try and correct for differences in sampling effort among parasite species. We found that viruses and sexually transmitted parasites infect significantly more hosts than other types of parasites in ungulates regardless of the estimate of host breadth considered.  We also found that viruses and vertically transmitted parasites infect significantly more hosts than other types of parasites among ungulate parasites that infect at least two hosts.  Finally, among carnivore parasites with two or more hosts, we found that parasites transmitted via feces infect significantly more hosts than other types of parasites.  We next investigated the effect of phylogenetic distance, differences in mass, and the geographic overlap among ungulate host species on parasite community similarity. All three variables showed statistically significant correlations with parasite overlap regardless of whether Jaccard’s or the corrected Jaccard’s index was used to measure parasite overlap among hosts.  However, geographic range area overlap and phylogenetic relatedness explained much more variation than differences in body mass among hosts.  Our results were almost identical when we restricted consideration to viruses, save that mass was an even weaker predictor of overlap.  Finally, we tested to see whether carnivore species that prey on ungulates are infected by more ungulate parasites than those that do not. We found that carnivore species that prey upon ungulates were infected by on average twice as many ungulate parasites than carnivores that specialize on different prey items.

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

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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

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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

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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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Modeling Chagas disease vector infection prevalence: incorporating life history characteristics and community composition

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Authors: Carolina Cabrera, Nicole L. Gottdenker

Abstract Multihost vector-borne pathogens play an important role in human and veterinary public health worldwide, and understanding factors that drive their transmission is critical to the development of vector-borne disease prevention and control. Two potentially important drivers of multihost vector-born pathogen transmission are 1) the community composition of reservoir host species that come in contact with the vector in a particular habitat, and 2) the life history characteristics of reservoir hosts. One of the most important multihost vector-borne pathogens in the Americas, infecting over 10 million people, is the protozoan parasite Trypanosoma cruzi, the cause of Chagas disease in humans. T. cruzi circulates between wild and domestic animal reservoirs and humans, and is transmitted by a triatomine vector. The objective of this study is to develop a mathematical model that attempts to incorporate biological realities of Trypanosoma cruzi transmission between reservoir hosts and a triatomine vector. Specifically, we evaluate the Chagas disease system in Panama, consisting of a wide range of mammalian reservoir hosts and the main vector Rhodnius pallescens. We link a deterministic SI model for pathogen transmission in the vector with an SI model that describes host community transmission, incorporating host community structure and host life history characteristics, as well as hosts that have been previously infected with T. cruzi, but have developed partial immunity and are less competent reservoirs. Using field and molecular blood meal data, and values from the literature, we calculate a reservoir potential index for the different habitats within this Chagas disease system and evaluate the degree to which changes in reservoir community structure and life history characteristics impact vector infection prevalence.

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